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

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

Massive Proteogenomic Reanalysis of Publicly Available Proteomic Datasets of Human Tissues in Search for Protein Recoding via Adenosine-to-Inosine RNA Editing.

The proteogenomic search pipeline developed in this work has been applied for reanalysis of 40 publicly available shotgun proteomic datasets from various human tissues comprising more than 8000 individual LC-MS/MS runs, of which 5442 .raw data files were processed in total. This reanalysis was focused on searching for ADAR-mediated RNA editing events, their clustering across samples of different origins, and classification. In total, 33 recoded protein sites were identified in 21 datasets. Of those, 18 sites were detected in at least two datasets, representing the core human protein editome. In agreement with prior artworks, neural and cancer tissues were found to be enriched with recoded proteins. Quantitative analysis indicated that recoding the rate of specific sites did not directly depend on the levels of ADAR enzymes or targeted proteins themselves, rather it was governed by differential and yet undescribed regulation of interaction of enzymes with mRNA. Nine recoding sites conservative between humans and rodents were validated by targeted proteomics using stable isotope standards in the murine brain cortex and cerebellum, and an additional one was validated in human cerebrospinal fluid. In addition to previous data of the same type from cancer proteomes, we provide a comprehensive catalog of recoding events caused by ADAR RNA editing in the human proteome.

Identification of Alternative Splicing in Proteomes of Human Melanoma Cell Lines without RNA Sequencing Data.

Alternative splicing is one of the main regulation pathways in living cells beyond simple changes in the level of protein expression. Most of the approaches proposed in proteomics for the identification of specific splicing isoforms require a preliminary deep transcriptomic analysis of the sample under study, which is not always available, especially in the case of the re-analysis of previously acquired data. Herein, we developed new algorithms for the identification and validation of protein splice isoforms in proteomic data in the absence of RNA sequencing of the samples under study. The bioinformatic approaches were tested on the results of proteome analysis of human melanoma cell lines, obtained earlier by high-resolution liquid chromatography and mass spectrometry (LC-MS). A search for alternative splicing events for each of the cell lines studied was performed against the database generated from all known transcripts (RefSeq) and the one composed of peptide sequences, which included all biologically possible combinations of exons. The identifications were filtered using the prediction of both retention times and relative intensities of fragment ions in the corresponding mass spectra. The fragmentation mass spectra corresponding to the discovered alternative splicing events were additionally examined for artifacts. Selected splicing events were further validated at the mRNA level by quantitative PCR.

Proteomics-based scoring of cellular response to stimuli for improved characterization of signaling pathway activity.

Omics technologies focus on uncovering the complex nature of molecular mechanisms in cells and organisms, including biomarkers and drug targets discovery. Aiming at these tasks, we see that information extracted from omics data is still underused. In particular, characteristics of differentially regulated molecules can be combined in a single score to quantify the signaling pathway activity. Such a metric can be useful for comprehensive data interpretation to follow: (1) developing molecular responses in time; (2) potency of a drug on a certain cell culture; (3) ranking the signaling pathway activity in stimulated cells; and (4) integration of the omics data and assay-based measurements of cell viability, cytotoxicity, and proliferation. With recent advances in ultrafast mass spectrometry for quantitative proteomics allowing data collection in a few minutes, proteomics score for cellular response to stimuli can become a fast, accurate, and informative complement to bioassays. Here, we utilized an interquartile-based selection of differentially regulated features and a variety of schemes for quantifying cellular responses to come up with the quantitative metric for total cellular response and pathway activity. Validation was performed using antiproliferative and virus assays and label-free proteomics data collected for cancer cells subjected to drug stimulation.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics.

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

DirectMS1Quant: Ultrafast Quantitative Proteomics with MS/MS-Free Mass Spectrometry.

Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at a 1% false discovery rate (FDR) when using 5 min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000-5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue, we performed a one-on-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free (LFQ) and tandem mass tag quantitation (TMT), both based on data-dependent acquisition (DDA) and data-independent acquisition (DIA). For comparison, we performed a series of proteome-wide analyses of well-characterized (ground truth) and biologically relevant samples, including a mix of UPS1 proteins spiked at different concentrations into an Echerichia coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods, yet the latter two utilized a 10-20-fold longer instrumentation time.

Multi-Omics Analysis of Glioblastoma Cells' Sensitivity to Oncolytic Viruses.

Oncolytic viruses have gained momentum in the last decades as a promising tool for cancer treatment. Despite the progress, only a fraction of patients show a positive response to viral therapy. One of the key variable factors contributing to therapy outcomes is interferon-dependent antiviral mechanisms in tumor cells. Here, we evaluated this factor using patient-derived glioblastoma multiforme (GBM) cultures. Cell response to the type I interferons' (IFNs) stimulation was characterized at mRNA and protein levels. Omics analysis revealed that GBM cells overexpress interferon-stimulated genes (ISGs) and upregulate their proteins, similar to the normal cells. A conserved molecular pattern unambiguously differentiates between the preserved and defective responses. Comparing ISGs' portraits with titration-based measurements of cell sensitivity to a panel of viruses, the "strength" of IFN-induced resistance acquired by GBM cells was ranked. The study demonstrates that suppressing a single ISG and encoding an essential antiviral protein, does not necessarily increase sensitivity to viruses. Conversely, silencing IFIT3 and PLSCR1 genes in tumor cells can negatively affect the internalization of vesicular stomatitis and Newcastle disease viruses. We present evidence of a complex relationship between the interferon response genes and other factors affecting the sensitivity of tumor cells to viruses.

A proteomics sample metadata representation for multiomics integration and big data analysis.

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

Accelerating photofragmentation UV Spectroscopy-Mass spectrometry fingerprinting for quantification of isomeric peptides.

Identification of isomeric biomolecules remains a challenging analytical problem. A recently developed spectroscopic method that combines UV photofragmentation and mass spectrometry for fingerprinting of cold ions (2D UV-MS), has already demonstrated its high performance in the library-based identification and quantification of different types of biomolecular isomers. The practical use of the method has been hindered by a slow rate of data acquisition, which makes the fingerprinting incompatible with high-throughput analysis and online liquid chromatography (LC) separation. Herein we demonstrate how the use of a few pre-selected wavelengths can accelerate the method by two orders of magnitude without a significant loss of accuracy. As a proof of principle, 2D UV-MS fingerprinting was coupled to online LC separation and tested for quantification of isomeric peptides containing either Asp or isoAsp residues. The relative concentrations of the peptides mixed in solution have been determined, on average, with better than 4% and 6% accuracy for resolving and non-resolving gradients of LC separation, respectively.

New insights into molecular changes in skeletal muscle aging and disease: Differential alternative splicing and senescence.

Progressive loss of muscle mass and function due to muscle fiber atrophy and loss in the elderly and chronically ill is now defined as sarcopenia. It is a major contributor to loss of independence, disability, need of long-term care as well as overall mortality. Sarcopenia is a heterogenous disease and underlying mechanisms are not completely understood. Here, we newly identified and used Tmem158, alongside Cdkn1a, as relevant senescence and denervation markers (SDMs), associated with muscle fiber atrophy. Subsequent application of laser capture microdissection (LCM) and RNA analyses revealed age- and disease-associated differences in gene expression and alternative splicing patterns in a rodent sarcopenia model. Of note, genes exhibiting such differential alternative splicing (DAS) are mainly involved in the contractile function of the muscle. Many of these splicing events are also found in a mouse model for myotonic dystrophy type 1 (DM1), underscoring the premature aging phenotype of this disease. We propose to add differential alternative splicing to the hallmarks of aging.

Improving the Protein Inference from Bottom-Up Proteomic Data Using Identifications from MS1 Spectra.

Protein inference is one of the crucial steps in proteome characterization using a bottom-up approach. Multiple algorithms to solve the problem are focused on extensive analysis of shared peptides identified from fragmentation mass spectra (MS/MS). However, many protein homologues with a similar amino acid sequence typically have identical lists of identified peptides due to the problem of proteome undersampling in a bottom-up approach and, thus, cannot be distinguished by existing protein inference methods. Here, we propose the use of peptide feature information extracted from precursor mass spectra to assist in identification of proteins otherwise indistinguishable from MS/MS. The proposed method was integrated with a protein inference algorithm based on the parsimony principle and built-in in the postsearch utility Scavager. The results demonstrate increasing accuracy and efficiency of homologous protein identifications for the well characterized data sets including the one with known protein sequences from iPRG-2016 study.

Cysteine alkylation methods in shotgun proteomics and their possible effects on methionine residues.

In order to optimize sample preparation for shotgun proteomics, we compared four cysteine alkylating agents: iodoacetamide, chloroacetamide, 4-vinylpyridine and methyl methanethiosulfonate, and estimated their effects on the results of proteome analysis. Because alkylation may result in methionine modification in vitro, proteomics data were searched for methionine to isothreonine conversions, which may mimic genomic methionine to threonine substitutions found in proteogenomic analyses. We found that chloroacetamide was superior to the other reagents in terms of the number of identified peptides and undesirable off-site reactions. Among the reagents evaluated, iodoacetamide increased the rate of methionine-to-isothreonine conversion, especially if the sample was prepared in gel. The presence of proline following methionine in a protein sequence increased the modification rate as well. Generally, the methionine-to-isothreonine conversion events were relatively rare, but should be taken into account in proteogenomic studies when searching for single nucleotide polymorphism events at the protein level. Additionally, we have evaluated other methionine modifications, such as oxidation and carbamidomethylation. We found that carbamidomethylation may affect up to 80% of peptides containing methionine under the condition of iodoacetamide alkylation. In this case, carbamidomethylation of methionine is more common than oxidation and should be accounted for as a variable modification during proteomic search. SIGNIFICANCE: One of the most trending questions in bottom-up proteomics is the depth of proteome profiling, in other words, the coverage of proteins by identified tryptic peptides. In proteogenomics, where the identification of a single peptide, e.g. bearing an amino acid substitution, may be of interest, high sequence coverage is especially important. Chemical modifications during sample preparation may mimic biologically significant coding mutations at the proteome level. A typical example of such modification is methionine to isothreonine conversion during alkylation, which mimics methionine to threonine substitution in protein sequences due to respective genomic mutations. Therefore, the studies on the proper selection of alkylating reagents which balance the cysteine alkylation efficiency and the extent of methionine conversion upon conventional proteomic sample preparation workflow are crucial for the outcome of proteogenomic analyses and should present a general interest for the proteomic community.

Identification-Free Control over the Precursor Isotopic Mass Misassignment in Orbitrap-Based Proteomics.

Selection of a precursor ion from a peptide isotopic cluster to obtain a fragmentation mass spectrum is a crucial step in data-dependent proteome analysis. However, the monoisotopic mass assignment performed in this step is often an issue confronted by the data acquisition software of hybrid Orbitrap FTMS that is most widely used in proteomics. To address the problem, many data processing tools, such as raw data converters and search engines, have optional accounting for the precursor mass shift due to the isotopic error. These solutions require additional data preprocessing steps and lead to an increase in the search space, thus making the analysis longer and/or less reliable. In this work, we processed 100 Orbitrap-based LC-MS/MS runs from 10 publicly available data sets to examine the rate of precursor isotope misassignment. The effect from taking the isotope error into account during the search on the number of identified peptides varied in a wide range from 0 to 33%. Thus, it may be tempting to spend extra time before or during a search to account for the mass assignment issue. Alternatively, this effect can be predicted a priori using an identification-free metric, which can be a part of data quality control software. Based on the results obtained in this work, we propose such a metric be further added into the visual and intuitive quality control software, viQC, developed previously and available at https://github.com/lisavetasol/viQC. It takes about a minute to calculate and plot nine quality metrics, including the proposed one for typical proteome analysis.

DirectMS1: MS/MS-Free Identification of 1000 Proteins of Cellular Proteomes in 5 Minutes.

Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty cycle. It was always tempting to implement approaches that do not require MS/MS, yet they were constantly failing to achieve a meaningful depth of quantitative proteome coverage within short experimental times, which is particularly important for clinical or biomarker-discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free method, DirectMS1, for bottom-up proteomics. The method is compared with the standard MS/MS-based data-dependent acquisition approach for proteome-wide analysis using 5 min LC gradients. Specifically, we demonstrate identification of 1 000 protein groups for a standard HeLa cell line digest. The amount of loaded sample was varied in a range from 1 to 500 ng, and the method demonstrated 10-fold higher sensitivity. Combined with the recently introduced Diffacto approach for relative protein quantification, DirectMS1 outperforms most popular MS/MS-based label-free quantitation approaches because of significantly higher protein sequence coverage.

Protein corona formed on silver nanoparticles in blood plasma is highly selective and resistant to physicochemical changes of the solution.

Nanoparticles (NPs) in contact with protein-containing media such as biological fluids rapidly acquire a surface layer of proteins, known as the protein corona. The protein composition and structural properties of the protein corona are crucial for NP interactions with living cells. Although much has been learned about the protein corona phenomenon, further elucidation could benefit from extensive quantitative proteomics analysis. Herein we report a comprehensive quantitative characterization (>350 proteins) of the corona that formed on 60 nm silver NPs via interaction with human blood plasma, as a function of pH and temperature. By varying the pH and temperature one can access different conformational spaces and charge localizations of the plasma proteins, which in turn provide knowledge pertinent to how the proteome corresponds to binding affinity. Thirty-eight percent of the quantified proteins bind at all temperatures, 47% at all pH values, and of these most persistent proteins, approximately 60% do not significantly change in abundance within the protein corona. Evaluation of 544 protein properties (present in the Kyoto databank) suggests that binding of these proteins to NPs is determined by the extent of hydrophobicity, ß-sheet propensity, α-helical structure (and turns), and amino acid composition. Protein binding is promoted by a larger amount of ß-sheets, higher hydrophobicity, and a smaller amount of α-helices. Our work enhances researchers' knowledge of a long-standing, vexing aspect of the nano-bio interface.

Method for Identification of Threonine Isoforms in Peptides by Ultraviolet Photofragmentation of Cold Ions.

Identification of isomeric amino acid residues in peptides and proteins is challenging but often highly desired in proteomics. One of the practically important cases that require isomeric assignments is that associated with single-nucleotide polymorphism substitutions of Met residues by Thr in cancer-related proteins. These genetically encoded substitutions can yet be confused with the chemical modifications, arising from protein alkylation by iodoacetamide, which is commonly used in the standard procedure of sample preparation for proteomic analysis. Similar to the genetically encoded mutations, the alkylation also induces a conversion of methionine residues, but to the iso-threonine form. Recognition of the mutations therefore requires isoform-sensitive detection techniques. Herein, we demonstrate an analytical method for reliable identification of isoforms of threonine residues in tryptic peptides. It is based on ultraviolet photodissociation mass spectrometry of cryogenically cooled ions and a machine-learning algorithm. The measured photodissociation mass spectra exhibit isoform-specific patterns, which are independent of the residues adjacent to threonine or iso-threonine in a peptide sequence. A comprehensive metric-based evaluation demonstrates that, being calibrated with a set of model peptides, the method allows for isomeric identification of threonine residues in peptides of arbitrary sequence.

Proteogenomics of Malignant Melanoma Cell Lines: The Effect of Stringency of Exome Data Filtering on Variant Peptide Identification in Shotgun Proteomics.

The identification of genetically encoded variants at the proteome level is an important problem in cancer proteogenomics. The generation of customized protein databases from DNA or RNA sequencing data is a crucial stage of the identification workflow. Genomic data filtering applied at this stage may significantly modify variant search results, yet its effect is generally left out of the scope of proteogenomic studies. In this work, we focused on this impact using data of exome sequencing and LC-MS/MS analyses of six replicates for eight melanoma cell lines processed by a proteogenomics workflow. The main objectives were identifying variant peptides and revealing the role of the genomic data filtering in the variant identification. A series of six confidence thresholds for single nucleotide polymorphisms and indels from the exome data were applied to generate customized sequence databases of different stringency. In the searches against unfiltered databases, between 100 and 160 variant peptides were identified for each of the cell lines using X!Tandem and MS-GF+ search engines. The recovery rate for variant peptides was ∼1%, which is approximately three times lower than that of the wild-type peptides. Using unfiltered genomic databases for variant searches resulted in higher sensitivity and selectivity of the proteogenomic workflow and positively affected the ability to distinguish the cell lines based on variant peptide signatures.

IdentiPy: An Extensible Search Engine for Protein Identification in Shotgun Proteomics.

We present an open-source, extensible search engine for shotgun proteomics. Implemented in Python programming language, IdentiPy shows competitive processing speed and sensitivity compared with the state-of-the-art search engines. It is equipped with a user-friendly web interface, IdentiPy Server, enabling the use of a single server installation accessed from multiple workstations. Using a simplified version of X!Tandem scoring algorithm and its novel "autotune" feature, IdentiPy outperforms the popular alternatives on high-resolution data sets. Autotune adjusts the search parameters for the particular data set, resulting in improved search efficiency and simplifying the user experience. IdentiPy with the autotune feature shows higher sensitivity compared with the evaluated search engines. IdentiPy Server has built-in postprocessing and protein inference procedures and provides graphic visualization of the statistical properties of the data set and the search results. It is open-source and can be freely extended to use third-party scoring functions or processing algorithms and allows customization of the search workflow for specialized applications.

FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics.

Recent advances in mass spectrometry and separation technologies created the opportunities for deep proteome characterization using shotgun proteomics approaches. The "real world" sample complexity and high concentration range limit the sensitivity of this characterization. The common strategy for increasing the sensitivity is sample fractionation prior to analysis either at the protein or the peptide level. Typically, fractionation at the peptide level is performed using linear gradient high-performance liquid chromatography followed by uniform fraction collection. However, this way of peptide fractionation results in significantly suboptimal operation of the mass spectrometer due to the non-uniform distribution of peptides between the fractions. In this work, we propose an approach based on peptide retention time prediction allowing optimization of chromatographic conditions and fraction collection procedures. An open-source software implementing the approach called FractionOptimizer was developed and is available at http://hg.theorchromo.ru/FractionOptimizer . The performance of the developed tool was demonstrated for human embryonic kidney (HEK293) cell line lysate. In these experiments, we improved the uniformity of the peptides distribution between fractions. Moreover, in addition to 13,492 peptides, we found 6787 new peptides not identified in the experiments without fractionation and up to 800 new proteins (or 25%). Graphical abstract The analysis workflow employing FractionOptimizer software.

Comparative proteomics as a tool for identifying specific alterations within interferon response pathways in human glioblastoma multiforme cells.

An acquisition of increased sensitivity of cancer cells to viruses is a common outcome of malignant progression that justifies the development of oncolytic viruses as anticancer therapeutics. Studying molecular changes that underlie the sensitivity to viruses would help to identify cases where oncolytic virus therapy would be most effective. We quantified changes in protein abundances in two glioblastoma multiforme (GBM) cell lines that differ in the ability to induce resistance to vesicular stomatitis virus (VSV) infection in response to type I interferon (IFN) treatment. In IFN-treated samples we observed an up-regulation of protein products of some IFN-regulated genes (IRGs). In total, the proteome analysis revealed up to 20% more proteins encoded by IRGs in the glioblastoma cell line, which develops resistance to VSV infection after pre-treatment with IFN. In both cell lines protein-protein interaction and signaling pathway analyses have revealed a significant stimulation of processes related to type I IFN signaling and defense responses to viruses. However, we observed a deficiency in STAT2 protein in the VSV-sensitive cell line that suggests a de-regulation of the JAK/STAT/IRF9 signaling. The study has shown that the up-regulation of IRG proteins induced by the IFNα treatment of GBM cells can be detected at the proteome level. Similar analyses could be applied for revealing functional alterations within the antiviral mechanisms in glioblastoma samples, accompanying by acquisition of sensitivity to oncolytic viruses. The approach can be useful for discovering the biomarkers that predict a potential sensitivity of individual glioblastoma tumors to oncolytic virus therapy.