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Bio:Dr Rebeca Kawahara (first author): Dr Kawahara is a passionate early career scientist funded by Cancer Institute NSW Early Career Research Fellowship (2019-22) and a member of the Analytical Glycoimmunology Group, led by Dr Morten Thaysen-Andersen in the Department of Molecular Sciences at Macquarie University, Sydney, Australia. Her core expertise is to develop and apply advanced mass spectrometry-based glycoproteomics, proteomics and glycomics to study complex biological systems. Her current research focus is to use integrated multi-omics approaches to advance our molecular understanding of the role of glycoproteins in health and diseases, including cancer and immune-related disorders.
Mrs. Anastasia Chernykh (second author): Anastasia Chernykh is a second-year PhD candidate in the Analytical Glycoimmunology Group situated in the Department of Molecular Sciences, Macquarie University, Sydney, Australia. Her research focuses on the structural and functional characterisation of protein glycosylation in inflammatory processes using glycomics and glycoproteomics.
Main text:Conducted through the HUPO Human Proteome Project – Human Glycoproteomics Initiative (HGI), this international community study brought together field-leading developers and expert users of glycoproteomics software to evaluate the performance of informatics solutions for system-wide glycopeptide mass spectrometric analysis (1). In total, 25 teams from 11 countries across five continents signed up for the challenge out of which 22 teams (~90%) completed the study (see accompanying figure for location of participants and key contributing authors). All teams were provided the same two high-resolution LC-MS/MS data files of N- and O-glycopeptides from human serum proteins generated on an Thermo Orbitrap Fusion Lumos mass spectrometer using different dissociation methods (HCD, ETciD, EThcD, CID) (data kindly provided by Drs Rosa Viner and Sergei Snovida, Thermo Fisher Scientific). A synthetic N-glycopeptide was included as a positive control. The study design including the sample type, preparation and data collection method was carefully chosen to mimic conditions typically encountered in glycoproteomics analysis while also aiming to accommodate most available informatic solutions and to appeal to users in the field. All teams were asked to identify intact N- and O-glycopeptides from the two shared data files and report back their identifications and their search strategies in a comprehensive and standardised reporting template. Completed reports were thoroughly checked by the independent study organisers for compliance to the study guidelines to enable a fair comparison between teams.
The identified glycopeptides varied dramatically between teams as illustrated by the wide range of N-glycopeptides (49-2,122 N-glycoPSMs) and O-glycopeptides (5-578 O-glycoPSMs) reported by the participants. Discrepant and non-uniform reporting is a recognised challenge in glycoproteomics. The search strategies employed by the teams were found to be highly diverse as exemplified by the variation in the applied glycan search space (23-381 N-glycan compositions and 3-223 O-glycan compositions). Despite the discrepant reporting and varied search strategies, high-confidence lists spanning 163 N- and 23 O-glycopeptides commonly reported by the teams could be generated from the standardised reports. These consensus glycopeptides form an important reference for future studies of the human serum glycoproteome and have therefore been made publicly available (GlyConnect Reference ID 2943).
The relative team performance for N- and O-glycopeptide data analysis was comprehensively established through multiple carefully constructed independent performance tests. The team scoring and ranking were subsequently validated using an orthogonal scoring method. Excitingly, the performance testing revealed that several high-performance glycoproteomics informatics solutions, some well-established (Protein Prospector, Byonic) and others only recently developed (IQ-GPA, GlycoPAT, glyXtoolMS), from both academic and commercial origins are available for N- and O-glycopeptide data analysis.
Backed by robust statistics, deep mining of the performance data also unearthed a set of both software-independent and software-specific performance-associated search variables and identified key parameters important for high-performance glycoproteomics data analysis. Notably, exploration of the impact of the different search strategies on the glycoproteomics data output by the popular Byonic search engine used by 11 teams led to recommendations for improved “high coverage” and “high accuracy” glycoproteomics search strategies that will immediately benefit researchers in the field when studying biological samples of this nature.
Most software solutions currently available for glycoproteomics data analysis were evaluated in this study. However, several newer glycopeptide search engines e.g. pGlyco, MSFragger-Glyco, O-Pair Search, and StrucGP were not represented due to LC-MS/MS data incompatibility or due to their development after the study period. Follow-up efforts to compare the performance of the latest glycoproteomics software upgrades and informatics solutions not included in this study are therefore being drafted within the next study of the Human Glycoproteomics Initiative.
This community-driven study concludes that diverse software for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies, and specifies key variables that may guide future software developments and assist informatics decision-making in glycoproteomics data analysis. While informatics challenges undoubtedly still exist in glycoproteomics, our study interestingly highlights that several computational tools, some already demonstrating high performance, others considerable potential, are available to the community.
Reference:Kawahara, R., Chernykh, A., Alagesan, K., Bern, M., Cao, W., Chalkley, R. J., Cheng, K., Choo, M. S., Edwards, N., Goldman, R., Hoffmann, M., Hu, Y., Huang, Y., Kim, J. Y., Kletter, D., Liquet-Weiland, B., Liu, M., Mechref, Y., Meng, B., Neelamegham, S., Nguyen-Khuong, T., Nilsson, J., Pap, A., Park, G. W., Parker, B. L., Pegg, C. L., Penninger, J. M., Phung, T. K., Pioch, M., Rapp, E., Sakalli, E., Sanda, M., Schulz, B. L., Scott, N. E., Sofronov, G., Stadlmann, J., Vakhrushev, S. Y., Woo, C. M., Wu, H.-Y., Yang, P., Ying, W., Zhang, H., Zhang, Y., Zhao, J., Zaia, J., Haslam, S. M., Palmisano, G., Yoo, J. S., Larson, G., Khoo, K.-H., Medzihradszky, K. F., Kolarich, D., Packer, N. H., and Thaysen-Andersen, M. (2021) Community Evaluation of Glycoproteomics Informatics Solutions Reveals High-Performance Search Strategies of Serum N- and O-Glycopeptide Data. Under final stages of consideration. Available at bioRxiv, (https://www.biorxiv.org/content/10.1101/2021.03.14.435332v3)
By Monique Zahn, neXtProt team, SIB Swiss Institute of Bioinformatics, Switzerland
Our understanding of human biology at the molecular level has gone from sequencing the human genome in 2001 to experimentally validating over 90% of the human proteome in 2020. Now that the protein “parts list” is almost complete, it is time to turn to our efforts to annotate the function of these human proteins, i.e. to complete the functional human proteome. The C-HPP neXt-CP50 pilot project launched in 2018 aims to characterize 50 functionally uncharacterized but identified PE1 proteins (uPE1 proteins) (1). In the current neXtProt release (data release 2021-02-18), there are 20,379 entries, of which 1,273 are uPE1 proteins and 396 are uMPs (proteins with evidence suggestive of their existence (PE2–4) having no known or predicted function. A manual workflow to generate hypotheses for the function of these uncharacterized proteins has been developed, based on predicted and experimental information on protein properties, interactions, tissular expression, subcellular localization, conservation in other organisms, as well as phenotypic data in mutant model organisms. This workflow has been applied in the frame of a course-based undergraduate research experience (CURE) organized at the University of Geneva (2). Function predictions for 21 entries, of which 20 are uPE1, are online. Many more entries are waiting for functional predictions!
A functional human proteome project page describing the goal, tracking progress and providing instructions on how to submit predictions to neXtProt is available at https://www.nextprot.org/about/functional-proteome-project.
Figure: Manual data mining workflow.
1. Paik YK et al. Launching the C-HPP neXt-CP50 Pilot Project for Functional Characterization of Identified Proteins with No Known Function. J Proteome Res. 2018 Dec 7;17(12):4042-4050. doi: 10.1021/acs.jproteome.8b00383
2. Duek P et al. Functionathon: a manual data mining workflow to generate functional hypotheses for uncharacterized human proteins and its application by undergraduate students. Database 2021, baab046 (2021). doi: 10.1093/database/baab046
Garni Barkhoudarian1, Julian P. Whitelegge2, Daniel F. Kelly1, Margaret Simonian2
1. John Wayne Cancer Institute, Providence St John’s Health Center, USA
2. David Geffen School of Medicine, University of California Los Angeles (UCLA), USA
Dr. Margaret Simonian has MPhil and PhD in Advanced Medicine. Works as a Research Scientist/Specialist at UCLA, David Geffen School of Medicine, previously worked as a Senior Research Fellow and Lecturer, at LA-Biomed Research Institute, Harbor-UCLA, and Macquarie University, Macquarie Neurosurgery (Australia). Her research interest focuses on utilizing Proteomics and Molecular Biology in biomarker discovery and drug development/therapy of diseases, as well as advancing cancer immunotherapy with Proteomics approaches, and Radioproteomics for cancer treatment. She is a recognised Reviewer & Editorial board member for multiple scientific journals and a member of (B/D-HPP).
Meningiomas are the most common benign intracranial tumors and their first-line treatment is surgical removal if the lesion can be largely removed at sufficiently low risk. However, a subset of patients develops more aggressive tumors. According to the World Health Organization (WHO), meningiomas are classified as typical (1), atypical (ll) and anaplastic (lll); up to 20% of patients may have atypical meningiomas and 1-3% may develop anaplastic or malignant subtypes . These aggressive subtypes of tumors typically exhibit more rapid tumor progression, invasiveness and recurrence precluding complete surgical removal and requiring additional therapies of radiosurgery/radiotherapy and chemotherapy . Occasionally, meningiomas have malignant transformation with distant metastases outside the central nervous system (CNS).
Few genetic and proteomics markers have been studied for meningioma subtypes with various aims [3,4] and their correlation to clinical behaviour and response to therapy is limited. While there is a notable overlap with some biomarkers found in other malignant neoplasms (glioblastoma, adenocarcinoma, squamous cell carcinoma and melanoma), the mechanisms that result in transformation from benign meningiomas to more aggressive subtypes are poorly understood.
Hence, this study aimed to better define biomarkers of transformation into the aggressive/malignant subtypes, and to identify targets for future therapies. Three tumor subtype (typical, atypical and anaplastic), and control (fresh cadaveric dura) tissues were used for the proteomics analysis. Multiplex peptide stable isotope labelling method was used to label all samples. With this method, all primary amines (the N terminus and the side chain of lysine residues) in a peptide mixture are converted to dimethylamines. The labelled samples are then mixed in equal ratios and analysed by liquid chromatography–mass spectrometry (LC/MS). The mass difference of the dimethyl labels is used to compare the peptide quantity across all samples.
Our analysis and observation was focused on the proteins that showed up or down-regulation in one phenotype compared to the others and compare to the control, as those proteins could potentially be investigated as biomarkers for aggressive tumors, e.g. protein alpha-adducin, was expressed in C, I and II only, and it was up-regulated in grade I by 3 fold compare to the control, however in II was down-regulated by 0.25 compare to the control, and wasn’t detected in III; hence the expression ratio for (I : II) was 11.6 fold. This may suggest that this protein is mainly present in the non-aggressive form of meningioma, or its representing gene (ADD1) may be switched off in the aggressive forms. Many other proteins showed similar pattern (Fig 1). Another intriguing observation of this data was the presence of some proteins in one subtype only compare to other subtypes and compare to the control. Twenty three proteins were detected in grade III only, including tumor protein D52, lysosome membrane protein 2, splicing factor-1 and MUC18. These proteins are of importance in biomarker study of meningiomas due to their unique expression. The full data is available in PubMed .
This data suggests the feasibility of identifying and quantifying the proteins in brain minengioma tissues for comparison studies. We are obtaining larger numbers of specimen to conduct a large scale experiments to significantly identify novel protein biomarkers that correlate with the aggressive tumors. These biomarkers will be clinically utilized in future management of patients, to better identify aggressive tumors for closer surveillance and application of novel targeted therapies. Ultimately this may potentially reduce the need for major high-risk surgery in this patient population.
Figure 1: Chart of the expression levels of selected proteins from. Their intensities in control (C) and meningioma tissues grade (I, II and III).
1. Commins DL, Atkinson RD, Burnett ME (2007) Review of meningioma histopathology. Neurosurg Focus 23: E3.
2. Doleželová H, Hynková L, Pospíšil P, Kazda T, Slampa P, et al. (2012) Therapeutic results of the treatment brain tumors using radiosurgery and stereotactic radiotherapy. Klin Onkol 25: 445-451
3. Lusis EA, Chicoine MR, Perry A (2005) High throughput screening of meningioma biomarkers using a tissue microarray. J Neurooncol 73: 219-223.
4. Sharma S, Ray S, Moiyadi A, Sridhar E, Srivastava S (2014) Quantitative proteomic analysis of meningiomas for the identificationof surrogate protein markers. Sci Rep 4: 7140.
5. Barkhoudarian G, Whitelegge JP, Kelly DF, Simonian M (2016) Proteomics Analysis of Brain Meningiomas in Pursuit of Novel Biomarkers of the Aggressive Behavior. J Proteomics Bioinform 9: 053-057.
Written by Thomas G. Martin & Jonathan A. Kirk, Department of Cell and Molecular Physiology, Loyola University Stritch School of Medicine, Maywood, IL.
Thomas Martin is a graduate student in Dr. Jonathan Kirk’s lab at Loyola University Stritch School of Medicine in Chicago, where he uses proteomics to explore mechanisms of cellular protein quality control in cardiomyocytes. He is particularly interested in how cardiac sarcomeres are dynamically turned over in the healthy heart and how this process is impacted in heart failure to cause dysfunction.
The maintenance of protein homeostasis (proteostasis) is fundamental to proteome stability and cell survival. From synthesis by the ribosome, to folding and maintenance by dozens of molecular chaperones, and finally degradation through the ubiquitin-proteasome system or autophagy, the life cycle of a protein is carefully regulated1. Dysfunctional protein quality control (PQC) is implicated in the pathophysiology of heart disease2, the number one cause of death in the United States3. However, our mechanistic understanding of cardiac PQC is relatively weak, due partially to limitations in experimental approaches, providing a significant obstacle to restoring proteostasis as a strategy for treating heart failure. Proteomics techniques offer unique opportunities to advance the understanding of cardiac PQC and help bridge the gap to treatment.
Harnessing Ubiquitinomics to Study Cardiac PQC
When proteins are damaged, often from stress-induced denaturation, they are targeted to degradation pathways to prevent cytotoxic protein aggregation. To denote which members should be removed by these pathways, the damaged proteins are tagged at lysine residues with ubiquitin. Ubiquitin-enrichment mass spectrometry is a great option for unbiased assessment of the global heart ubiquitinome and for use in more targeted studies, where the impact of disease or individual proteins on ubiquitin signaling are of interest. The technique, which was introduced a decade ago by Xu et al., relies on immunoaffinity purification of ubiquitinated substrates at the peptide level6. Specifically, when ubiquitinated proteins are digested with trypsin, the enzyme cleaves all but two amino acids of the ubiquitin moiety, leaving a diglycine lysine remnant modification. Diglycine lysine peptides can then be enriched by immunoprecipitation and identified by mass spectrometry7.
Our group recently adopted this approach to characterize how heart failure changes protein ubiquitination of the cardiac sarcomere. We found several sarcomeric proteins had increased ubiquitination in myocardial samples from human dilated cardiomyopathy, suggesting they were tagged for degradation but the clearance either stalled completely or was operating at an inadequate rate8. There are other recent examples of targeted ubiquitinomics in the skeletal muscle field, where the approach has been used to characterize substrates of the E3 ubiquitin ligase ASB2β in skeletal muscle atrophy9 and quantify changes in exercise-regulated ubiquitin signaling10. The only other examples thus far in the cardiac muscle field are studies characterizing the global heart ubiquitinome11, however these studies are few and global changes in disease have not been explored. While there has been limited use of this technique to date in the cardiovascular field, the abundance of detailed protocols and the commercially available diglycine lysine antibody make ubiquitinated-peptide enrichment a feasible approach for most laboratories with access to the necessary mass spectrometry instrumentation.
Monitoring Proteome Turnover Dynamics with Stable Isotope Labeling
Traditional molecular biology approaches such as western blot, or broader analyses like quantitative proteomics, are useful for observing changes in protein expression at a single time point. However, more sophisticated methods are necessary to explore proteome dynamics and determine whether protein expression changes arise from altered protein synthesis or protein stability/degradation. Proteomic techniques using stable isotope labeling provide a holistic assessment of protein turnover kinetics and can be much more informative to the experimenter. Using deuterium oxide (2H2O) amino acid labeling combined with high resolution mass spectrometry, Lam et al. interrogated in vivo protein turnover in a mouse model of adverse cardiac remodeling stemming from chronic β-adrenergic stimulation and found over 1,000 proteins with altered turnover kinetics12. Variations of this labeling technique and model have been used since to explore the proteome kinetics of the remodeling heart in pathological hypertrophy13,14. In a another study, Shekar et al. specifically explored mitochondrial proteome kinetics in a mouse model of heart failure secondary to trans-aortic constriction and showed that heart failure reduced mitochondrial protein content and increased the turnover rate of metabolic proteins15. Studies using stable isotope labeling in other models of heart disease remain to be performed, but it will be particularly interesting to determine how the kinetics are affected between various etiologies of heart failure, as well as in disease manifestations of genetic origin.
Beyond quantifying turnover kinetics in models of disease, the stable isotope labeling technique has been used to determine the effect of autophagy activation on cardiac proteome turnover. Dai and colleagues used deuterated leucine in combination with caloric restriction or rapamycin administration in a mouse model of aging and found that protein half-lives significantly increased with autophagy activation16. That negative age-dependent proteome remodeling can be reversed with autophagy activation is promising and one can speculate that the same may be true for heart failure, though such studies have not yet been performed.
These proteomic approaches and others are under-utilized in the study of cardiac PQC, but it is clear from recent studies how much more informative they can be when used to supplement classical approaches.
Figure 1. Basic proteomics approaches for studying cardiac PQC. A. Paradigm for purification and identification of ubiquitinated proteins from the myocardium. B. Simplified approach to studying protein turnover dynamics via in vivo stable isotope protein labeling with deuterated water.
1. Chen, B., Retzlaff, M., Roos, T. & Frydman, J. Cellular strategies of protein quality control. Cold Spring Harb. Perspect. Biol. 3, (2011).
2. Martin, T. G. & Kirk, J. A. Under construction: The dynamic assembly, maintenance, and degradation of the cardiac sarcomere. J. Mol. Cell. Cardiol. 148, 89–102 (2020).
3. Dassanayaka, S. & Jones, S. P. Recent Developments in Heart Failure. Circulation Research vol. 117 (2015).
4. Willis, M. S., Townley-Tilson, W. H. D., Kang, E. Y., Homeister, J. W. & Patterson, C. Sent to destroy: The ubiquitin proteasome system regulates cell signaling and protein quality control in cardiovascular development and disease. Circulation Research vol. 106 463–478 (2010).
5. Abdellatif, M., Sedej, S., Carmona-Gutierrez, D., Madeo, F. & Kroemer, G. Autophagy in cardiovascular aging. Circ. Res. 123, 803–824 (2018).
6. Xu, G., Paige, J. S. & Jaffrey, S. R. Global analysis of lysine ubiquitination by ubiquitin remnant immunoaffinity profiling. Nat. Biotechnol. 28, (2010).
7. Udeshi, N. D., Mertins, P., Svinkina, T. & Carr, S. A. Large-scale identification of ubiquitination sites by mass spectrometry. Nat. Protoc. 8, 1950–1960 (2013).
8. Martin, T. G. et al. Cardiomyocyte Contractile Impairment in Heart Failure Results from Reduced BAG3-mediated Sarcomeric Protein Turnover. Nat. Commun. (2021).
9. Goodman, C. A., Davey, J. R., Hagg, A., Parker, B. L. & Gregorevic, P. Dynamic Changes to the Skeletal Muscle Proteome and Ubiquitinome Induced by the E3 Ligase, ASB2β. Mol. Cell. Proteomics 20, (2021).
10. Parker, B. L., Kiens, B., Wojtaszewski, J. F. P., Richter, E. A. & James, D. E. Quantification of exercise-regulated ubiquitin signaling in human skeletal muscle identifies protein modification cross talk via NEDDylation. FASEB J. 34, (2020).
11. Heunis, T., Lamoliatte, F., Marín-Rubio, J. L., Dannoura, A. & Trost, M. Technical report: Targeted proteomic analysis reveals enrichment of atypical ubiquitin chains in contractile murine tissues. J. Proteomics 229, (2020).
12. Lam, M. P. Y. et al. Protein kinetic signatures of the remodeling heart following isoproterenol stimulation. J. Clin. Invest. 124, (2014).
13. McClatchy, D. B. et al. Quantitative temporal analysis of protein dynamics in cardiac remodeling. J. Mol. Cell. Cardiol. 121, (2018).
14. Lau, E. et al. Integrated omics dissection of proteome dynamics during cardiac remodeling. Nat. Commun. 9, (2018).
15. Shekar, K. C. et al. Cardiac mitochondrial proteome dynamics with heavy water reveals stable rate of mitochondrial protein synthesis in heart failure despite decline in mitochondrial oxidative capacity. J. Mol. Cell. Cardiol. 75, (2014).
16. Dai, D. F. et al. Altered proteome turnover and remodeling by short-term caloric restriction or rapamycin rejuvenate the aging heart. Aging Cell 13, (2014).
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