Computational and statistical methods for high-throughput analysis of post-translational modifications of proteins

msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level

In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

Conjugation site characterization of antibody-drug conjugates using electron-transfer/higher-energy collision dissociation (EThcD)

Antibody-drug conjugates (ADCs) are formed by binding of cytotoxic drugs to monoclonal antibodies (mAbs) through chemical linkers. A comprehensive evaluation of the critical quality attributes (CQAs) of ADCs is vital for drug development but remains challenging owing to ADC structural heterogeneity than mAbs. Drug conjugation sites can considerably affect ADC properties, such as stability and pharmacokinetics, however, few studies have focused on method development in this area owing to technical challenges. Hybrid electron-transfer/higher-energy collision dissociation (EThcD) produces more fragment ions than conventional higher-energy collision dissociation (HCD) fragmentation, which aids in identifying and localizing post-translational modifications. Herein, we systematically employ EThcD to assess the fragmentation mode impact on conjugation site characterization for randomly conjugated and site-specific ADCs. EThcD generates more fragment ions in tandem mass spectrometry (MS/MS) spectra compared with HCD. Additional ions aid in pinpointing the correct conjugation sites that bear complex linker payload structures. Our study may contribute to the quality control of various preclinical and clinical ADCs.

MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is increasingly used to detect changes in posttranslational modifications (PTMs) in samples from different conditions. Analysis of data from such experiments faces numerous statistical challenges. These include the low abundance of modified proteoforms, the small number of observed peptides that span modification sites, and confounding between changes in the abundance of PTM and the overall changes in the protein abundance. Therefore, statistical approaches for detecting differential PTM abundance must integrate all the available information pertaining to a PTM site and consider all the relevant sources of confounding and variation. In this manuscript, we propose such a statistical framework, which is versatile, accurate, and leads to reproducible results. The framework requires an experimental design, which quantifies, for each sample, both peptides with PTMs and peptides from the same proteins with no modification sites. The proposed framework supports both label-free and tandem mass tag-based LC-MS/MS acquisitions. The statistical methodology separately summarizes the abundances of peptides with and without the modification sites, by fitting separate linear mixed effects models appropriate for the experimental design. Next, model-based inferences regarding the PTM and the protein-level abundances are combined to account for the confounding between these two sources. Evaluations on computer simulations, a spike-in experiment with known ground truth, and three biological experiments with different organisms, modification types, and data acquisition types demonstrate the improved fold change estimation and detection of differential PTM abundance, as compared to currently used approaches. The proposed framework is implemented in the free and open-source R/Bioconductor package MSstatsPTM.

Metabolomics and its Applications in Cancer Cachexia

Cancer cachexia (CC) is a complicated metabolic derangement and muscle wasting syndrome, affecting 50–80% cancer patients. So far, molecular mechanisms underlying CC remain elusive. Metabolomics techniques have been used to study metabolic shifts including changes of metabolite concentrations and disturbed metabolic pathways in the progression of CC, and expand further fundamental understanding of muscle loss. In this article, we aim to review the research progress and applications of metabolomics on CC in the past decade, and provide a theoretical basis for the study of prediction, early diagnosis, and therapy of CC.

Discovering the landscape of protein modifications

Protein modifications modulate nearly every aspect of cell biology in organisms, ranging from Archaea to Eukaryotes. The earliest evidence of covalent protein modifications was found in the early 20th century by studying the amino acid composition of proteins by chemical hydrolysis. These discoveries challenged what defined a canonical amino acid. The advent and rapid adoption of mass-spectrometry-based proteomics in the latter part of the 20th century enabled a veritable explosion in the number of known protein modifications, with more than 500 discrete modifications counted today. Now, new computational tools in data science, machine learning, and artificial intelligence are poised to allow researchers to make significant progress in discovering new protein modifications and determining their function. In this review, we take an opportunity to revisit the historical discovery of key post-translational modifications, quantify the current landscape of covalent protein adducts, and assess the role that new computational tools will play in the future of this field.

Bioinformatics in Immunoglobulin Glycosylation Analysis

Analytical methods developed for studying immunoglobulin glycosylation rely heavily on software tailored for this purpose. Many of these tools are now used in high-throughput settings, especially for the glycomic characterization of IgG. A collection of these tools, and the databases they rely on, are presented in this chapter. Specific applications are detailed in examples of immunoglobulin glycomics and glycoproteomics data processing workflows. The results obtained in the glycoproteomics workflow are emphasized with the use of dedicated visualizing tools. These tools enable the user to highlight glycan properties and their differential expression.

Glycan Compositions with GlyConnect Compozitor to Enhance Glycopeptide Identification

Glycoproteomics is unquestionably on the rise and its current development benefits from past experience in proteomics, in particular when attending to bioinformatics needs. An extensive range of software solutions is available, but the reproducibility of mass spectrometry data processing remains challenging. One of the key issues in running automated glycopeptide identification software is the selection of a reference glycan composition file. The default choices are often too broad, and a fastidious literature search to properly target this selection can be avoided. This chapter suggests the use of GlyConnect Compozitor to collect relevant information on glycosylation in a given tissue or cell line and shape an appropriate glycan composition set that can be input in the majority of search engines accommodating user-defined compositions.

Proteogenomic Characterization of the Pathogenic Fungus Aspergillus flavus Reveals Novel Genes Involved in Aflatoxin Production

Aspergillus flavus (A. flavus), a pathogenic fungus, can produce carcinogenic and toxic aflatoxins that are a serious agricultural and medical threat worldwide. Attempts to decipher the aflatoxin biosynthetic pathway have been hampered by the lack of a high-quality genome annotation for A. flavus. To address this gap, we performed a comprehensive proteogenomic analysis using high-accuracy mass spectrometry data for this pathogen. The resulting high-quality data set confirmed the translation of 8724 previously predicted genes and identified 732 novel proteins, 269 splice variants, 447 single amino acid variants, 188 revised genes. A subset of novel proteins was experimentally validated by RT-PCR and synthetic peptides. Further functional annotation suggested that a number of the identified novel proteins may play roles in aflatoxin biosynthesis and stress responses in A. flavus. This comprehensive strategy also identified a wide range of posttranslational modifications (PTMs), including 3461 modification sites from 1765 proteins. Functional analysis suggested the involvement of these modified proteins in the regulation of cellular metabolic and aflatoxin biosynthetic pathways. Together, we provided a high-quality annotation of A. flavus genome and revealed novel insights into the mechanisms of aflatoxin production and pathogenicity in this pathogen.

Untangling the fibre ball: Proteomic characterization of South American camelid hair fibres by untargeted multivariate analysis and molecular networking

The proteomic analysis of hairs, yarns or textiles has emerged as a powerful method to determine species of origin, mainly used in archaeozoological research and fraud control. Differentiation between the South American camelid (SAC) species (the wild guanaco and vicuña and their respective domesticates the llama and alpaca) is particularly challenging due to poor database information and significant hybridization between species. In this study, we analysed 41 modern and 4 archaeological samples from the four SACs species. Despite strong similarities with Old World Camelidae, we identified 7 peptides specific to SACs assigned to keratin K86 and the keratin-associated proteins KAP13-1 and KAP11-1. Untargeted multivariate analysis of the LC-MS data permitted to distinguish SAC species and propose discriminant features. MS/MS-based molecular networking combined with database-assisted de novo sequencing permitted to identify 5 new taxonomic peptides assigned to K33a, K81 and/or K83 keratins and KAP19-1. These peptides differentiate the two wild species, guanaco and vicuña. These results show the value of combining database search and untargeted metabolomic approaches for paleoproteomics, and reveal for the first time the potential of molecular networks to highlight deamidation related to diagenesis and cluster highly similar peptides related to interchain homologies or intra- or inter-specific polymorphism. SIGNIFICANCE: This study used an innovative approach combining multivariate analysis of LC-MS data together with molecular networking and database-assisted de novo sequencing to identify taxonomic peptides in palaeoproteomics. It constitutes the first attempt to differentiate between hair fibres from the four South American camelids (SACs) based on proteomic analysis of modern and archaeological samples. It provides different proteomic signatures for each of the four SAC species and proposes new SAC taxonomic peptides of interest in archaeozoology and fraud control. SACs have been extensively exploited since human colonization of South America but have not been studied to the extent of their economic, cultural and heritage importance. Applied to the analysis of ancient Andean textiles, our results should permit a better understanding of cultural and pastoral practices in South America. The wild SACs are endangered by poaching and black-market sale of their fibre. For the first time, our results provide discriminant features for the determination of species of origin of contraband fibre.

Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics

BACKGROUND

Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data.

RESULTS

Our methodology includes variance stabilization, normalization, and missing data imputation to account for the large dynamic range of PTM measurements. It also corrects biases from an enrichment protocol and reduces the random and systematic errors associated with label-free quantification. The performance of the methodology is tested by performing proteome-wide differential PTM quantitation using linear models analysis (limma). We objectively compare two imputation methods along with significance testing when using multiple-imputation for missing data.

CONCLUSION

Identifying PTMs in large-scale datasets is a problem with distinct characteristics that require new methods for handling missing data imputation and differential proteome analysis. Linear models in combination with multiple-imputation could significantly outperform a t-test-based decision method.

Clinically Relevant Post-Translational Modification Analyses-Maturing Workflows and Bioinformatics Tools

Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra-many of which likely harbour PTMs-through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant samples such as plasma and other body fluids, and well-established tools and options for data interpretation.

Genome Annotation of a Model Diatom Phaeodactylum tricornutum Using an Integrated Proteogenomic Pipeline

Diatoms comprise a diverse and ecologically important group of eukaryotic phytoplankton that significantly contributes to marine primary production and global carbon cycling. Phaeodactylum tricornutum is commonly used as a model organism for studying diatom biology. Although its genome was sequenced in 2008, a high-quality genome annotation is still not available for this diatom. Here we report the development of an integrated proteogenomic pipeline and its application for improved annotation of P. tricornutum genome using mass spectrometry (MS)-based proteomics data. Our proteogenomic analysis unambiguously identified approximately 8300 genes and revealed 606 novel proteins, 506 revised genes, 94 splice variants, 58 single amino acid variants, and a holistic view of post-translational modifications in P. tricornutum. We experimentally confirmed a subset of novel events and obtained MS evidence for more than 200 micropeptides in P. tricornutum. These findings expand the genomic landscape of P. tricornutum and provide a rich resource for the study of diatom biology. The proteogenomic pipeline we developed in this study is applicable to any sequenced eukaryote and thus represents a significant contribution to the toolset for eukaryotic proteogenomic analysis. The pipeline and its source code are freely available at https://sourceforge.net/projects/gapeproteogenomic.

Cobalamin-Dependent C-Methyltransferases From Marine Microbes: Accessibility via Rhizobia Expression

Cobalamin-dependent radical S-adenosylmethionine (rSAM) methyltransferases catalyze chemically challenging methylation reactions on diverse natural products at unactivated carbon centers. In vivo reconstitution and biosynthetic studies of natural product gene clusters encoding these enzymes are often severely limited by ineffective heterologous expression hosts, including the otherwise versatile Escherichia coli. In this chapter, we describe the use of rhizobia bacteria as effective expression hosts for cobalamin-dependent rSAM C-methyltransferases. We chose the natural product pathway encoding the heavily modified cytotoxic peptides, the polytheonamides, as our model pathway due to the presence of two methyltransferases responsible for a total of 17 C-methylations. Detailed protocols are given for vector construction, transformation, and heterologous expression in Rhizobium leguminosarum bv. viciae 3841. Additional methods pertaining to analytical separation and mass spectrometric analysis of modified peptides are also entailed. As genomics continues to uncover new enzymes and pathways from unknown and uncultivated microbes, use of metabolically distinct heterologous expression hosts like rhizobia will be a necessary tool to unravel the catalytic and metabolic diversity of marine microbial life.

Computational and Statistical Methods for High-Throughput Mass Spectrometry-Based PTM Analysis

Cell signaling and functions heavily rely on post-translational modifications (PTMs) of proteins. Their high-throughput characterization is thus of utmost interest for multiple biological and medical investigations. In combination with efficient enrichment methods, peptide mass spectrometry analysis allows the quantitative comparison of thousands of modified peptides over different conditions. However, the large and complex datasets produced pose multiple data interpretation challenges, ranging from spectral interpretation to statistical and multivariate analyses. Here, we present a typical workflow to interpret such data.

Statistical characterization of therapeutic protein modifications

Peptide mapping with liquid chromatography–tandem mass spectrometry (LC-MS/MS) is an important analytical method for characterization of post-translational and chemical modifications in therapeutic proteins. Despite its importance, there is currently no consensus on the statistical analysis of the resulting data. In this manuscript, we distinguish three statistical goals for therapeutic protein characterization: (1) estimation of site occupancy of modifications in one condition, (2) detection of differential site occupancy between conditions, and (3) estimation of combined site occupancy across multiple modification sites. We propose an approach, which addresses these goals in terms of summarizing the quantitative information from the mass spectra, statistical modeling, and model-based analysis of LC-MS/MS data. We illustrate the approach using an LC-MS/MS experiment from an antibody-drug conjugate and its monoclonal antibody intermediate. The performance was compared to a ‘naïve’ data analysis approach, by using computer simulation, evaluation of differential site occupancy in positive and negative controls, and comparisons of estimated site occupancy with orthogonal experimental measurements of N-linked glycoforms and total oxidation. The results demonstrated the importance of replicated studies of protein characterization, and of appropriate statistical modeling, for reproducible, accurate and efficient site occupancy estimation and differential analysis.

Flux control through protein phosphorylation in yeast

Protein phosphorylation is one of the most important mechanisms regulating metabolism as it can directly modify metabolic enzymes by the addition of phosphate groups. Attributed to such a rapid and reversible mechanism, cells can adjust metabolism rapidly in response to temporal changes. The yeast Saccharomyces cerevisiae, a widely used cell factory and model organism, is reported to show frequent phosphorylation events in metabolism. Studying protein phosphorylation in S. cerevisiae allows for gaining new insight into the function of regulatory networks, which may enable improved metabolic engineering as well as identify mechanisms underlying human metabolic diseases. Here we collect functional phosphorylation events of 41 enzymes involved in yeast metabolism and demonstrate functional mechanisms and the application of this information in metabolic engineering. From a systems biology perspective, we describe the development of phosphoproteomics in yeast as well as approaches to analysing the phosphoproteomics data. Finally, we focus on integrated analyses with other omics data sets and genome-scale metabolic models. Despite the advances, future studies improving both experimental technologies and computational approaches are imperative to expand the current knowledge of protein phosphorylation in S. cerevisiae.

Aedes-Borne Virus-Mosquito Interactions: Mass Spectrometry Strategies and Findings

Aedes-borne viruses are responsible for high-impact neglected tropical diseases and unpredictable outbreaks such as the ongoing Zika epidemics. Aedes mosquitoes spread different arboviruses such as Dengue virus (DENV), Chikungunya virus (CHIKV), and Zika virus, among others, and are responsible for the continuous emergence and reemergence of these pathogens. These viruses have complex transmission cycles that include two hosts, namely the Aedes mosquito as a vector and susceptible vertebrate hosts. Human infection with arboviruses causes diseases that range from subclinical or mild to febrile diseases, encephalitis, and hemorrhagic fever. Infected mosquitoes do not show detectable signs of disease, even though the virus maintains a lifelong persistent infection. The infection of the Aedes mosquito by viruses involves a molecular crosstalk between cell and viral proteins. An understanding of how mosquito vectors and viruses interact is of fundamental interest, and it also offers novel perspectives for disease control. In recent years, mass spectrometry (MS)-based strategies in combination with bioinformatics have been successfully applied to identify and quantify global changes in cellular proteins, lipids, peptides, and metabolites in response to viral infection. Although the information about proteomics in the Aedes mosquito is limited, the information that has been reported can set up the basis for future studies. This review reflects how MS-based approaches have extended our understanding of Aedes mosquito biology and the development of DENV and CHIKV infection in the vector. Finally, this review discusses future challenges in the field.

Urinary proteomics and metabolomics studies to monitor bladder health and urological diseases

Background

Assays of molecular biomarkers in urine are non-invasive compared to other body fluids and can be easily repeated. Based on the hypothesis that the secreted markers from the diseased organs may locally release into the body fluid in the vicinity of the injury, urine-based assays have been considered beneficial to monitoring bladder health and urological diseases. The urine proteome is much less complex than the serum and tissues, but nevertheless can contain biomarkers for diagnosis and prognosis of diseases. The urine metabolome has a much higher number and concentration of low-molecular metabolites than the serum or tissues, with a far lower lipid concentration, yet informs directly about dietary and microbial metabolism.

Discussion

We here discuss the use of mass spectrometry-based proteomics and metabolomics for urine biomarker assays, specifically with respect to the underlying mechanisms that trigger the pathological condition.

Conclusion

Molecular biomarker profiles, based on proteomics and metabolomics studies, reliably distinguish patients from healthy controls, stratify sub-populations with respect to treatment options, and predict therapeutic response of patients with urological disease.

Post-translational modifications of plant cell wall proteins and peptides: A survey from a proteomics point of view

Plant cell wall proteins (CWPs) and peptides are important players in cell walls contributing to their assembly and their remodeling during development and in response to environmental constraints. Since the rise of proteomics technologies at the beginning of the 2000's, the knowledge of CWPs has greatly increased leading to the discovery of new CWP families and to the description of the cell wall proteomes of different organs of many plants. Conversely, cell wall peptidomics data are still lacking. In addition to the identification of CWPs and peptides by mass spectrometry (MS) and bioinformatics, proteomics has allowed to describe their post-translational modifications (PTMs). At present, the best known PTMs consist in proteolytic cleavage, N-glycosylation, hydroxylation of P residues into hydroxyproline residues (O), O-glycosylation and glypiation. In this review, the methods allowing the capture of the modified proteins based on the specific properties of their PTMs as well as the MS technologies used for their characterization are briefly described. A focus is done on proteolytic cleavage leading to protein maturation or release of signaling peptides and on O-glycosylation. Some new technologies, like top-down proteomics and terminomics, are described. They aim at a finer description of proteoforms resulting from PTMs or degradation mechanisms. This article is part of a Special Issue entitled: Plant Proteomics--a bridge between fundamental processes and crop production, edited by Dr. Hans-Peter Mock.