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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.

Abstract Monoclonal antibodies (mAbs) eliminate cancer cells via various effector mechanisms including antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC), which are influenced by the N-glycan structures on the Fc region of mAbs. Manipulating these glycan structures on mAbs allows for optimization of therapeutic benefits associated with effector functions. Traditional approaches such as gene deletion or overexpression often lead to only all-or-nothing changes in gene expression and fail to modulate the expression of multiple genes at defined ratios and levels. In this work, we have developed a CHO cell engineering platform enabling modulation of multiple gene expression to tailor the N-glycan profiles of mAbs for enhanced effector functions. Our platform involves a CHO targeted integration platform with two independent landing pads, allowing expression of multiple genes at two pre-determined genomic sites. By combining with internal ribosome entry site (IRES)-based polycistronic vectors, we simultaneously modulated the expression of α-mannosidase II (MANII) and chimeric β-1,4-N-acetylglucosaminyl-transferase III (cGNTIII) genes in CHO cells. This strategy enabled the production of mAbs carrying N-glycans with various levels of bisecting and non-fucosylated structures. Importantly, these engineered mAbs exhibited different degrees of effector cell activation and CDC, facilitating the identification of mAbs with optimal effector functions. This platform was demonstrated as a powerful tool for producing antibody therapeutics with tailored effector functions via precise engineering of N-glycan profiles. It holds promise for advancing the field of metabolic engineering in mammalian cells.

Since its release in November 2022, ChatGPT and the related AI technology have disrupted multiple fields of society where people anticipate its pathways with a mixture of hope and fear. Among the affected fields, education, in particular, may incur one of the largest impacts in the future partly due to its nature of learning and teaching knowledge, an element that is more or less questioned by the rise of these technologies. As education can be seen as a component that determines the future of every other field of society, tools such as ChatGPT must be optimally regulated to enhance its gain or mitigate its loss. To contribute to this goal, this paper approaches the state of ChatGPT and its expected impacts on higher education through the lens of two major educational theories—John Dewey’s Reflective-Thought-and-Action model and revised Bloom’s taxonomy—aiming to propose possible evaluative criteria for the optimal usage of ChatGPT in academia. As ChatGPT is a relatively new topic of research yet a topic that requires an immediate focus due to its capabilities, this paper also aims to provide these criteria as one of the concrete starting points of future research in this field.

Abstract Stem cells possess the unique ability to differentiate into specialized cell types. These specialized cell types can be used for regenerative medicine purposes such as cell therapy. Myosatellite cells, also known as skeletal muscle stem cells (MuSCs), play important roles in the growth, repair, and regeneration of skeletal muscle tissues. However, despite its therapeutic potential, the successful differentiation, proliferation, and expansion processes of MuSCs remain a significant challenge due to a variety of factors. For example, the growth and differentiation of MuSCs can be greatly influenced by actively replicating the MuSCs microenvironment (known as the niche) using mechanical forces. However, the molecular role of mechanobiology in MuSC growth, proliferation, and differentiation for regenerative medicine is still poorly understood. In this present review, we comprehensively summarize, compare, and critically analyze how different mechanical cues shape stem cell growth, proliferation, differentiation, and their potential role in disease development (Fig. 1). The insights developed from the mechanobiology of stem cells will also contribute to how these applications can be used for regenerative purposes using MuSCs.

Elevated serum concentrations of leucine-rich α-2-glycoprotein (LRG1) have been reported in patients with inflammatory, autoimmune, and cardiovascular diseases. This study aims to investigate the role of LRG1 in endothelial activation. LRG1 in endothelial cells (ECs) of arteries and serum of patients with critical limb ischemia (CLI) was assessed by immunohistochemistry and ELISA, respectively. LRG1 expression in sheared and tumor necrosis factor-α (TNF-α)-treated ECs was analyzed. The mechanistic role of LRG1 in endothelial activation was studied in vitro. Plasma of 37-week-old Lrg1–/– mice was used to investigate causality between LRG1 and tumor necrosis factor receptor 1 (TNFR1) shedding. LRG1 was highly expressed in ECs of stenotic but not normal arteries. LRG1 concentrations in serum of patients with CLI were elevated compared to healthy controls. LRG1 expression was shear dependent. It could be induced by TNF-α, and the induction of its expression was mediated by NF-κB activation. LRG1 inhibited TNF-α-induced activation of NF-κB signaling, expression of VCAM-1 and ICAM-1, and monocyte capture, firm adhesion, and transendothelial migration. Mechanistically, LRG1 exerted its function by causing the shedding of TNFR1 via the ALK5-SMAD2 pathway and the subsequent activation of ADAM10. Consistent with this mechanism, LRG1 and sTNFR1 concentrations were correlated in the serum of CLI patients. Causality between LRG1 and TNFR1 shedding was established by showing that Lrg1–/–mice had lower plasma sTNFR1 concentrations than wild type mice. Our results demonstrate a novel role for LRG1 in endothelial activation and its potential therapeutic role in inflammatory diseases should be investigated further.

It is estimated that food fraud, where meat from different species is deceitfully labelled or contaminated, has cost the global food industry around USD 6.2 to USD 40 billion annually. To overcome this problem, novel and robust quantitative methods are needed to accurately characterise and profile meat samples. In this study, we use a glycomic approach for the profiling of meat from different species. This involves an O-glycan analysis using LC-MS qTOF, and an N-glycan analysis using a high-resolution non-targeted ultra-performance liquid chromatography-fluorescence-mass spectrometry (UPLC-FLR-MS) on chicken, pork, and beef meat samples. Our integrated glycomic approach reveals the distinct glycan profile of chicken, pork, and beef samples; glycosylation attributes such as fucosylation, sialylation, galactosylation, high mannose, α-galactose, Neu5Gc, and Neu5Ac are significantly different between meat from different species. The multi-attribute data consisting of the abundance of each O-glycan and N-glycan structure allows a clear separation between meat from different species through principal component analysis. Altogether, we have successfully demonstrated the use of a glycomics-based workflow to extract multi-attribute data from O-glycan and N-glycan analysis for meat profiling. This established glycoanalytical methodology could be extended to other high-value biotechnology industries for product authentication.

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.

Atherosclerosis, a chronic inflammatory disorder characterized by endothelial dysfunction and blood vessel narrowing, is the leading cause of cardiovascular diseases including heart attack and stroke. Herein, we present a novel tunable microfluidic atherosclerosis model to study vascular inflammation and leukocyte-endothelial interactions in 3D vessel stenosis. Flow and shear stress profiles were characterized in pneumatic-controlled stenosis conditions (0%, 50% and 80% constriction) using fluid simulation and experimental beads perfusion. Due to non-uniform fluid flow at the 3D stenosis, distinct monocyte (THP-1) adhesion patterns on inflamed [tumor necrosis factor-α (TNF-α) treated] endothelium were observed, and there was a differential endothelial expression of intercellular adhesion molecule-1 (ICAM-1) at the constriction region. Whole blood perfusion studies also showed increased leukocyte interactions (cell rolling and adherence) at the stenosis of healthy and inflamed endothelium, clearly highlighting the importance of vascular inflammation, flow disturbance, and vessel geometry in recapitulating atherogenic microenvironment. To demonstrate inflammatory risk assessment using leukocytes as functional biomarkers, we perfused whole blood samples into the developed microdevices (80% constriction) and observed significant dose-dependent effects of leukocyte adhesion in healthy and inflamed (TNF-α treated) blood samples. Taken together, the 3D stenosis chip facilitates quantitative study of hemodynamics and leukocyte-endothelial interactions, and can be further developed into a point-of-care blood profiling device for atherosclerosis and other vascular diseases.