Glycosylation Patterns of Proteins Studied by Liquid Chromatography-Mass Spectrometry and Bioinformatic Tools

Due to their extensive structural heterogeneity, the elucidation of glycosylation patterns in glycoproteins such as the subunits of chorionic gonadotropin (CG), CG-alpha and CG-beta remains one of the most challenging problems in the proteomic analysis of posttranslational modifications. In consequence, glycosylation is usually studied after decomposition of the intact proteins to the proteolytic peptide level. However, by this approach all information about the combination of the different glycopeptides in the intact protein is lost. In this study we have, therefore, attempted to combine the results of glycan identification after tryptic digestion with molecular mass measurements on the intact glycoproteins. Despite the extremely high number of possible combinations of the glycans identified in the tryptic peptides by high-performance liquid chromatography-mass spectrometry (> 1000 for CG-alpha and > 10.000 for CG-beta), the mass spectra of intact CG-alpha and CG-beta revealed only a limited number of glycoforms present in CG preparations from pools of pregnancy urines. Peak annotations for CG-alpha were performed with the help of an algorithm that generates a database containing all possible modifications of the proteins (inclusive possible artificial modifications such as oxidation or truncation) and subsequent searches for combinations fitting the mass difference between the polypeptide backbone and the measured molecular masses. Fourteen different glycoforms of CG-alpha, including methionine-oxidized and N-terminally truncated forms, were readily identified. For CG-beta, however, the relatively high mass accuracy of ± 2 Da was still insufficient to unambiguously assign the possible combinations of posttranslational modifications. Finally, the mass spectrometric fingerprints of the intact molecules were shown to be very useful for the characterization of glycosylation patterns in different CG preparations

Analiza peptida iz hrane

The aim of this review is to discuss the definition of food peptidomics and highlight the role of this approach in food and nutrition sciences. Similar to living organisms, food peptidome may be defined as the whole peptide pool present in a food product or raw material. This definition also covers peptides obtained during technological processes and/or storage. The area of interest of food peptidomics covers research concerning the origin of peptidome, its dynamic changes during processing and/or storage, the influence of its presence, the composition and changes in the pool of peptides on the properties of food products or raw materials as well as the methods applied in research into this group of compounds. The area of interests of food peptidomics would include biological activity, functional properties, allergenicity, sensory properties and information on the product or resource authenticity and origin as well as its history and relationships. Research methods applied in food peptidomics, with special emphasis on computational methods, are also summarized.Ovaj revijalni prikaz razmatra definiciju pojma „food peptidomics“, tj. istraživanje peptida iz hrane i njihovu ulogu u prehrambenoj tehnologiji i nutricionizmu. Slično živim organizmima, „peptidome“ hrane obuhvaća sve peptide u prehrambenom proizvodu ili sirovini, a i one proizvedene tijekom prerade i/ili njihova skladištenja. Ova grana znanosti obuhvaća istraživanje podrijetla peptida, dinamičnosti njihove promjene pri preradi i/ili skladištenju, utjecaja peptida, njihova sastava i promjene sastava na svojstva prehrambenih proizvoda i sirovina, te metode istraživanja. To uključuje ispitivanje njihove biološke aktivnosti, funkcionalnih, alergenskih i senzorskih svojstava, informacije o autentičnosti i porijeklu proizvoda ili izvora, te povijesni razvoj i odnose. U radu su opisane metode istraživanja peptida, s posebnim naglaskom na računske metode

Analiza peptida iz hrane

The aim of this review is to discuss the definition of food peptidomics and highlight the role of this approach in food and nutrition sciences. Similar to living organisms, food peptidome may be defined as the whole peptide pool present in a food product or raw material. This definition also covers peptides obtained during technological processes and/or storage. The area of interest of food peptidomics covers research concerning the origin of peptidome, its dynamic changes during processing and/or storage, the influence of its presence, the composition and changes in the pool of peptides on the properties of food products or raw materials as well as the methods applied in research into this group of compounds. The area of interests of food peptidomics would include biological activity, functional properties, allergenicity, sensory properties and information on the product or resource authenticity and origin as well as its history and relationships. Research methods applied in food peptidomics, with special emphasis on computational methods, are also summarized.Ovaj revijalni prikaz razmatra definiciju pojma „food peptidomics“, tj. istraživanje peptida iz hrane i njihovu ulogu u prehrambenoj tehnologiji i nutricionizmu. Slično živim organizmima, „peptidome“ hrane obuhvaća sve peptide u prehrambenom proizvodu ili sirovini, a i one proizvedene tijekom prerade i/ili njihova skladištenja. Ova grana znanosti obuhvaća istraživanje podrijetla peptida, dinamičnosti njihove promjene pri preradi i/ili skladištenju, utjecaja peptida, njihova sastava i promjene sastava na svojstva prehrambenih proizvoda i sirovina, te metode istraživanja. To uključuje ispitivanje njihove biološke aktivnosti, funkcionalnih, alergenskih i senzorskih svojstava, informacije o autentičnosti i porijeklu proizvoda ili izvora, te povijesni razvoj i odnose. U radu su opisane metode istraživanja peptida, s posebnim naglaskom na računske metode

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma’s current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field

Analysis of glycosylation heterogeneity in antibody production by Pichia pastoris

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em BiotecnologiaThe aims of this M.Sc. thesis are the production of an antibody fragment in Pichia pastoris and to assess O-glycosylation heterogeneity theoretically and experimentally. All possible glycoforms (glycans that are attached to proteins) attached to this antibody were calculated and a simple mathematical model was developed in MATLAB to predict glycoforms heterogeneity. The production of Anti-(ED-B) scFv by Pichia pastoris was made in a 50 L fedbatch fermenter. The maximum product concentration obtained at approximately 100 hours of operation was 8,4mg/L. The Anti-(ED-B) scFv has no sequence of N-linked glycosylation and for the sequence of O-linked glycosylation there are 56 possible spots. Two mathematical models were developed in MATLAB: a deterministic and a stochastic model. Both models predict a given glycoforms distribution under the premise that the Endoplasmatic Reticulum enzymes and Golgi enzymes (O-Mannosyltranferase, α-1,2- Mannosyltransferase and β-1,2-Mannosyltransferase) are active. The models predict when the ratio of protein concentration per initial mannose concentration is low the prevalent glycoforms are protein with two mannoses. While when the ratio of protein concentration per initial mannose concentration is very low, the prevalent glycoforms are protein with five mannoses. For high number of mannose molecules at the beginning of the glycosylation process there is a convergence of results predicted by the deterministic and stochastic models. However, both models diverge when the number of mannose molecules is very low. In this situation, the stochastic predictions are more consistent with the true nature of the system than the deterministic ones. Despite of a theoretical O-linked glycoforms distribution, experimental test by Glycoprotein Detection Kit showed that the anti-(ED-B) scFv produced by Pichia pastoris is not glycosylated at the tested culture conditions. The posttranslational modifications that lead to the formation of monomers or dimers are not related to glycosylation but probably to the folding process. In future studies it would be interesting to study if the occurrence of Oglycosylation and its heterogeneity can be controlled by reactor operation parameters such as Temperature, pH and methanol feeding rate. It is known that such operational parameters have an important impact on the conformation of the scFv

Characterization of Metastasis-Associated Cell Surface Glycoproteins in Prostate Cancer

Prostate cancer (PCa) is a major health problem in males in the United States. Its lethality is mostly attributed to the primary tumor metastasizing to distant sites that are highly resistant to conventional therapies. Serum Prostate Specific Antigen (PSA) is the only protein biomarker used in clinic for prediction of prostate cancer recurrence following local therapies. Nonetheless, PSA lacks the ability to predict the behavior of an individual tumor in an individual patient. Therefore, development of reliable biomarkers for detection of metastatic potential in primary tumors, as well as discovery of new therapeutic targets, is in a great need for improved disease survival and management. Tumor metastasis is a multistep process involving extravasation of a cancer cell subsequent invasion and expression at a site distal to the primary tumors. Cell surface glycoproteins play pivotal roles as recognition molecules in a range of cell communication and adhesion events. Aberrant cell surface glycosylation has been reported in various cancers including PCa, and strongly correlated with prognosis and metastasis. However, the staggering complexity of glycans renders their analysis extraordinarily difficult. This research project aims to develop a mass spectrometry-based glycoproteomic approach for the selective isolation and identification of cell surface glycoproteins from cellular samples, and apply this technology to the discovery of new glycoprotein biomarkers which are indicative of prostate cancer progression and metastasis. To this end, cell surface glycosylation patterns were characterized by lectin flow cytometry and lectin cytochemistry on a human syngeneic PCa cell metastatic model, PC3 and its two variants with different metastatic potentials. It was found that metastatic potentials of PC3 variants were inversely correlated with cell surface α2-6 sialic acid levels. Targeted to cell surface sialoglycoproteins, a new glycoproteomic approach was successfully developed, which combined selective metabolic labeling of cell surface sialyl glycans, chemically probing the labeled sugar with a biotin tag, affinity purification of sialylated proteins, SDS-PAGE separation, and subsequent LC-MS/MS for protein identification. Application of this methodology in our prostate cancer model system resulted in unique identification of a total of 80 putative cell surface sialoglycoproteins differentially expressed between PC3 variants. After prioritization of the candidate biomarkers, one cell-based prioritized biomarker CUB-domain-containing protein 1 (CDCP1) was verified in prostate cancer cell lines and clinical samples, including tissues and body fluids, by immunoassays. Results indicated that expression of CDCP1 protein is dysregulated in prostate cancer and it has potential utility as a therapeutic target and a diagnostic marker for PCa progression. Overall, the data from this research project provided the proof-of-principle evidence for our targeted glycoproteomic approach, which we believe will help expedite the discovery of new cancer biomarkers and therapeutic targets in diseases and delineation of signal transduction pathways on a global scale

Post-translational modifications and mass spectrometry detection

In this review, we provide a comprehensive bibliographic overview of the role of mass spectrometry and the recent technical developments in the detection of post-translational modifications (PTMs). We briefly describe the principles of mass spectrometry for detecting PTMs and the protein and peptide enrichment strategies for PTM analysis, including phosphorylation, acetylation and oxidation. This review presents a bibliographic overview of the scientific achievements and the recent technical development in the detection of PTMs is provided. In order to ascertain the state of the art in mass spectrometry and proteomics methodologies for the study of PTMs, we analyzed all the PTM data introduced in the Universal Protein Resource (UniProt) and the literature published in the last three years. The evolution of curated data in UniProt for proteins annotated as being post-translationally modified is also analyzed. Additionally, we have undertaken a careful analysis of the research articles published in the years 2010 to 2012 reporting the detection of PTMs in biological samples by mass spectrometry. © 2013 Elsevier Inc

Ovarian cancer: can proteomics give new insights for therapy and diagnosis?

The study of the ovarian proteomic profile represents a new frontier in ovarian cancer research, since this approach is able to enlighten the wide variety of post-translational events (such as glycosylation and phosphorylation). Due to the possibility of analyzing thousands of proteins, which could be simultaneously altered, comparative proteomics represent a promising model of possible biomarker discovery for ovarian cancer detection and monitoring. Moreover, defining signaling pathways in ovarian cancer cells through proteomic analysis offers the opportunity to design novel drugs and to optimize the use of molecularly targeted agents against crucial and biologically active pathways. Proteomic techniques provide more information about different histological types of ovarian cancer, cell growth and progression, genes related to tumor microenvironment and specific molecular targets predictive of response to chemotherapy than sequencing or microarrays. Estimates of specificity with proteomics are less consistent, but suggest a new role for combinations of biomarkers in early ovarian cancer diagnosis, such as the OVA1 test. Finally, the definition of the proteomic profiles in ovarian cancer would be accurate and effective in identifying which pathways are differentially altered, defining the most effective therapeutic regimen and eventually improving health outcomes

Glycoproteomic and glycomic databases

Protein glycosylation serves critical roles in the cellular and biological processes of many organisms. Aberrant glycosylation has been associated with many illnesses such as hereditary and chronic diseases like cancer, cardiovascular diseases, neurological disorders, and immunological disorders. Emerging mass spectrometry (MS) technologies that enable the high-throughput identification of glycoproteins and glycans have accelerated the analysis and made possible the creation of dynamic and expanding databases. Although glycosylation-related databases have been established by many laboratories and institutions, they are not yet widely known in the community. Our study reviews 15 different publicly available databases and identifies their key elements so that users can identify the most applicable platform for their analytical needs. These databases include biological information on the experimentally identified glycans and glycopeptides from various cells and organisms such as human, rat, mouse, fly and zebrafish. The features of these databases - 7 for glycoproteomic data, 6 for glycomic data, and 2 for glycan binding proteins are summarized including the enrichment techniques that are used for glycoproteome and glycan identification. Furthermore databases such as Unipep, GlycoFly, GlycoFish recently established by our group are introduced. The unique features of each database, such as the analytical methods used and bioinformatical tools available are summarized. This information will be a valuable resource for the glycobiology community as it presents the analytical methods and glycosylation related databases together in one compendium. It will also represent a step towards the desired long term goal of integrating the different databases of glycosylation in order to characterize and categorize glycoproteins and glycans better for biomedical research