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Lakshmanan M, Chia S, Pang KT, Sim LC, Teo G, Mak SY, Chen S, Lim HL, Lee AP, Bin Mahfut F, Ng SK, Yang Y, Soh A, Tan AHM, Choo A, Ho YS, Nguyen-Khuong T, Walsh I. Antibody glycan quality predicted from CHO cell culture media markers and machine learning. Comput Struct Biotechnol J 2024; 23:2497-2506. [PMID: 38966680 PMCID: PMC11222931 DOI: 10.1016/j.csbj.2024.05.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 07/06/2024] Open
Abstract
N-glycosylation can have a profound effect on the quality of mAb therapeutics. In biomanufacturing, one of the ways to influence N-glycosylation patterns is by altering the media used to grow mAb cell expression systems. Here, we explore the potential of machine learning (ML) to forecast the abundances of N-glycan types based on variables related to the growth media. The ML models exploit a dataset consisting of detailed glycomic characterisation of Anti-HER fed-batch bioreactor cell cultures measured daily under 12 different culture conditions, such as changes in levels of dissolved oxygen, pH, temperature, and the use of two different commercially available media. By performing spent media quantitation and subsequent calculation of pseudo cell consumption rates (termed media markers) as inputs to the ML model, we were able to demonstrate a small subset of media markers (18 selected out of 167 mass spectrometry peaks) in a Chinese Hamster Ovary (CHO) cell cultures are important to model N-glycan relative abundances (Regression - correlations between 0.80-0.92; Classification - AUC between 75.0-97.2). The performances suggest the ML models can infer N-glycan critical quality attributes from extracellular media as a proxy. Given its accuracy, we envisage its potential applications in biomaufactucuring, especially in areas of process development, downstream and upstream bioprocessing.
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Affiliation(s)
- Meiyappan Lakshmanan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology Madras, India
- Robert Bosch Centre for Data Science and AI (RBCDSAI), Indian Institute of Technology Madras, India
| | - Sean Chia
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Kuin Tian Pang
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Lyn Chiin Sim
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Gavin Teo
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Shi Ya Mak
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Shuwen Chen
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Hsueh Lee Lim
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Alison P. Lee
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Farouq Bin Mahfut
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Say Kong Ng
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Yuansheng Yang
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Annie Soh
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Andy Hee-Meng Tan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Andre Choo
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Ying Swan Ho
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Terry Nguyen-Khuong
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
| | - Ian Walsh
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A⁎STAR), 20 Biopolis Way, #06–01 Centros, Singapore 138668, Republic of Singapore
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Trbojević-Akmačić I, Lageveen-Kammeijer GSM, Heijs B, Petrović T, Deriš H, Wuhrer M, Lauc G. High-Throughput Glycomic Methods. Chem Rev 2022; 122:15865-15913. [PMID: 35797639 PMCID: PMC9614987 DOI: 10.1021/acs.chemrev.1c01031] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Glycomics aims to identify the structure and function of the glycome, the complete set of oligosaccharides (glycans), produced in a given cell or organism, as well as to identify genes and other factors that govern glycosylation. This challenging endeavor requires highly robust, sensitive, and potentially automatable analytical technologies for the analysis of hundreds or thousands of glycomes in a timely manner (termed high-throughput glycomics). This review provides a historic overview as well as highlights recent developments and challenges of glycomic profiling by the most prominent high-throughput glycomic approaches, with N-glycosylation analysis as the focal point. It describes the current state-of-the-art regarding levels of characterization and most widely used technologies, selected applications of high-throughput glycomics in deciphering glycosylation process in healthy and disease states, as well as future perspectives.
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Affiliation(s)
| | | | - Bram Heijs
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
| | - Tea Petrović
- Genos,
Glycoscience Research Laboratory, Borongajska cesta 83H, 10 000 Zagreb, Croatia
| | - Helena Deriš
- Genos,
Glycoscience Research Laboratory, Borongajska cesta 83H, 10 000 Zagreb, Croatia
| | - Manfred Wuhrer
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
| | - Gordan Lauc
- Genos,
Glycoscience Research Laboratory, Borongajska cesta 83H, 10 000 Zagreb, Croatia
- Faculty
of Pharmacy and Biochemistry, University
of Zagreb, A. Kovačića 1, 10 000 Zagreb, Croatia
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Lageveen-Kammeijer GSM, Rapp E, Chang D, Rudd PM, Kettner C, Zaia J. The minimum information required for a glycomics experiment (MIRAGE): reporting guidelines for capillary electrophoresis. Glycobiology 2022; 32:580-587. [DOI: 10.1093/glycob/cwac021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/21/2022] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
The Minimum Information Required for a Glycomics Experiment (MIRAGE) is an initiative to standardize the reporting of glycoanalytical methods and to assess their reproducibility. To date, the MIRAGE Commission has published several reporting guidelines that describe what information should be provided for sample preparation methods, mass spectrometry methods, liquid chromatography (LC) analysis, exoglycosidase digestions, glycan microarray methods and nuclear magnetic resonance methods. Here we present the first version of reporting guidelines for glyco(proteo)mics analysis by capillary electrophoresis (CE) for standardized and high-quality reporting of experimental conditions in the scientific literature. The guidelines cover all aspects of a glyco(proteo)mics CE experiment including sample preparation, CE operation mode (CZE, CGE, CEC, MEKC, cIEF, cITP), instrument configuration, capillary separation conditions, detection, data analysis, and experimental descriptors. These guidelines are linked to other MIRAGE guidelines and are freely available through the project website https://www.beilstein-institut.de/en/projects/mirage/guidelines/#ce_analysis (doi:10.3762/mirage.7).
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Affiliation(s)
| | - Erdmann Rapp
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany
- glyXera GmbH, Brenneckestrasse 20 – ZENIT, 39120, Magdeburg, Germany
| | - Deborah Chang
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University Medical Campus, 715 Albany Street, Boston, MA 02118, USA
| | - Pauline M Rudd
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, Centros, Singapore
| | - Carsten Kettner
- Beilstein-Institut, Trakehner Str. 7-9, 60487 Frankfurt am Main, Germany
| | - Joseph Zaia
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University Medical Campus, 715 Albany Street, Boston, MA 02118, USA
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Aoki-Kinoshita KF, Lisacek F, Karlsson N, Kolarich D, Packer NH. GlycoBioinformatics. Beilstein J Org Chem 2021; 17:2726-2728. [PMID: 34858527 PMCID: PMC8593694 DOI: 10.3762/bjoc.17.184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 10/27/2021] [Indexed: 11/23/2022] Open
Affiliation(s)
- Kiyoko F Aoki-Kinoshita
- Faculty of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji-shi, Tokyo, Japan
| | - Frédérique Lisacek
- University of Geneva and Swiss Institute of Bioinformatics, CUI - 7, route de Drize, 1211 Geneva, Switzerland
| | - Niclas Karlsson
- Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Box 440, 40530 Gothenburg, Sweden.,Faculty of Health Sciences, Department of Life Sciences and Health, Pharmacy, Oslo Metropolitan University, 0167 Oslo, Norway
| | - Daniel Kolarich
- Griffith University, Gold Coast Campus, Southport, Queensland 4222, Australia
| | - Nicolle H Packer
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales, Australia
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Pang KT, Tay SJ, Wan C, Walsh I, Choo MSF, Yang YS, Choo A, Ho YS, Nguyen-Khuong T. Semi-Automated Glycoproteomic Data Analysis of LC-MS Data Using GlycopeptideGraphMS in Process Development of Monoclonal Antibody Biologics. Front Chem 2021; 9:661406. [PMID: 34084765 PMCID: PMC8167043 DOI: 10.3389/fchem.2021.661406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 04/30/2021] [Indexed: 11/13/2022] Open
Abstract
The glycosylation of antibody-based proteins is vital in translating the right therapeutic outcomes of the patient. Despite this, significant infrastructure is required to analyse biologic glycosylation in various unit operations from biologic development, process development to QA/QC in bio-manufacturing. Simplified mass spectrometers offer ease of operation as well as the portability of method development across various operations. Furthermore, data analysis would need to have a degree of automation to relay information back to the manufacturing line. We set out to investigate the applicability of using a semiautomated data analysis workflow to investigate glycosylation in different biologic development test cases. The workflow involves data acquisition using a BioAccord LC-MS system with a data-analytical tool called GlycopeptideGraphMS along with Progenesis QI to semi-automate glycoproteomic characterisation and quantitation with a LC-MS1 dataset of a glycopeptides and peptides. Data analysis which involved identifying glycopeptides and their quantitative glycosylation was performed in 30 min with minimal user intervention. To demonstrate the effectiveness of the antibody and biologic glycopeptide assignment in various scenarios akin to biologic development activities, we demonstrate the effectiveness in the filtering of IgG1 and IgG2 subclasses from human serum IgG as well as innovator drugs trastuzumab and adalimumab and glycoforms by virtue of their glycosylation pattern. We demonstrate a high correlation between conventional released glycan analysis with fluorescent tagging and glycopeptide assignment derived from GraphMS. GraphMS workflow was then used to monitor the glycoform of our in-house trastuzumab biosimilar produced in fed-batch cultures. The demonstrated utility of GraphMS to semi-automate quantitation and qualitative identification of glycopeptides proves to be an easy data analysis method that can complement emerging multi-attribute monitoring (MAM) analytical toolsets in bioprocess environments.
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Affiliation(s)
- Kuin Tian Pang
- Bioprocessing Technology Institute, Agency for Science Technology and Research (ASTAR), Queenstown, Singapore
| | - Shi Jie Tay
- Bioprocessing Technology Institute, Agency for Science Technology and Research (ASTAR), Queenstown, Singapore
| | - Corrine Wan
- Bioprocessing Technology Institute, Agency for Science Technology and Research (ASTAR), Queenstown, Singapore
| | - Ian Walsh
- Bioprocessing Technology Institute, Agency for Science Technology and Research (ASTAR), Queenstown, Singapore
| | - Matthew S F Choo
- Bioprocessing Technology Institute, Agency for Science Technology and Research (ASTAR), Queenstown, Singapore
| | - Yuan Sheng Yang
- Bioprocessing Technology Institute, Agency for Science Technology and Research (ASTAR), Queenstown, Singapore
| | - Andre Choo
- Bioprocessing Technology Institute, Agency for Science Technology and Research (ASTAR), Queenstown, Singapore
| | - Ying Swan Ho
- Bioprocessing Technology Institute, Agency for Science Technology and Research (ASTAR), Queenstown, Singapore
| | - Terry Nguyen-Khuong
- Bioprocessing Technology Institute, Agency for Science Technology and Research (ASTAR), Queenstown, Singapore
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