Protein glycosylation and glycoinformatics for novel biomarker discovery in neurodegenerative diseases

Funding Information: This work was supported by FCT - Fundação para a Ciência e a Tecnologia, I.P., through iNOVA4Health (UIDB/04462/2020, UIDP/04462/2020) and LS4FUTURE Associated Laboratory (LA/P/0087/2020). Funding Information: The development of resources of the Glyco@Expasy initiative that includes GlyConnect and GlyConnect Compozitor, is supported by the Swiss Federal Government through the State Secretariat for Education, Research and Innovation (SERI ). Expasy is maintained by the Swiss Institute of Bioinformatics and hosted at the Vital-IT Competency Center. Funding Information: We thank: Prof. Nicolle H. Packer and Dr. Katherine Wongtrakul-Kish, School of Natural Sciences, Faculty of Science, & Engineering, Macquarie University, Sydney 2109, Australia, for their suggestions and critical reading of the manuscript; Dr. Julien Mariethoz for his contribution to integrating the brain dataset in GlyConnect. This work was supported by FCT - Fundação para a Ciência e a Tecnologia, I.P. through iNOVA4Health (UIDB/04462/2020, UIDP/04462/2020) and LS4FUTURE Associated Laboratory (LA/P/0087/2020). The development of resources of the Glyco@Expasy initiative that includes GlyConnect and GlyConnect Compozitor, is supported by the Swiss Federal Government through the State Secretariat for Education, Research and Innovation (SERI). Expasy is maintained by the Swiss Institute of Bioinformatics and hosted at the Vital-IT Competency Center. Publisher Copyright: © 2023 The AuthorsGlycosylation is a common post-translational modification of brain proteins including cell surface adhesion molecules, synaptic proteins, receptors and channels, as well as intracellular proteins, with implications in brain development and functions. Using advanced state-of-the-art glycomics and glycoproteomics technologies in conjunction with glycoinformatics resources, characteristic glycosylation profiles in brain tissues are increasingly reported in the literature and growing evidence shows deregulation of glycosylation in central nervous system disorders, including aging associated neurodegenerative diseases. Glycan signatures characteristic of brain tissue are also frequently described in cerebrospinal fluid due to its enrichment in brain-derived molecules. A detailed structural analysis of brain and cerebrospinal fluid glycans collected in publications in healthy and neurodegenerative conditions was undertaken and data was compiled to create a browsable dedicated set in the GlyConnect database of glycoproteins (https://glyconnect.expasy.org/brain). The shared molecular composition of cerebrospinal fluid with brain enhances the likelihood of novel glycobiomarker discovery for neurodegeneration, which may aid in unveiling disease mechanisms, therefore, providing with novel therapeutic targets as well as diagnostic and progression monitoring tools.publishersversionpublishe

Structure and engineering of tandem repeat lectins

International audience(100 − 120 words) Through their ability to bind complex glycoconjugates, lectins have unique specificity and potential for biomedical and biotechnological applications. In particular, lectins with short repeated peptides forming carbohydrate-binding domains are not only of high interest for understanding protein evolution but can also be used as scaffold for engineering novel receptors. Synthetic glycobiology now provides the tools for engineering the specificity of lectins as well as their structure, multivalency and topologies. This review focuses on the structure and diversity of two families of tandem-repeat lectins, i.e. β-trefoils and β-propellers, demonstrated as the most promising scaffold for engineering novel lectins

Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells - a benchmarking study

BACKGROUND: Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples. RESULTS: Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs. CONCLUSIONS: Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.The cost of this publication was funded by Vladimir Brusic. (Vladimir Brusic)Published versio

Glycated platelets proteome

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Artificial intelligence in orthopaedic surgery

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction

Sweet and Sour Ehrlichia: Glycoproteomics and Phosphoproteomics Reveal New Players in Ehrlichia ruminantium Physiology and Pathogenesis

Ehrlichia ruminantium; N-glycoproteins; O-GlcNAcylated proteinsEhrlichia ruminantium; N-glicoproteïnes; Proteïnes O-GlcNAciladesEhrlichia ruminantium; N-glicoproteínas; Proteínas O-GlcNAciladasUnraveling which proteins and post-translational modifications (PTMs) affect bacterial pathogenesis and physiology in diverse environments is a tough challenge. Herein, we used mass spectrometry-based assays to study protein phosphorylation and glycosylation in Ehrlichia ruminantium Gardel virulent (ERGvir) and attenuated (ERGatt) variants and, how they can modulate Ehrlichia biological processes. The characterization of the S/T/Y phosphoproteome revealed that both strains share the same set of phosphoproteins (n = 58), 36% being overexpressed in ERGvir. The percentage of tyrosine phosphorylation is high (23%) and 66% of the identified peptides are multi-phosphorylated. Glycoproteomics revealed a high percentage of glycoproteins (67% in ERGvir) with a subset of glycoproteins being specific to ERGvir (n = 64/371) and ERGatt (n = 36/343). These glycoproteins are involved in key biological processes such as protein, amino-acid and purine biosynthesis, translation, virulence, DNA repair, and replication. Label-free quantitative analysis revealed over-expression in 31 proteins in ERGvir and 8 in ERGatt. While further PNGase digestion confidently localized 2 and 5 N-glycoproteins in ERGvir and ERGatt, respectively, western blotting suggests that many glycoproteins are O-GlcNAcylated. Twenty-three proteins were detected in both the phospho- and glycoproteome, for the two variants. This work represents the first comprehensive assessment of PTMs on Ehrlichia biology, rising interesting questions regarding ER-host interactions. Phosphoproteome characterization demonstrates an increased versatility of ER phosphoproteins to participate in different mechanisms. The high number of glycoproteins and the lack of glycosyltransferases-coding genes highlight ER dependence on the host and/or vector cellular machinery for its own protein glycosylation. Moreover, these glycoproteins could be crucial to interact and respond to changes in ER environment. PTMs crosstalk between of O-GlcNAcylation and phosphorylation could be used as a major cellular signaling mechanism in ER. As little is known about the Ehrlichia proteins/proteome and its signaling biology, the results presented herein provide a useful resource for further hypothesis-driven exploration of Ehrlichia protein regulation by phosphorylation and glycosylation events. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD012589

Architecture and Evolution of Blade Assembly in β-propeller Lectins

International audienceLectins with a β-propeller fold bind glycans on the cell surface through multivalent binding sites and appropriate directionality. These proteins are formed by repeats of short domains, raising questions about evolutionary duplication. However, these repeats are difficult to detect in translated genomes and seldom correctly annotated in sequence databases. To address these issues, we defined the blade signature of the five types of β-propellers using 3D-structural data. With these templates, we predicted 3887 β-propeller lectins in 1889 species and organised this new information in a searchable online database. The data reveals a widespread distribution of β-propeller lectins across species. Prediction also emphasises multiple architectures and led to uncover a novel β-propeller assembly scenario. This was confirmed by producing and characterizing a predicted protein coded in the genome of Kordia zhangzhouensis. The crystal structure shows a new intermediate in the evolution of β-propeller assembly and demonstrates the power of our tool

swissPIT: a novel approach for pipelined analysis of mass spectrometry data

The identification and characterization of peptides from tandem mass spectrometry (MS/MS) data represents a critical aspect of proteomics. Today, tandem MS analysis is often performed by only using a single identification program achieving identification rates between 10-50% (Elias and Gygi, 2007). Beside the development of new analysis tools, recent publications describe also the pipelining of different search programs to increase the identification rate (Hartler et al., 2007; Keller et al., 2005). The Swiss Protein Identification Toolbox (swissPIT) follows this approach, but goes a step further by providing the user an expandable multi-tool platform capable of executing workflows to analyze tandem MS-based data. One of the major problems in proteomics is the absent of standardized workflows to analyze the produced data. This includes the pre-processing part as well as the final identification of peptides and proteins. The main idea of swissPIT is not only the usage of different identification tool in parallel, but also the meaningful concatenation of different identification strategies at the same time. The swissPIT is open source software but we also provide a user-friendly web platform, which demonstrates the capabilities of our software and which is available at http://swisspit.cscs.ch upon request for account. Contact: [email protected]

UniCarbKB: building a knowledge platform for glycoproteomics

The UniCarb KnowledgeBase (UniCarbKB; http://unicarbkb.org) offers public access to a growing, curated database of information on the glycan structures of glycoproteins. UniCarbKB is an international effort that aims to further our understanding of structures, pathways and networks involved in glycosylation and glyco-mediated processes by integrating structural, experimental and functional glycoscience information. This initiative builds upon the success of the glycan structure database GlycoSuiteDB, together with the informatic standards introduced by EUROCarbDB, to provide a high-quality and updated resource to support glycomics and glycoproteomics research. UniCarbKB provides comprehensive information concerning glycan structures, and published glycoprotein information including global and site-specific attachment information. For the first release over 890 references, 3740 glycan structure entries and 400 glycoproteins have been curated. Further, 598 protein glycosylation sites have been annotated with experimentally confirmed glycan structures from the literature. Among these are 35 glycoproteins, 502 structures and 60 publications previously not included in GlycoSuiteDB. This article provides an update on the transformation of GlycoSuiteDB (featured in previous NAR Database issues and hosted by ExPASy since 2009) to UniCarbKB and its integration with UniProtKB and GlycoMod. Here, we introduce a refactored database, supported by substantial new curated data collections and intuitive user-interfaces that improve database searchin

GlycoDigest: a tool for the targeted use of exoglycosidase digestions in glycan structure determination

Summary: Sequencing oligosaccharides by exoglycosidases, either sequentially or in an array format, is a powerful tool to unambiguously determine the structure of complex N- and O-link glycans. Here, we introduce GlycoDigest, a tool that simulates exoglycosidase digestion, based on controlled rules acquired from expert knowledge and experimental evidence available in GlycoBase. The tool allows the targeted design of glycosidase enzyme mixtures by allowing researchers to model the action of exoglycosidases, thereby validating and improving the efficiency and accuracy of glycan analysis. Availability and implementation: http://www.glycodigest.org. Contact: [email protected] or [email protected]