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.

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.

ProteaseGuru: A Tool for Protease Selection in Bottom-Up Proteomics.

Bottom-up proteomics is currently the dominant strategy for proteome analysis. It relies critically upon the use of a protease to digest proteins into peptides, which are then identified by liquid chromatography-mass spectrometry (LC-MS). The choice of protease(s) has a substantial impact upon the utility of the bottom-up results obtained. Protease selection determines the nature of the peptides produced, which in turn affects the ability to infer the presence and quantities of the parent proteins and post-translational modifications in the sample. We present here the software tool ProteaseGuru, which provides in silico digestions by candidate proteases, allowing evaluation of their utility for bottom-up proteomic experiments. This information is useful for both studies focused on a single or small number of proteins, and for analysis of entire complex proteomes. ProteaseGuru provides a convenient user interface, valuable peptide information, and data visualizations enabling the comparison of digestion results of different proteases. The information provided includes data tables of theoretical peptide sequences and their biophysical properties, results summaries outlining the numbers of shared and unique peptides per protease, histograms facilitating the comparison of proteome-wide proteolytic data, protein-specific summaries, and sequence coverage maps. Examples are provided of its use to inform analysis of variant-containing proteins in the human proteome, as well as for studies requiring the use of multiple proteomic databases such as a human:mouse xenograft model, and microbiome metaproteomics.

Binary Classifier for Computing Posterior Error Probabilities in MetaMorpheus.

MetaMorpheus is a free, open-source software program for the identification of peptides and proteoforms from data-dependent acquisition tandem MS experiments. There is inherent uncertainty in these assignments for several reasons, including the limited overlap between experimental and theoretical peaks, the m/z uncertainty, and noise peaks or peaks from coisolated peptides that produce false matches. False discovery rates provide only a set-wise approximation for incorrect spectrum matches. Here we implemented a binary decision tree calculation within MetaMorpheus to compute a posterior error probability, which provides a measure of uncertainty for each peptide-spectrum match. We demonstrate its utility for increasing identifications and resolving ambiguities in bottom-up, top-down, proteogenomic, and nonspecific digestion searches.

Construction of Human Proteoform Families from 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Top-Down Proteomic Data.

Identification of proteoforms, the different forms of a protein, is important to understand biological processes. A proteoform family is the set of different proteoforms from the same gene. We previously developed the software program Proteoform Suite, which constructs proteoform families and identifies proteoforms by intact-mass analysis. Here, we have applied this approach to top-down proteomic data acquired at the National High Magnetic Field Laboratory 21 tesla Fourier transform ion cyclotron resonance mass spectrometer (data available on the MassIVE platform with identifier MSV000085978). We explored the ability to construct proteoform families and identify proteoforms from the high mass accuracy data that this instrument provides for a complex cell lysate sample from the MCF-7 human breast cancer cell line. There were 2830 observed experimental proteforms, of which 932 were identified, 44 were ambiguous, and 1854 were unidentified. Of the 932 unique identified proteoforms, 766 were identified by top-down MS2 analysis at 1% false discovery rate (FDR) using TDPortal, and 166 were additional intact-mass identifications (∼4.7% calculated global FDR) made using Proteoform Suite. We recently published a proteoform level schema to represent ambiguity in proteoform identifications. We implemented this proteoform level classification in Proteoform Suite for intact-mass identifications, which enables users to determine the ambiguity levels and sources of ambiguity for each intact-mass proteoform identification.

Spritz: A Proteogenomic Database Engine.

Proteoforms are the workhorses of the cell, and subtle differences between their amino acid sequences or post-translational modifications (PTMs) can change their biological function. To most effectively identify and quantify proteoforms in genetically diverse samples by mass spectrometry (MS), it is advantageous to search the MS data against a sample-specific protein database that is tailored to the sample being analyzed, in that it contains the correct amino acid sequences and relevant PTMs for that sample. To this end, we have developed Spritz (https://smith-chem-wisc.github.io/Spritz/), an open-source software tool for generating protein databases annotated with sequence variations and PTMs. We provide a simple graphical user interface for Windows and scripts that can be run on any operating system. Spritz automatically sets up and executes approximately 20 tools, which enable the construction of a proteogenomic database from only raw RNA sequencing data. Sequence variations that are discovered in RNA sequencing data upon comparison to the Ensembl reference genome are annotated on proteins in these databases, and PTM annotations are transferred from UniProt. Modifications can also be discovered and added to the database using bottom-up mass spectrometry data and global PTM discovery in MetaMorpheus. We demonstrate that such sample-specific databases allow the identification of variant peptides, modified variant peptides, and variant proteoforms by searching bottom-up and top-down proteomic data from the Jurkat human T lymphocyte cell line and demonstrate the identification of phosphorylated variant sites with phosphoproteomic data from the U2OS human osteosarcoma cell line.

Comprehensive in vivo identification of the c-Myc mRNA protein interactome using HyPR-MS.

Proteins bind mRNA through their entire life cycle from transcription to degradation. We analyzed c-Myc mRNA protein interactors in vivo using the HyPR-MS method to capture the crosslinked mRNA by hybridization and then analyzed the bound proteins using mass spectrometry proteomics. Using HyPR-MS, 229 c-Myc mRNA-binding proteins were identified, confirming previously proposed interactors, suggesting new interactors, and providing information related to the roles and pathways known to involve c-Myc. We performed structural and functional analysis of these proteins and validated our findings with a combination of RIP-qPCR experiments, in vitro results released in past studies, publicly available RIP- and eCLIP-seq data, and results from software tools for predicting RNA-protein interactions.

Identification and Quantification of Proteoforms by Mass Spectrometry.

A proteoform is a defined form of a protein derived from a given gene with a specific amino acid sequence and localized post-translational modifications. In top-down proteomic analyses, proteoforms are identified and quantified through mass spectrometric analysis of intact proteins. Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top-down proteomic workflows. In this review, some recent advances are outlined and current challenges and future directions for the field are discussed.

Improved Protein Inference from Multiple Protease Bottom-Up Mass Spectrometry Data.

Peptides detected by tandem mass spectrometry (MS/MS) in bottom-up proteomics serve as proxies for the proteins expressed in the sample. Protein inference is a process routinely applied to these peptides to generate a plausible list of candidate protein identifications. The use of multiple proteases for parallel protein digestions expands sequence coverage, provides additional peptide identifications, and increases the probability of identifying peptides that are unique to a single protein, which are all valuable for protein inference. We have developed and implemented a multi-protease protein inference algorithm in MetaMorpheus, a bottom-up search software program, which incorporates the calculation of protease-specific q-values and preserves the association of peptide sequences and their protease of origin. This integrated multi-protease protein inference algorithm provides more accurate results than either the aggregation of results from the separate analysis of the peptide identifications produced by each protease (separate approach) in MetaMorpheus, or results that are obtained using Fido, ProteinProphet, or DTASelect2. MetaMorpheus' integrated multi-protease data analysis decreases the ambiguity of the protein group list, reduces the frequency of erroneous identifications, and increases the number of post-translational modifications identified, while combining multi-protease search and protein inference into a single software program.

Constructing Human Proteoform Families Using Intact-Mass and Top-Down Proteomics with a Multi-Protease Global Post-Translational Modification Discovery Database.

Complex human biomolecular processes are made possible by the diversity of human proteoforms. Constructing proteoform families, groups of proteoforms derived from the same gene, is one way to represent this diversity. Comprehensive, high-confidence identification of human proteoforms remains a central challenge in mass spectrometry-based proteomics. We have previously reported a strategy for proteoform identification using intact-mass measurements, and we have since improved that strategy by mass calibration based on search results, the use of a global post-translational modification discovery database, and the integration of top-down proteomics results with intact-mass analysis. In the present study, we combine these strategies for enhanced proteoform identification in total cell lysate from the Jurkat human T lymphocyte cell line. We collected, processed, and integrated three types of proteomics data (NeuCode-labeled intact-mass, label-free top-down, and multi-protease bottom-up) to maximize the number of confident proteoform identifications. The integrated analysis revealed 5950 unique experimentally observed proteoforms, which were assembled into 848 proteoform families. Twenty percent of the observed proteoforms were confidently identified at a 3.9% false discovery rate, representing 1207 unique proteoforms derived from 484 genes.

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.

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.

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.

Development of an Immunohistochemical Assay to Detect the Ataxia-Telangiectasia Mutated (ATM) Protein in Gastric Carcinoma.

Ataxia-telangiectasia mutated (ATM), a key activator of DNA damage response mechanisms, represents a potential biomarker for targeted gastric carcinoma therapies. A phase II study (Study 39; NCT01063517) designed to investigate the combination olaparib plus paclitaxel in patients with recurrent or metastatic gastric cancer did not meet its primary endpoint of progression-free survival; however, an improvement in the secondary endpoint of overall survival was recorded with a greater overall survival benefit noted in patients with ATM-negative tumors. An ATM immunohistochemical (IHC) diagnostic assay was developed to identify patients who may respond favorably to targeted therapies and deployed in the confirmatory phase III GOLD trial (NCT01924533). The VENTANA ATM (Y170) assay was developed for investigational use in formalin-fixed, paraffin-embedded gastric carcinoma samples using an anti-ATM rabbit monoclonal antibody (clone Y170) and was optimized with OptiView DAB IHC Detection Kit on a BenchMark ULTRA instrument. The assay was deployed in studies assessing sensitivity, specificity, robustness, precision, and determining optimal ATM staining cutoff to define ATM-deficiency (ATM-low). The ATM (Y170) assay met all predefined product development acceptance criteria. Multiple parameters were characterized, including repeatability, reproducibility, analytical sensitivity, specificity, robustness, and product stability. The scoring algorithm was defined; gastric carcinoma samples were considered ATM-negative or ATM-positive when <25% or ≥25%, respectively, of tumor cell nuclei expressed ATM at any IHC stain intensity and nuclei of immune and/or endothelial cells expressed ATM at a moderate stain intensity (internal positive control). Results highlight reproducibility of the assay, supporting suitability for investigational use for evaluation of gastric carcinoma samples using tumor cell staining cutoff of <25% to define ATM-deficiency. Using this ATM assay, phase III GOLD trial (NCT01924533) clinical trial did not meet its primary endpoint, only suggesting, but not demonstrating, that assessment of ATM levels by IHC could possibly be useful in assessing the degree of benefit that may be achieved by adding olaparib to paxitaxel when treating gastric carcinoma. The utility of ATM (Y170) assay as a companion diagnostic requires further clinical validation.

CHRNA7 triplication associated with cognitive impairment and neuropsychiatric phenotypes in a three-generation pedigree.

Although deletions of CHRNA7 have been associated with intellectual disability (ID), seizures and neuropsychiatric phenotypes, the pathogenicity of CHRNA7 duplications has been uncertain. We present the first report of CHRNA7 triplication. Three generations of a family affected with various neuropsychiatric phenotypes, including anxiety, bipolar disorder, developmental delay and ID, were studied with array comparative genomic hybridization (aCGH). High-resolution aCGH revealed a 650-kb triplication at chromosome 15q13.3 encompassing the CHRNA7 gene, which encodes the alpha7 subunit of the neuronal nicotinic acetylcholine receptor. A small duplication precedes the triplication at the proximal breakpoint junction, and analysis of the breakpoint indicates that the triplicated segment is in an inverted orientation with respect to the duplication. CHRNA7 triplication appears to occur by a replication-based mechanism that produces inverted triplications embedded within duplications. Co-segregation of the CHRNA7 triplication with neuropsychiatric and cognitive phenotypes provides further evidence for dosage sensitivity of CHRNA7.

Is speech alignment to talkers or tasks?

Speech alignment, or the tendency of individuals to subtly imitate each other's speaking styles, is often assessed by comparing a subject's baseline and shadowed utterances to a model's utterances, often through perceptual ratings. These types of comparisons provide information about the occurrence of a change in subject's speech, but they do not indicate that this change is toward the specific shadowed model. In three experiments, we investigated whether alignment is specific to a shadowed model. Experiment 1 involved the classic baseline-to-shadowed comparison, to confirm that subjects did, in fact, sound more like their model when they shadowed, relative to any preexisting similarities between a subject and a model. Experiment 2 tested whether subjects' utterances sounded more similar to the model whom they had shadowed or to another, unshadowed model. In Experiment 3, we examined whether subjects' utterances sounded more similar to the model whom they had shadowed or to another subject who had shadowed a different model. The results of all experiments revealed that subjects sounded more similar to the model whom they had shadowed. This suggests that shadowing-based speech alignment is not just a change, but a change in the direction of the shadowed model, specifically.

Alignment to visual speech information.

Speech alignment is the tendency for interlocutors to unconsciously imitate one another's speaking style. Alignment also occurs when a talker is asked to shadow recorded words (e.g., Shockley, Sabadini, & Fowler, 2004). In two experiments, we examined whether alignment could be induced with visual (lipread) speech and with auditory speech. In Experiment 1, we asked subjects to lipread and shadow out loud a model silently uttering words. The results indicate that shadowed utterances sounded more similar to the model's utterances than did subjects' nonshadowed read utterances. This suggests that speech alignment can be based on visual speech. In Experiment 2, we tested whether raters could perceive alignment across modalities. Raters were asked to judge the relative similarity between a model's visual (silent video) utterance and subjects' audio utterances. The subjects' shadowed utterances were again judged as more similar to the model's than were read utterances, suggesting that raters are sensitive to cross-modal similarity between aligned words.

Visual influences on alignment to voice onset time.

PURPOSE: Speech shadowing experiments were conducted to test whether alignment (inadvertent imitation) to voice onset time (VOT) can be influenced by visual speech information. METHOD: Experiment 1 examined whether alignment would occur to auditory /pa/ syllables manipulated to have 3 different VOTs. Nineteen female participants were asked to listen to 180 syllables over headphones and to say each syllable out loud quickly and clearly. In Experiment 2, visual speech tokens composed of a face articulating /pa/ syllables at 2 different rates were dubbed onto the audio /pa/ syllables of Experiment 1. Sixteen new female participants were asked to listen to and watch (over a video monitor) 180 syllables and to say each syllable out loud quickly and clearly. RESULTS: Results of Experiment 1 showed that the 3 VOTs of the audio /pa/ stimuli influenced the VOTs of the participants' produced syllables. Results of Experiment 2 revealed that both the visible syllable rate and audio VOT of the audiovisual /pa/ stimuli influenced the VOTs of the participants' produced syllables. CONCLUSION: These results show that, like auditory speech, visual speech information can induce speech alignment to a phonetically relevant property of an utterance.

Lip-read me now, hear me better later: cross-modal transfer of talker-familiarity effects.

There is evidence that for both auditory and visual speech perception, familiarity with the talker facilitates speech recognition. Explanations of these effects have concentrated on the retention of talker information specific to each of these modalities. It could be, however, that some amodal, talker-specific articulatory-style information facilitates speech perception in both modalities. If this is true, then experience with a talker in one modality should facilitate perception of speech from that talker in the other modality. In a test of this prediction, subjects were given about 1 hr of experience lipreading a talker and were then asked to recover speech in noise from either this same talker or a different talker. Results revealed that subjects who lip-read and heard speech from the same talker performed better on the speech-in-noise task than did subjects who lip-read from one talker and then heard speech from a different talker.