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

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary\ua0cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion.

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology

Elucidation of the CHO Super-Ome (CHO-SO) by Proteoinformatics

Chinese hamster ovary (CHO) cells are the preferred host cell line for manufacturing a variety of complex biotherapeutic drugs including monoclonal antibodies. We performed a proteomics and bioinformatics analysis on the spent medium from adherent CHO cells. Supernatant from CHO-K1 culture was collected and subjected to in-solution digestion followed by LC/LC–MS/MS analysis, which allowed the identification of 3281 different host cell proteins (HCPs). To functionally categorize them, we applied multiple bioinformatics tools to the proteins identified in our study including SignalP, TargetP, SecretomeP, TMHMM, WoLF PSORT, and Phobius. This analysis provided information on the presence of signal peptides, transmembrane domains, and cellular localization and showed that both secreted and intracellular proteins were constituents of the supernatant. Identified proteins were shown to be localized to the secretory pathway including ones playing roles in cell growth, proliferation, and folding as well as those involved in protein degradation and removal. After combining proteins predicted to be secreted or having a signal peptide, we identified 1015 proteins, which we termed as CHO supernatant-ome (CHO-SO), or superome. As a part of this effort, we created a publically accessible web-based tool called GO–CHO to functionally categorize proteins found in CHO-SO and identify enriched molecular functions, biological processes, and cellular components. We also used a tool to evaluate the immunogenicity potential of high-abundance HCPs. Among enriched functions were catalytic activity and structural constituents of the cytoskeleton. Various transport related biological processes, such as vesicle mediated transport, were found to be highly enriched. Extracellular space and vesicular exosome associated proteins were found to be the most enriched cellular components. The superome also contained proteins secreted from both classical and nonclassical secretory pathways. The work and database described in our study will enable the CHO community to rapidly identify high-abundance HCPs in their cultures and therefore help assess process and purification methods used in the production of biologic drugs