Enhanced Global Post-translational Modification Discovery with MetaMorpheus

JUMPptm: Integrated software for sensitive identification of post-translational modifications and its application in Alzheimer's disease study

BACKGROUND

Mass spectrometry (MS)-based proteomic analysis of posttranslational modifications (PTMs) usually requires the pre-enrichment of modified proteins or peptides. However, recent ultra-deep whole proteome profiling generates millions of spectra in a single experiment, leaving many unassigned spectra, some of which may be derived from PTM peptides.

METHODS

Here we present JUMPptm, an integrative computational pipeline, to extract PTMs from unenriched whole proteome. JUMPptm combines the advantages of JUMP, MSFragger and Comet search engines, and includes de novo tags, customized database search and peptide filtering, which iteratively analyzes each PTM by a multi-stage strategy to improve sensitivity and specificity.

RESULTS

We applied JUMPptm to the deep brain proteome of Alzheimer's disease (AD), and identified 34,954 unique peptides with phosphorylation, methylation, acetylation, ubiquitination, and others. The phosphorylated peptides were validated by enriched phosphoproteome from the same sample. TMT-based quantification revealed 482 PTM peptides dysregulated at different stages during AD progression. For example, the acetylation of numerous mitochondrial proteins is significantly decreased in AD. A total of 60 PTM sites are found in the pan-PTM map of the Tau protein.

CONCLUSION

The JUMPptm program is an effective tool for pan-PTM analysis and the resulting AD pan-PTM profile serves as a valuable resource for AD research.

Simultaneous N-Deglycosylation and Digestion of Complex Samples on S-Traps Enables Efficient Glycosite Hypothesis Generation

N-linked glycosylation is an important post-translational modification that is difficult to identify and quantify in traditional bottom-up proteomics experiments. Enzymatic deglycosylation of proteins by peptide:N-glycosidase F (PNGase F) prior to digestion and subsequent mass spectrometry analysis has been shown to improve coverage of various N-linked glycopeptides, but the inclusion of this step may add up to a day to an already lengthy sample preparation process. An efficient way to integrate deglycosylation with bottom-up proteomics would be a valuable contribution to the glycoproteomics field. Here, we demonstrate a proteomics workflow in which deglycosylation and proteolytic digestion of samples occur simultaneously using suspension trapping (S-Trap). This approach adds no time to standard digestion protocols. Applying this sample preparation strategy to a human serum sample, we demonstrate improved identification of potential N-glycosylated peptides in deglycosylated samples compared with non-deglycosylated samples, identifying 156 unique peptides that contain the N-glycosylation motif (asparagine-X-serine/threonine), the deamidation modification characteristic of PNGase F, and an increase in peptide intensity over a control sample. We expect that this rapid sample preparation strategy will assist in the identification and quantification of both known and potential glycoproteins. Data are available via ProteomeXchange with the identifier PXD037921.

Overview and considerations in bottom-up proteomics

Proteins are the key biological actors within cells, driving many biological processes integral to both healthy and diseased states. Understanding the depth of complexity represented within the proteome is crucial to our scientific understanding of cellular biology and to provide disease specific insights for clinical applications. Mass spectrometry-based proteomics is the premier method for proteome analysis, with the ability to both identify and quantify proteins. Although proteomics continues to grow as a robust field of bioanalytical chemistry, advances are still necessary to enable a more comprehensive view of the proteome. In this review, we provide a broad overview of mass spectrometry-based proteomics in general, and highlight four developing areas of bottom-up proteomics: (1) protein inference, (2) alternative proteases, (3) sample-specific databases and (4) post-translational modification discovery.

PrIntMap-R: An Online Application for Intraprotein Intensity and Peptide Visualization from Bottom-Up Proteomics

Bottom-up proteomics (BUP) produces rich data, but visualization and analysis are time-consuming and often require programming skills. Many tools analyze these data at the proteome-level, but fewer options exist for individual proteins. Sequence coverage maps are common, but do not proportion peptide intensity. Abundance-based visualization of sequence coverage facilitates detection of protein isoforms, domains, potential truncation sites, peptide "hot-spots", and localization of post-translational modifications (PTMs). Redundant stacked-sequence coverage is an important tool in designing hydrogen-deuterium exchange (HDX) experiments. Visualization tools often lack graphical and tabular-export of processed data which complicates publication of results. Quantitative peptide abundance across amino acid sequences is an essential and missing tool in proteomics toolkits. Here we created PrIntMap-R, an online application that only requires peptide files from a database search and FASTA protein sequences. PrIntMap-R produces a variety of plots for quantitative visualization of coverage; annotation of specific sequences, PTM's, and comparisons of one or many samples overlaid with calculated fold-change or several intensity metrics. We show use-cases including protein phosphorylation, identification of glycosylation, and the optimization of digestion conditions for HDX experiments. PrIntMap-R is freely available, open source, and can run online with no installation, or locally by downloading source code from GitHub.

Integrative proteogenomics using ProteomeGenerator2

Recent advances in nucleic acid sequencing now permit rapid and genome-scale analysis of genetic variation and transcription, enabling population-scale studies of human biology, disease, and diverse organisms. Likewise, advances in mass spectrometry proteomics now permit highly sensitive and accurate studies of protein expression at the whole proteome-scale. However, most proteomic studies rely on consensus databases to match spectra to peptide and proteins sequences, and thus remain limited to the analysis of canonical protein sequences. Here, we develop ProteomeGenerator2 (PG2), based on the scalable and modular ProteomeGenerator framework. PG2 integrates genome and transcriptome sequencing to incorporate protein variants containing amino acid substitutions, insertions, and deletions, as well as non-canonical reading frames, exons, and other variants caused by genomic and transcriptomic variation. We benchmarked PG2 using synthetic data and genomic, transcriptomic, and proteomic analysis of human leukemia cells. PG2 can be integrated with current and emerging sequencing technologies, assemblers, variant callers, and mass spectral analysis algorithms, and is available open-source from https://github.com/kentsisresearchgroup/ProteomeGenerator2 .

Enhanced Proteomic Data Analysis with MetaMorpheus

MetaMorpheus is a free and open-source software program dedicated to the comprehensive analysis of proteomic data. In bottom-up proteomics, protein samples are digested into peptides prior to chromatographic separation and tandem mass spectrometric analysis. The resulting fragmentation spectra are subsequently analyzed with search software programs to obtain peptide identifications and infer the presence of proteins in the samples. MetaMorpheus seeks to maximize the information gleaned from proteomic data through the use of (a) mass calibration, (b) post-translational modification discovery, (c) multiple search algorithms, which aid in the analysis of data from traditional, crosslinking, and glycoproteomic experiments, (d) isotope-based or label-free quantification, (e) multi-protease protein inference, and (f) spectral annotation and data visualization capabilities. This protocol provides detailed descriptions of how use MetaMorpheus and how to customize data analysis workflows using MetaMorpheus tasks to meet the specific needs of the user.

Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD

MetaMSD is a proteomic software that integrates multiple quantitative mass spectrometry data analysis results using statistical summary combination approaches. By utilizing this software, scientists can combine results from their pilot and main studies to maximize their biomarker discovery while effectively controlling false discovery rates. It also works for combining proteomic datasets generated by different labeling techniques and/or different types of mass spectrometry instruments. With these advantages, MetaMSD enables biological researchers to explore various proteomic datasets in public repositories to discover new biomarkers and generate interesting hypotheses for future studies. In this protocol, we provide a step-by-step procedure on how to install and perform a meta-analysis for quantitative proteomics using MetaMSD.

MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer's Biomarker Data

In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible proteome science by generating extensive data visualizations and quality reporting, provided as standardized PDF reports. Second, we performed a systematic evaluation of methods for normalization and statistical analysis on a large variety of data sets, including additional data generated in this study, which revealed key differences. Commonly used approaches for differential testing based on moderated t-statistics were consistently outperformed by more recent statistical models, all integrated in MS-DAP. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to reanalyze a recently published large-scale proteomics data set of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.

Analysis of Golgi Secretory Functions in Cancer

Cancer cells utilize secretory pathways for paracrine signaling and extracellular matrix remodeling to facilitate directional cell migration, invasion, and metastasis. The Golgi apparatus is a central secretory signaling hub that is often deregulated in cancer. Here we described technologies that utilize microscopic, biochemical, and proteomic approaches to analyze Golgi secretory functions in genetically heterogeneous cancer cell lines.

Analysis of Golgi Protein Acetylation Using In Vitro Assays and Parallel Reaction Monitoring Mass Spectrometry

Acetylation is one of the most abundant post-translational protein modifications that regulates all cellular compartments ranging from chromatin to cytoskeleton and Golgi. The dynamic acetylation of the Golgi stacking protein GRASP55 was shown to regulate Golgi reassembly after mitosis. Here we provide a detailed protocol for the analysis of Golgi acetylation including in vitro assays to detect protein acetylation and mass spectrometry analysis to identify specific acetylation sites and their relative abundance.

NrnA is a 5'-3' exonuclease that processes short RNA substrates in vivo and in vitro

Bacterial RNases process RNAs until only short oligomers (2-5 nucleotides) remain, which are then processed by one or more specialized enzymes until only nucleoside monophosphates remain. Oligoribonuclease (Orn) is an essential enzyme that acts in this capacity. However, many bacteria do not encode for Orn and instead encode for NanoRNase A (NrnA). Yet, the catalytic mechanism, cellular roles and physiologically relevant substrates have not been fully resolved for NrnA proteins. We herein utilized a common set of reaction assays to directly compare substrate preferences exhibited by NrnA-like proteins from Bacillus subtilis, Enterococcus faecalis, Streptococcus pyogenes and Mycobacterium tuberculosis. While the M. tuberculosis protein specifically cleaved cyclic di-adenosine monophosphate, the B. subtilis, E. faecalis and S. pyogenes NrnA-like proteins uniformly exhibited striking preference for short RNAs between 2-4 nucleotides in length, all of which were processed from their 5' terminus. Correspondingly, deletion of B. subtilis nrnA led to accumulation of RNAs between 2 and 4 nucleotides in length in cellular extracts. Together, these data suggest that many Firmicutes NrnA-like proteins are likely to resemble B. subtilis NrnA to act as a housekeeping enzyme for processing of RNAs between 2 and 4 nucleotides in length.

Deciphering O-glycoprotease substrate preferences with O-Pair Search

O-Glycoproteases are an emerging class of enzymes that selectively digest glycoproteins at positions decorated with specific O-linked glycans. O-Glycoprotease substrates range from any O-glycoprotein (albeit with specific O-glycan modifications) to only glycoproteins harboring specific O-glycosylated sequence motifs, such as those found in mucin domains. Their utility for multiple glycoproteomic applications is driving the search to both discover new O-glycoproteases and to understand how structural features of characterized O-glycoproteases influence their substrate specificities. One challenge of defining O-glycoprotease specificity restraints is the need to characterize O-glycopeptides with site-specific analysis of O-glycosites. Here, we demonstrate how O-Pair Search, a recently developed O-glycopeptide-centric identification platform that enables rapid searches and confident O-glycosite localization, can be used to determine substrate specificities of various O-glycoproteases de novo from LC-MS/MS data of O-glycopeptides. Using secreted protease of C1 esterase inhibitor (StcE) from enterohemorrhagic Escherichia coli and O-endoprotease OgpA from Akkermansia mucinophila, we explore numerous settings that effect O-glycopeptide identification and show how non-specific and semi-tryptic searches of O-glycopeptide data can produce candidate cleavage motifs. These putative motifs can be further used to define new protease cleavage settings that lower search times and improve O-glycopeptide identifications. We use this platform to generate a consensus motif for the recently characterized immunomodulating metalloprotease (IMPa) from Pseudomonas aeruginosa and show that IMPa is a favorable O-glycoprotease for characterizing densely O-glycosylated mucin-domain glycoproteins.

Seeing the complete picture: proteins in top-down mass spectrometry

Top-down protein mass spectrometry can provide unique insights into protein sequence and structure, including precise proteoform identification and study of protein–ligand and protein–protein interactions. In contrast with the commonly applied bottom-up approach, top-down approaches do not include digestion of the protein of interest into small peptides, but instead rely on the ionization and subsequent fragmentation of intact proteins. As such, it is fundamentally the only way to fully characterize the composition of a proteoform. Here, we provide an overview of how a top-down protein mass spectrometry experiment is performed and point out recent applications from the literature to the reader. While some parts of the top-down workflow are broadly applicable, different research questions are best addressed with specific experimental designs. The most important divide is between studies that prioritize sequence information (i.e., proteoform identification) versus structural information (e.g., conformational studies, or mapping protein–protein or protein–ligand interactions). Another important consideration is whether to work under native or denaturing solution conditions, and the overall complexity of the sample also needs to be taken into account, as it determines whether (chromatographic) separation is required prior to MS analysis. In this review, we aim to provide enough information to support both newcomers and more experienced readers in the decision process of how to answer a potential research question most efficiently and to provide an overview of the methods that exist to answer these questions.

Reversed-Phase Liquid Chromatography of Peptides for Bottom-Up Proteomics: A Tutorial

The performance of the current bottom-up liquid chromatography hyphenated with mass spectrometry (LC-MS) analyses has undoubtedly been fueled by spectacular progress in mass spectrometry. It is thus not surprising that the MS instrument attracts the most attention during LC-MS method development, whereas optimizing conditions for peptide separation using reversed-phase liquid chromatography (RPLC) remains somewhat in its shadow. Consequently, the wisdom of the fundaments of chromatography is slowly vanishing from some laboratories. However, the full potential of advanced MS instruments cannot be achieved without highly efficient RPLC. This is impossible to attain without understanding fundamental processes in the chromatographic system and the properties of peptides important for their chromatographic behavior. We wrote this tutorial intending to give practitioners an overview of critical aspects of peptide separation using RPLC to facilitate setting the LC parameters so that they can leverage the full capabilities of their MS instruments. After briefly introducing the gradient separation of peptides, we discuss their properties that affect the quality of LC-MS chromatograms the most. Next, we address the in-column and extra-column broadening. The last section is devoted to key parameters of LC-MS methods. We also extracted trends in practice from recent bottom-up proteomics studies and correlated them with the current knowledge on peptide RPLC separation.

An extracellular receptor tyrosine kinase motif orchestrating intracellular STAT activation

The ErbB4 receptor isoforms JM-a and JM-b differ within their extracellular juxtamembrane (eJM) domains. Here, ErbB4 isoforms are used as a model to address the effect of structural variation in the eJM domain of receptor tyrosine kinases (RTK) on downstream signaling. A specific JM-a-like sequence motif is discovered, and its presence or absence (in JM-b-like RTKs) in the eJM domains of several RTKs is demonstrated to dictate selective STAT activation. STAT5a activation by RTKs including the JM-a like motif is shown to involve interaction with oligosaccharides of N-glycosylated cell surface proteins such as β1 integrin, whereas STAT5b activation by JM-b is dependent on TYK2. ErbB4 JM-a- and JM-b-like RTKs are shown to associate with specific signaling complexes at different cell surface compartments using analyses of RTK interactomes and super-resolution imaging. These findings provide evidence for a conserved mechanism linking a ubiquitous extracellular motif in RTKs with selective intracellular STAT signaling.

A Hybrid Spectral Library and Protein Sequence Database Search Strategy for Bottom-Up and Top-Down Proteomic Data Analysis

Tandem mass spectrometry (MS/MS) is widely employed for the analysis of complex proteomic samples. While protein sequence database searching and spectral library searching are both well-established peptide identification methods, each has shortcomings. Protein sequence databases lack fragment peak intensity information, which can result in poor discrimination between correct and incorrect spectrum assignments. Spectral libraries usually contain fewer peptides than protein sequence databases, which limits the number of peptides that can be identified. Notably, few post-translationally modified peptides are represented in spectral libraries. This is because few search engines can both identify a broad spectrum of PTMs and create corresponding spectral libraries. Also, programs that generate spectral libraries using deep learning approaches are not yet able to accurately predict spectra for the vast majority of PTMs. Here, we address these limitations through use of a hybrid search strategy that combines protein sequence database and spectral library searches to improve identification success rates and sensitivity. This software uses Global PTM Discovery (G-PTM-D) to produce spectral libraries for a wide variety of different PTMs. These features, along with a new spectrum annotation and visualization tool, have been integrated into the freely available and open-source search engine MetaMorpheus.

Comparison of matched formalin-fixed paraffin embedded and fresh frozen meningioma tissue reveals bias in proteomic profiles

Tissue biopsies are most commonly archived in a paraffin block following tissue fixation with formaldehyde (FFPE) or as fresh frozen tissue (FFT). While both methods preserve biological samples, little is known about how they affect the quantifiable proteome. We performed a 'bottom-up' proteomic analysis (N = 20) of short and long-term archived FFPE surgical samples of human meningiomas and compared them to matched FFT specimens. FFT facilitated a similar number of proteins assigned by MetaMorpheus compared with matched FFPE specimens (5378 vs. 5338 proteins, respectively (p = 0.053), regardless of archival time. However, marked differences in the proteome composition were apparent between FFPE and FFT specimens. Twenty-three percent of FFPE-derived peptides and 8% of FFT-derived peptides contained at least one chemical modification. Methylation and formylation were most prominent in FFPE-derived peptides (36% and 17% of modified FFPE peptides, respectively) while, most of phosphorylation and iron modifications appeared in FFT-derived peptides (p < 0.001). A mean 14% (± 2.9) of peptides identified in FFPE contained at least one modified Lysine residue. Importantly, larger proteins were significantly overrepresented in FFT specimens, while FFPE specimens were enriched with smaller proteins.

A solution to the long-standing problem of actin expression and purification

Actin is the most abundant protein in the cytoplasm of eukaryotic cells and interacts with hundreds of proteins to perform essential functions, including cell motility and cytokinesis. Numerous diseases are caused by mutations in actin, but studying the biochemistry of actin mutants is difficult without a reliable method to obtain recombinant actin. Moreover, biochemical studies have typically used tissue-purified α-actin, whereas humans express six isoforms that are nearly identical but perform specialized functions and are difficult to obtain in isolation from natural sources. Here, we describe a solution to the problem of actin expression and purification. We obtain high yields of actin isoforms in human Expi293F cells. Experiments along the multistep purification protocol demonstrate the removal of endogenous actin and the functional integrity of recombinant actin isoforms. Proteomics analysis of endogenous vs. recombinant actin isoforms confirms the presence of native posttranslational modifications, including N-terminal acetylation achieved after affinity-tag removal using the actin-specific enzyme Naa80. The method described facilitates studies of actin under fully native conditions to determine differences among isoforms and the effects of disease-causing mutations that occur in all six isoforms.

Evaluation of Variant-Specific Peptides for Detection of SARS-CoV-2 Variants of Concern

The SARS-CoV-2 omicron variant presented significant challenges to the global effort to counter the pandemic. SARS-CoV-2 is predicted to remain prevalent for the foreseeable future, making the ability to identify SARS-CoV-2 variants imperative in understanding and controlling the pandemic. The predominant variant discovery method, genome sequencing, is time-consuming, insensitive, and expensive. Ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) offers an exciting alternative detection modality provided that variant-containing peptide markers are sufficiently detectable from their tandem mass spectra (MS/MS). We have synthesized model tryptic peptides of SARS-CoV-2 variants alpha, beta, gamma, delta, and omicron and evaluated their signal intensity, HCD spectra, and reverse phase retention time. Detection limits of 781, 781, 65, and 65 amol are obtained for the molecular ions of the proteotypic peptides, beta (QIAPGQTGNIADYNYK), gamma (TQLPSAYTNSFTR), delta (VGGNYNYR), and omicron (TLVKQLSSK), from neat solutions. These detection limits are on par with the detection limits of a previously reported proteotypic peptide from the SARS-CoV-2 spike protein, HTPINLVR. This study demonstrates the potential to differentiate SARS-CoV-2 variants through their proteotypic peptides with an approach that is broadly applicable across a wide range of pathogens.

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics

Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.

Combined proteomic and biochemical analyses redefine the consensus sequence requirement for epidermal growth factor-like domain hydroxylation

Epidermal growth factor-like domains (EGFDs) have important functions in cell-cell signaling. Both secreted and cell surface human EGFDs are subject to extensive modifications, including aspartate and asparagine residue C3-hydroxylations catalyzed by the 2-oxoglutarate oxygenase aspartate/asparagine-β-hydroxylase (AspH). Although genetic studies show AspH is important in human biology, studies on its physiological roles have been limited by incomplete knowledge of its substrates. Here, we redefine the consensus sequence requirements for AspH-catalyzed EGFD hydroxylation based on combined analysis of proteomic mass spectrometric data and mass spectrometry-based assays with isolated AspH and peptide substrates. We provide cellular and biochemical evidence that the preferred site of EGFD hydroxylation is embedded within a disulfide-bridged macrocycle formed of 10 amino acid residues. This definition enabled the identification of previously unassigned hydroxylation sites in three EGFDs of human fibulins as AspH substrates. A non-EGFD containing protein, lymphocyte antigen-6/plasminogen activator urokinase receptor domain containing protein 6B (LYPD6B) was shown to be a substrate for isolated AspH, but we did not observe evidence for LYPD6B hydroxylation in cells. AspH-catalyzed hydroxylation of fibulins is of particular interest given their important roles in extracellular matrix dynamics. In conclusion, these results lead to a revision of the consensus substrate requirements for AspH and expand the range of observed and potential AspH-catalyzed hydroxylation in cells, which will enable future study of the biological roles of AspH.

Assessment of label-free quantification and missing value imputation for proteomics in non-human primates

BACKGROUND

Reliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data normalization and imputation. In non-human primates (NHP), LFQ is challenging because the query databases for NHP are limited since the genomes of these species are not comprehensively annotated. This invariably results in limited discovery of proteins and associated Post Translational Modifications (PTMs) and a higher fraction of missing data points. While identification of fewer proteins and PTMs due to database limitations can negatively impact uncovering important and meaningful biological information, missing data also limits downstream analyses (e.g., multivariate analyses), decreases statistical power, biases statistical inference, and makes biological interpretation of the data more challenging. In this study we attempted to address both issues: first, we used the MetaMorphues proteomics search engine to counter the limits of NHP query databases and maximize the discovery of proteins and associated PTMs, and second, we evaluated different imputation methods for accurate data inference. We used a generic approach for missing data imputation analysis without distinguising the potential source of missing data (either non-assigned m/z or missing values across runs).

RESULTS

Using the MetaMorpheus proteomics search engine we obtained quantitative data for 1622 proteins and 10,634 peptides including 58 different PTMs (biological, metal and artifacts) across a diverse age range of NHP brain frontal cortex. However, among the 1622 proteins identified, only 293 proteins were quantified across all samples with no missing values, emphasizing the importance of implementing an accurate and statiscaly valid imputation method to fill in missing data. In our imputation analysis we demonstrate that Single Imputation methods that borrow information from correlated proteins such as Generalized Ridge Regression (GRR), Random Forest (RF), local least squares (LLS), and a Bayesian Principal Component Analysis methods (BPCA), are able to estimate missing protein abundance values with great accuracy.

CONCLUSIONS

Overall, this study offers a detailed comparative analysis of LFQ data generated in NHP and proposes strategies for improved LFQ in NHP proteomics data.

Proteomic repository data submission, dissemination, and reuse: key messages

INTRODUCTION

The creation of ProteomeXchange data workflows in 2012 transformed the field of proteomics, consisting of the standardization of data submission and dissemination and enabling the widespread reanalysis of public MS proteomics data worldwide. ProteomeXchange has triggered a growing trend toward public dissemination of proteomics data, facilitating the assessment, reuse, comparative analyses, and extraction of new findings from public datasets. By 2022, the consortium is integrated by PRIDE, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public.

AREAS COVERED

Here, we review and discuss the current ecosystem of resources, guidelines, and file formats for proteomics data dissemination and reanalysis. Special attention is drawn to new exciting quantitative and post-translational modification-oriented resources. The challenges and future directions on data depositions including the lack of metadata and cloud-based and high-performance software solutions for fast and reproducible reanalysis of the available data are discussed.

EXPERT OPINION

The success of ProteomeXchange and the amount of proteomics data available in the public domain have triggered the creation and/or growth of other protein knowledgebase resources. Data reuse is a leading, active, and evolving field; supporting the creation of new formats, tools, and workflows to rediscover and reshape the public proteomics data.

Revealing the human mucinome

Mucin domains are densely O-glycosylated modular protein domains found in various extracellular and transmembrane proteins. Mucin-domain glycoproteins play important roles in many human diseases, such as cancer and cystic fibrosis, but the scope of the mucinome remains poorly defined. Recently, we characterized a bacterial O-glycoprotease, StcE, and demonstrated that an inactive point mutant retains binding selectivity for mucin-domain glycoproteins. In this work, we leverage inactive StcE to selectively enrich and identify mucin-domain glycoproteins from complex samples like cell lysate and crude ovarian cancer patient ascites fluid. Our enrichment strategy is further aided by an algorithm to assign confidence to mucin-domain glycoprotein identifications. This mucinomics platform facilitates detection of hundreds of glycopeptides from mucin domains and highly overlapping populations of mucin-domain glycoproteins from ovarian cancer patients. Ultimately, we demonstrate our mucinomics approach can reveal key molecular signatures of cancer from in vitro and ex vivo sources.

Mucin-domain glycoproteins are densely O-glycosylated proteins with unique secondary structure that imparts a large influence on cellular environments. Here, the authors develop a technique to selectively enrich and characterize mucin-domain glycoproteins from cell lysate and patient biofluids.

Aedes aegypti Aag-2 Cell Proteome Modulation in Response to Chikungunya Virus Infection

Chikungunya virus (CHIKV) is a single-stranded positive RNA virus that belongs to the genus Alphavirus and is transmitted to humans by infected Aedes aegypti and Aedes albopictus bites. In humans, CHIKV usually causes painful symptoms during acute and chronic stages of infection. Conversely, virus–vector interaction does not disturb the mosquito’s fitness, allowing a persistent infection. Herein, we studied CHIKV infection of Ae. aegypti Aag-2 cells (multiplicity of infection (MOI) of 0.1) for 48 h through label-free quantitative proteomic analysis and transmission electron microscopy (TEM). TEM images showed a high load of intracellular viral cargo at 48 h postinfection (hpi), as well as an unusual elongated mitochondria morphology that might indicate a mitochondrial imbalance. Proteome analysis revealed 196 regulated protein groups upon infection, which are related to protein synthesis, energy metabolism, signaling pathways, and apoptosis. These Aag-2 proteins regulated during CHIKV infection might have roles in antiviral and/or proviral mechanisms and the balance between viral propagation and the survival of host cells, possibly leading to the persistent infection.

Methods for discovering catalytic activities for pseudokinases

Pseudoenzymes resemble active enzymes, but lack key catalytic residues believed to be required for activity. Many pseudoenzymes appear to be inactive in conventional enzyme assays. However, an alternative explanation for their apparent lack of activity is that pseudoenzymes are being assayed for the wrong reaction. We have discovered several new protein kinase-like families which have revealed how different binding orientations of adenosine triphosphate (ATP) and active site residue migration can generate a novel reaction from a common kinase scaffold. These results have exposed the catalytic versatility of the protein kinase fold and suggest that atypical kinases and pseudokinases should be analyzed for alternative transferase activities. In this chapter, we discuss a general approach for bioinformatically identifying divergent or atypical members of an enzyme superfamily, then present an experimental approach to characterize their catalytic activity.

Open Search of Peptide Glycation Products from Tandem Mass Spectra

Identification of chemically modified peptides in mass spectrometry (MS)-based glycation studies is a crucial yet challenging task. There is a need to establish a mode for matching tandem mass spectrometry (MS/MS) data, allowing for both known and unknown peptide glycation modifications. We present an open search approach that uses classic and modified peptide fragment ions. The latter are shifted by the mass delta of the modification. Both provide key structural information that can be used to assess the peptide core structure of the glycation product. We also leverage redundant neutral losses from the modification side chain, introducing a third ion class for matching referred to as characteristic fragment ions. We demonstrate that peptide glycation product MS/MS spectra contain multidimensional information and that most often, more than half of the spectral information is ignored if no attempt is made to use a multi-step matching algorithm. Compared to regular and/or modified peptide ion matching, our triple-ion strategy significantly increased the median interpretable fraction of the glycation product MS/MS spectra. For reference, we apply our approach for Amadori product characterization and identify all established diagnostic ions automatically. We further show how this method effectively applies the open search concept and allows for optimized elucidation of unknown structures by presenting two hitherto undescribed peptide glycation modifications with a delta mass of 102.0311 and 268.1768 Da. We characterize their fragmentation signature by integration with isotopically labeled glycation products, which provides high validity for non-targeted structure identification.

Glyco-Decipher enables glycan database-independent peptide matching and in-depth characterization of site-specific N-glycosylation

Glycopeptides with unusual glycans or poor peptide backbone fragmentation in tandem mass spectrometry are unaccounted for in typical site-specific glycoproteomics analysis and thus remain unidentified. Here, we develop a glycoproteomics tool, Glyco-Decipher, to address these issues. Glyco-Decipher conducts glycan database-independent peptide matching and exploits the fragmentation pattern of shared peptide backbones in glycopeptides to improve the spectrum interpretation. We benchmark Glyco-Decipher on several large-scale datasets, demonstrating that it identifies more peptide-spectrum matches than Byonic, MSFragger-Glyco, StrucGP and pGlyco 3.0, with a 33.5%-178.5% increase in the number of identified glycopeptide spectra. The database-independent and unbiased profiling of attached glycans enables the discovery of 164 modified glycans in mouse tissues, including glycans with chemical or biological modifications. By enabling in-depth characterization of site-specific protein glycosylation, Glyco-Decipher is a promising tool for advancing glycoproteomics analysis in biological research.

ProSight annotator: Complete control and customization of protein entries in UniProt xml files

The effectiveness of any proteomics database search depends on the theoretical candidate information contained in the protein database. Unfortunately, candidate entries from protein databases such as UniProt rarely contain all the post-translational modifications (PTMs), disulfide bonds, or endogenous cleavages of interest to researchers. These ommissions can limit discovery of novel and biologically important proteoforms. Conversely, searching for a specific proteoform becomes a computationally difficult task for heavily modified proteins. Both situations require updates to the database through user-annotated entries. Unfortunately, manually creating properly formatted UniProt XML files is tedious and prone to errors. ProSight Annotator solves these issues by providing a graphical interface for adding user-defined features to UniProt-formatted XML files for better informed proteoform searches. It can be downloaded from http://prosightannotator.northwestern.edu. This article is protected by copyright. All rights reserved.

Enhancing Cysteine Chemoproteomic Coverage through Systematic Assessment of Click Chemistry Product Fragmentation

Mass spectrometry-based chemoproteomics has enabled functional analysis and small molecule screening at thousands of cysteine residues in parallel. Widely adopted chemoproteomic sample preparation workflows rely on the use of pan cysteine-reactive probes such as iodoacetamide alkyne combined with biotinylation via copper-catalyzed azide-alkyne cycloaddition (CuAAC) or "click chemistry" for cysteine capture. Despite considerable advances in both sample preparation and analytical platforms, current techniques only sample a small fraction of all cysteines encoded in the human proteome. Extending the recently introduced labile mode of the MSFragger search engine, here we report an in-depth analysis of cysteine biotinylation via click chemistry (CBCC) reagent gas-phase fragmentation during MS/MS analysis. We find that CBCC conjugates produce both known and novel diagnostic fragments and peptide remainder ions. Among these species, we identified a candidate signature ion for CBCC peptides, the cyclic oxonium-biotin fragment ion that is generated upon fragmentation of the N(triazole)-C(alkyl) bond. Guided by our empirical comparison of fragmentation patterns of six CBCC reagent combinations, we achieved enhanced coverage of cysteine-labeled peptides. Implementation of labile searches afforded unique PSMs and provides a roadmap for the utility of such searches in enhancing chemoproteomic peptide coverage.

Enhanced protein isoform characterization through long-read proteogenomics

BACKGROUND

The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms.

RESULTS

We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.

CONCLUSIONS

Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research.

Characterization of Metronidazole-Resistant Giardia intestinalis Lines by Comparative Transcriptomics and Proteomics

Metronidazole (MTZ) is a clinically important antimicrobial agent that is active against both bacterial and protozoan organisms. MTZ has been used extensively for more than 60 years and until now resistance has been rare. However, a recent and dramatic increase in the number of MTZ resistant bacteria and protozoa is of great concern since there are few alternative drugs with a similarly broad activity spectrum. To identify key factors and mechanisms underlying MTZ resistance, we utilized the protozoan parasite Giardia intestinalis, which is commonly treated with MTZ. We characterized two in vitro selected, metronidazole resistant parasite lines, as well as one revertant, by analyzing fitness aspects associated with increased drug resistance and transcriptomes and proteomes. We also conducted a meta-analysis using already existing data from additional resistant G. intestinalis isolates. The combined data suggest that in vitro generated MTZ resistance has a substantial fitness cost to the parasite, which may partly explain why resistance is not widespread despite decades of heavy use. Mechanistically, MTZ resistance in Giardia is multifactorial and associated with complex changes, yet a core set of pathways involving oxidoreductases, oxidative stress responses and DNA repair proteins, is central to MTZ resistance in both bacteria and protozoa.

Discovery of Dehydroamino Acid Residues in the Capsid and Matrix Structural Proteins of HIV-1

Human immunodeficiency virus type 1 (HIV-1) remains a deadly infectious disease despite existing antiretroviral therapies. A comprehensive understanding of the specific mechanisms of viral infectivity remains elusive and currently limits the development of new and effective therapies. Through in-depth proteomic analysis of HIV-1 virions, we discovered the novel post-translational modification of highly conserved residues within the viral matrix and capsid proteins to the dehydroamino acids, dehydroalanine and dehydrobutyrine. We further confirmed their presence by labeling the reactive alkene, characteristic of dehydroamino acids, with glutathione via Michael addition. Dehydroamino acids are rare, understudied, and have been observed mainly in select bacterial and fungal species. Until now, they have not been observed in HIV proteins. We hypothesize that these residues are important in viral particle maturation and could provide valuable insight into HIV infectivity mechanisms.

Discovery and Visualization of Uncharacterized Drug-Protein Adducts Using Mass Spectrometry

Drugs are often metabolized to reactive intermediates that form protein adducts. Adducts can inhibit protein activity, elicit immune responses, and cause life-threatening adverse drug reactions. The masses of reactive metabolites are frequently unknown, rendering traditional mass spectrometry-based proteomics approaches incapable of adduct identification. Here, we present Magnum, an open-mass search algorithm optimized for adduct identification, and Limelight, a web-based data processing package for analysis and visualization of data from all existing algorithms. Limelight incorporates tools for sample comparisons and xenobiotic-adduct discovery. We validate our tools with three drug/protein combinations and apply our label-free workflow to identify novel xenobiotic-protein adducts in CYP3A4. Our new methods and software enable accurate identification of xenobiotic-protein adducts with no prior knowledge of adduct masses or protein targets. Magnum outperforms existing label-free tools in xenobiotic-protein adduct discovery, while Limelight fulfills a major need in the rapidly developing field of open-mass searching, which until now lacked comprehensive data visualization tools.

A Practical Guide to Small Protein Discovery and Characterization Using Mass Spectrometry

Small proteins of up to ∼50 amino acids are an abundant class of biomolecules across all domains of life. Yet due to the challenges inherent in their size, they are often missed in genome annotations, and are difficult to identify and characterize using standard experimental approaches. Consequently, we still know few small proteins even in well-studied prokaryotic model organisms. Mass spectrometry (MS) has great potential for the discovery, validation, and functional characterization of small proteins. However, standard MS approaches are poorly suited to the identification of both known and novel small proteins due to limitations at each step of a typical proteomics workflow, i.e., sample preparation, protease digestion, liquid chromatography, MS data acquisition, and data analysis. Here, we outline the major MS-based workflows and bioinformatic pipelines used for small protein discovery and validation. Special emphasis is placed on highlighting the adjustments required to improve detection and data quality for small proteins. We discuss both the unbiased detection of small proteins and the targeted analysis of small proteins of interest. Finally, we provide guidelines to prioritize novel small proteins, and an outlook on methods with particular potential to further improve comprehensive discovery and characterization of small proteins.

Identifying Protein Interactomes of Target RNAs Using HyPR-MS

RNA-protein interactions are integral to maintaining proper cellular function and homeostasis, and the disruption of key RNA-protein interactions is central to many disease states. HyPR-MS (hybridization purification of RNA-protein complexes followed by mass spectrometry) is a highly versatile and efficient technology which enables multiplexed discovery of specific RNA-protein interactomes. This chapter provides extensive guidance for successful application of HyPR-MS to the system and target RNA(s) of interest, as well as a detailed description of the fundamental HyPR-MS procedure, including: (1) experimental design of controls, capture oligonucleotides, and qPCR assays; (2) formaldehyde cross-linking of cell culture; (3) cell lysis and RNA solubilization; (4) isolation of target RNA(s); (5) RNA purification and RT-qPCR analysis; (6) protein preparation and mass spectrometric analysis; and (7) mass spectrometric data analysis.

Proteoform Analysis and Construction of Proteoform Families in Proteoform Suite

Proteoform Suite is an interactive software program for the identification and quantification of intact proteoforms from mass spectrometry data. Proteoform Suite identifies proteoforms observed by intact-mass (MS1) analysis. In intact-mass analysis, unfragmented experimental proteoforms are compared to a database of known proteoform sequences and to one another, searching for mass differences corresponding to well-known post-translational modifications or amino acids. Intact-mass analysis enables proteoforms observed in the MS1 data without MS/MS (MS2) fragmentation to be identified. Proteoform Suite further facilitates the construction and visualization of proteoform families, which are the sets of proteoforms derived from individual genes. Bottom-up peptide identifications and top-down (MS2) proteoform identifications can be integrated into the Proteoform Suite analysis to increase the sensitivity and accuracy of the analysis. Proteoform Suite is open source and freely available at https://github.com/smith-chem-wisc/proteoform-suite .

Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Post-translational modifications (PTMs) regulate complex biological processes through the modulation of protein activity, stability, and localization. Insights into the specific modification type and localization within a protein sequence can help ascertain functional significance. Computational models are increasingly demonstrated to offer a low-cost, high-throughput method for comprehensive PTM predictions. Algorithms are optimized using existing experimental PTM data, thus accurate prediction performance relies on the creation of robust datasets. Herein, advancements in mass spectrometry-based proteomics technologies to maximize PTM coverage are reviewed. Further, requisite experimental validation approaches for PTM predictions are explored to ensure that follow-up mechanistic studies are focused on accurate modification sites.

Characterization of protein isoform diversity in human umbilical vein endothelial cells via long-read proteogenomics

Endothelial cells (ECs) comprise the lumenal lining of all blood vessels and are critical for the functioning of the cardiovascular system. Their phenotypes can be modulated by alternative splicing of RNA to produce distinct protein isoforms. To characterize the RNA and protein isoform landscape within ECs, we applied a long read proteogenomics approach to analyse human umbilical vein endothelial cells (HUVECs). Transcripts delineated from PacBio sequencing serve as the basis for a sample-specific protein database used for downstream mass-spectrometry (MS) analysis to infer protein isoform expression. We detected 53,863 transcript isoforms from 10,426 genes, with 22,195 of those transcripts being novel. Furthermore, the predominant isoform in HUVECs does not correspond with the accepted "reference isoform" 25% of the time, with vascular pathway-related genes among this group. We found 2,597 protein isoforms supported through unique peptides, with an additional 2,280 isoforms nominated upon incorporation of long-read transcript evidence. We characterized a novel alternative acceptor for endothelial-related gene CDH5, suggesting potential changes in its associated signalling pathways. Finally, we identified novel protein isoforms arising from a diversity of RNA splicing mechanisms supported by uniquely mapped novel peptides. Our results represent a high-resolution atlas of known and novel isoforms of potential relevance to endothelial phenotypes and function.[Figure: see text].

Deep Time Paleoproteomics: Looking Forward

The goal of paleoproteomics is to characterize proteins from specimens that have been subjected to the degrading and obscuring effects of time, thus obtaining biological information about tissues or organisms both unobservable in the present and unobtainable through morphological study. Although the description of sequences from Tyrannosaurus rex and Brachylophosaurus canadensis suggested that proteins may persist over tens of millions of years, the majority of paleoproteomic analyses have focused on historical, archeological, or relatively young paleontological samples that rarely exceed 1 million years in age. However, recent advances in methodology and analyses of diverse tissues types (e.g., fossil eggshell, dental enamel) have begun closing the large window of time that remains unexplored in the fossil history of the Cenozoic. In this perspective, we discuss the history and current state of deep time paleoproteomics (DTPp), here defined as paleoproteomic study of samples ∼1 million years (1 Ma) or more in age. We then discuss the future of DTPp research, including what we see as critical ways the field can expand, advancements in technology that can be utilized, and the types of questions DTPp can address if such a future is realized.

Features of Peptide Fragmentation Spectra in Single-Cell Proteomics

The goal of proteomics is to identify and quantify the complete set of proteins in a biological sample. Single-cell proteomics specializes in the identification and quantitation of proteins for individual cells, often used to elucidate cellular heterogeneity. The significant reduction in ions introduced into the mass spectrometer for single-cell samples could impact the features of MS2 fragmentation spectra. As all peptide identification software tools have been developed on spectra from bulk samples and the associated ion-rich spectra, the potential for spectral features to change is of great interest. We characterize the differences between single-cell spectra and bulk spectra by examining three fundamental spectral features that are likely to affect peptide identification performance. All features show significant changes in single-cell spectra, including the loss of annotated fragment ions, blurring signal and background peaks due to diminishing ion intensity, and distinct fragmentation pattern, compared to bulk spectra. As each of these features is a foundational part of peptide identification algorithms, it is critical to adjust algorithms to compensate for these losses.

Internal Fragment Ions Disambiguate and Increase Identifications in Top-Down Proteomics

A large fraction of observed fragment ion intensity remains unidentified in top-down proteomics. The elucidation of these unknown fragment ions could enable researchers to identify additional proteoforms and reduce proteoform ambiguity in their analyses. Internal fragment ions have received considerable attention as a major source of these unidentified fragment ions. Internal fragments are product ions that contain neither protein terminus, in contrast with terminal ions that contain a single terminus. There are many more possible internal fragments than terminal fragments, and the resulting computational complexity has historically limited the application of internal fragment ions to low-complexity samples containing only one or a few proteins of interest. We implemented internal fragment ion functionality in MetaMorpheus to allow the proteome-wide annotation of internal fragment ions. MetaMorpheus first uses terminal fragment ions to identify putative proteoforms and then employs internal fragment ions to disambiguate similar proteoforms. In the analysis of mammalian cell lysates, we found that MetaMorpheus could disambiguate over half of its previously ambiguous proteoforms while also providing up to a 7% increase in proteoform-spectrum matches identified at a 1% false discovery rate.

PeptideShaker Online: A User-Friendly Web-Based Framework for the Identification of Mass Spectrometry-Based Proteomics Data

Mass spectrometry-based proteomics is a high-throughput technology generating ever-larger amounts of data per project. However, storing, processing, and interpreting these data can be a challenge. A key element in simplifying this process is the development of interactive frameworks focusing on visualization that can greatly simplify both the interpretation of data and the generation of new knowledge. Here we present PeptideShaker Online, a user-friendly web-based framework for the identification of mass spectrometry-based proteomics data, from raw file conversion to interactive visualization of the resulting data. Storage and processing of the data are performed via the versatile Galaxy platform (through SearchGUI, PeptideShaker, and moFF), while the interaction with the results happens via a locally installed web server, thus enabling researchers to process and interpret their own data without requiring advanced bioinformatics skills or direct access to compute-intensive infrastructures. The source code, additional documentation, and a fully functional demo is available at https://github.com/barsnes-group/peptide-shaker-online.

An atlas of protein turnover rates in mouse tissues

Protein turnover is critical to cellular physiology as well as to the growth and maintenance of tissues. The unique synthesis and degradation rates of each protein help to define tissue phenotype, and knowledge of tissue- and protein-specific half-lives is directly relevant to protein-related drug development as well as the administration of medical therapies. Using stable isotope labeling and mass spectrometry, we determine the in vivo turnover rates of thousands of proteins-including those of the extracellular matrix-in a set of biologically important mouse tissues. We additionally develop a data visualization platform, named ApplE Turnover, that enables facile searching for any protein of interest in a tissue of interest and then displays its half-life, confidence interval, and supporting measurements. This extensive dataset and the corresponding visualization software provide a reference to guide future studies of mammalian protein turnover in response to physiologic perturbation, disease, or therapeutic intervention.

SPEAR: A proteomics approach for simultaneous protein expression and redox analysis

Oxidation and reduction of protein cysteinyl thiols serve as molecular switches, which is considered the most central mechanism for redox regulation of biological processes, altering protein structure, biochemical activity, subcellular localization, and binding affinity. Redox proteomics allows global identification of redox-modified cysteine (Cys) sites and quantification of their reversible oxidation/reduction responses, serving as a hypothesis-generating platform to stimulate redox biology mechanistic research. Here, we developed Simultaneous Protein Expression and Redox (SPEAR) analysis, a new redox-proteomics approach based on differential labeling of reversibly oxidized and reduced cysteines with light and heavy isotopic forms of commercially available isotopically-labeled N-ethylmaleimide (NEM). The presented method does not require enrichment for labeled peptides, thus enabling simultaneous quantification of Cys reversible oxidation state and protein abundance. Using SPEAR, we were able to quantify the in-vivo reversible oxidation state of thousands of cysteines across the Arabidopsis proteome under steady-state and oxidative stress conditions. Functional assignment of the identified redox-sensitive proteins demonstrated the widespread effect of oxidative conditions on various cellular functions and highlighted the enrichment of chloroplastic proteins. SPEAR provides a simple, straightforward, and cost-effective means of studying redox proteome dynamics. The presented data provide a global quantitative view of the reversible oxidation of well-known redox-regulated active sites and many novel redox-sensitive sites whose role in plant acclimation to stress conditions remains to be further explored.

Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing

Alternative splicing is a post-transcriptional regulatory mechanism producing distinct mRNA molecules from a single pre-mRNA with a prominent role in the development and function of the central nervous system. We used long-read isoform sequencing to generate full-length transcript sequences in the human and mouse cortex. We identify novel transcripts not present in existing genome annotations, including transcripts mapping to putative novel (unannotated) genes and fusion transcripts incorporating exons from multiple genes. Global patterns of transcript diversity are similar between human and mouse cortex, although certain genes are characterized by striking differences between species. We also identify developmental changes in alternative splicing, with differential transcript usage between human fetal and adult cortex. Our data confirm the importance of alternative splicing in the cortex, dramatically increasing transcriptional diversity and representing an important mechanism underpinning gene regulation in the brain. We provide transcript-level data for human and mouse cortex as a resource to the scientific community.

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There is widespread transcript diversity in the cortex and many novel transcripts

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Some genes display big differences in isoform number between human and mouse cortex

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There is evidence of differential transcript usage between human fetal and adult cortex

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There are many novel isoforms of genes associated with human brain disease

Strategies for mass spectrometry-based phosphoproteomics using isobaric tagging

INTRODUCTION

Protein phosphorylation is a primary mechanism of signal transduction in cellular systems. Isobaric tagging can be used to investigate alterations in phosphorylation events in sample multiplexing experiments where quantification extends across all conditions. As such, innovations in tandem mass tag methods can facilitate the expansion of the depth and breadth of phosphoproteomic analyses.

AREAS COVERED

This review discusses the current state of tandem mass tag-centric phosphoproteomics and highlights advances in reagent chemistry, instrumentation, data acquisition, and data analysis. We stress that approaches for phosphoproteomic investigations require high-specificity enrichment, sensitive detection, and accurate phosphorylation site localization.

EXPERT OPINION

Tandem mass tag-centric phosphoproteomics will continue to be an important conduit for our understanding of signal transduction in living organisms. We anticipate that progress in phosphopeptide enrichment methodologies, enhancements in instrumentation and data acquisition technologies, and further refinements in analytical strategies will be key to the discovery of biologically relevant findings from phosphoproteomics studies.

Novel human cell expression method reveals the role and prevalence of posttranslational modification in nonmuscle tropomyosins

Biochemical studies require large quantities of proteins, which are typically obtained using bacterial overexpression. However, the folding machinery in bacteria is inadequate for expressing many mammalian proteins, which additionally undergo posttranslational modifications (PTMs) that bacteria, yeast, or insect cells cannot perform. Many proteins also require native N- and C-termini and cannot tolerate extra tag amino acids for proper function. Tropomyosin (Tpm), a coiled coil protein that decorates most actin filaments in cells, requires both native N- and C-termini and PTMs, specifically N-terminal acetylation (Nt-acetylation), to polymerize along actin filaments. Here, we describe a new method that combines native protein expression in human cells with an intein-based purification tag that can be precisely removed after purification. Using this method, we expressed several nonmuscle Tpm isoforms (Tpm1.6, Tpm1.7, Tpm2.1, Tpm3.1, Tpm3.2, and Tpm4.2) and the muscle isoform Tpm1.1. Proteomics analysis revealed that human-cell-expressed Tpms present various PTMs, including Nt-acetylation, Ser/Thr phosphorylation, Tyr phosphorylation, and Lys acetylation. Depending on the Tpm isoform (humans express up to 40 Tpm isoforms), Nt-acetylation occurs on either the initiator methionine or on the second residue after removal of the initiator methionine. Human-cell-expressed Tpms bind F-actin differently than their Escherichia coli-expressed counterparts, with or without N-terminal extensions intended to mimic Nt-acetylation, and they can form heterodimers in cells and in vitro. The expression method described here reveals previously unknown features of nonmuscle Tpms and can be used in future structural and biochemical studies with Tpms and other proteins, as shown here for α-synuclein.