Biology subjects

Malinov, N.

Publications and source records attributed to Malinov, N..

3 recordsLinked to original sources

bioRxiv2026

Late-Stage Large Extracellular Vesicles Reprogram CHO Cell Metabolism in a Glutamine-Dependent Mode and Promote Antibody-Productivity to Cell-Growth Tradeoff

Extracellular vesicles (EVs) are mediators of intercellular communication, yet their impact on Chinese Hamster Ovary (CHO) cell physiology and bioprocess performance remains poorly understood. Here, we investigated whether small EVs (sEVs) and large EVs (LgEVs) that accumulate during fed-batch and perfusion cultures modulate CHO cell growth, metabolism, apoptosis, and monoclonal antibody (mAb) production. EVs isolated from early- and late-stage cultures were added to fresh CHO cultures grown with or without glutamine supplementation. Only LgEVs had a significant impact. Late-stage LgEVs markedly altered CHO-cell behavior, reducing cell proliferation, increasing apoptosis under glutamine-limited conditions, and substantially enhancing mAb productivity in a dose-dependent manner. Glutamine supplementation largely alleviated the growth-inhibitory and pro-apoptotic effects of LgEVs while preserving their positive impact on productivity, suggesting that glutamine decouples EV-mediated stress from productivity enhancement. Metabolic analyses revealed increased glucose consumption, a glutamine-dependent shift between glycine and alanine overflow metabolism, and remodeling of amino-acid utilization. Metabolic flux analysis further demonstrated enhanced glycolytic overflow and increased reliance on amino acid-supported anaplerosis. Conversely, selective removal of LgEVs from perfusion medium significantly improved cell expansion without reducing antibody production, supporting an inhibitory role for late-stage LgEVs. These LgEVs were enriched in let-7 family miRNAs and miR-21, consistent with RNAseq analyses demonstrating stress-associated enrichment of these miRNAs in CHO EVs and with functional studies showing that let-7a and miR-21reduce CHO-cell growth. Together, these observations suggest that selective miRNA loading contributes to the growth, metabolic, and productivity phenotypes elicited by late-stage LgEVs. Our findings identify LgEVs as endogenous regulators of CHO-cell physiology and potential targets for optimizing high-density fed-batch and perfusion biomanufacturing processes. HighlightsO_LIEndogenous late-stage Large Extracellular Vesicles (LgEVs) reduce CHO cell growth but boost specific mAb productivity. C_LIO_LIGlutamine supplementation rescues LgEV-mediated growth inhibition and apoptosis. C_LIO_LIMetabolic Flux Analysis (MFA) based on the dynamic behavior of amino acid and other metabolite and substrate concentrations reveals the pyruvate node as a metabolic bottleneck and the associated lactate overflow metabolism as resulting from LgEV exposure. C_LIO_LIStress-associated let-7 and miR-21 microRNAs are highly enriched on a per-EV basis in late-stage LgEVs. C_LIO_LISelective removal of LgEVs improves perfusion cell growth without impacting antibody titer. C_LI

Cell Biology↗

bioRxiv2026

A dynamic metabolic flux analysis (DMFA) model for performance predictions of diverse CHO cell culture process modes and conditions

Bioreactor pH can significantly affect Chinese Hamster Ovary (CHO) cell metabolism, thus impacting glycoprotein titers. However, there is very limited literature on incorporating pH in mathematical models for CHO cell metabolism. To address this limitation, guided by recently published experimental data, we have curated a stoichiometric network and formulated phenotype-driven kinetic expressions to develop a Dynamic Metabolic Flux Analysis (DMFA) model. The DMFA model incorporates Critical Process Parameters (CPPs), notably bioreactor pH, basal and feed media nutrient composition, feeding times, and inoculation cell densities to predict bioreactor performance: cell growth rates, antibody titers, and nutrient and metabolite profiles. The DMFA model was trained on diverse fed-batch data of the CHO VRC01 cell line to regress the kinetic parameters. The models utility was demonstrated through experimentally validated model predictions of CHO-cell performance in intensified fed-batch cultures, perfusion cultures, and cultures with different media. Experimentally validated predictions of a culture with high initial cell density and increased feed addition (intensified fed-batch culture) showed that mAb titers similar to fed-batch culture can be achieved with shorter culture durations. Similarly, experimentally validated predictions of perfusion bioreactor performance showed that coupling historical fed-batch data with computational tools can be leveraged to predict continuous biomanufacturing performance. We thus demonstrate that the developed mathematical model can simulate culture performance outside of the training data set. This supports the predictive robustness of the framework and provides a valuable tool for bioprocess development of diverse culture modes. HighlightsO_LIExperimentally measured fed-batch cell culture data was used to curate a reaction network. This reaction network was integrated with phenotypically driven kinetic expressions to yield a dynamic metabolic flux analysis (DMFA) model. C_LIO_LIThe DMFA model can predict bioprocess performance indicators such as concentration of viable cells, mAb, amino acids, glucose, lactate, and ammonia. C_LIO_LIThe model was developed to make these predictions under various process conditions such as bioreactor pH, media concentrations, feed supplementation schedule, and initial cell densities. C_LIO_LIPredicting and experimentally validating the impact of high initial cell density and increased feed media supplementation yielded in mAb titers similar to traditional fed-batch processes with much shorter culture durations. C_LIO_LIThe application of the DMFA model trained on data from a traditional fed-batch process to predict perfusion bioreactor culture performance was successfully demonstrated and experimentally verified. C_LIO_LIThe impact of AMBIC reference media on cell culture process performance was also predicted and experimentally validated. The predictions of amino acid metabolism yielded insights into improving the media. C_LI

bioengineering↗

bioRxiv2025

Pseudo perfusion of Chinese Hamster Ovary (CHO) cells as a reliable platform for data generation to model and guide continuous perfusion biomanufacturing

Chinese Hamster Ovary (CHO) cell monoclonal antibody (mAb) production in continuous perfusion has witnessed a renewed interest within the biopharmaceutical industry. Widespread implementation of perfusion biomanufacturing, however, remains hindered by long process development timelines and high costs. Use of predictive scale-down platforms to generate large informative metabolic datasets and guide process development decisions is critical to decreasing a molecules time to market. While scale-down platforms based on the pseudo perfusion concept have been previously reported, they have not been rigorously validated. They are often limited by oxygen transport or insufficient metabolic characterization, reducing their role to a preliminary screening tool. Here, we report the design and validation of a pseudo perfusion platform based on a phenotype-driven approach to ascertain that the process emulates continuous perfusion characteristics and is not oxygen limited. Beyond metabolic and cell size steady state, we show that our pseudo perfusion design enables cell cycle subpopulation and intracellular antibody expression steady state. We also demonstrate that pseudo perfusion robustly predicts amino acid demands in continuous perfusion bioreactors with exceptional linear correlation across a broad range of cell-specific perfusion rates (CSPRs). When coupling the pseudo perfusion platform developed here with a workflow for metabolic characterization, we significantly augment the dimensionality and reliability of data which can be generated at this scale to gain actionable insights towards perfusion process design, ultimately reducing process development timelines and the associated costs. HighlightsResidual lactate is a key proxy for oxygen transport in scale down platform design Novel flow cytometry workflow confirms cell cycle and intracellular steady state Pseudo perfusion robustly predicts metabolic phenotypes in continuous perfusion K-means clustering analysis of nutrient rates provides insight into media design

bioengineering↗