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The performance of commercially available polymeric and microporous silica membranes (both supplied by Sulzer Chemtech GmbH) was investigated with respect to the dehydration of binary mixtures of isopropyl alcohol (IPA)/water. The range of concentrations investigated (CH2O ≤ 20 wt.%) was selected according to existing industrial needs. Fluxes and selectivities were monitored as dehydration proceeded, and the performance of both membranes has been compared. In addition, the effect of temperature on these parameters was investigated. Over this range, both water and IPA fluxes generally decreased with water concentration. The effect of temperature was investigated in the 70 to 105°C range, covering the pervaporation and vapour permeation operating conditions. Increasing the operating temperature resulted, as expected, in larger water fluxes for both polymeric and silica membranes. For the range of conditions investigated, water fluxes through the SMS membrane were found to be up to three times larger than those of the polymeric PERVAP 2510 membrane. However, IPA fluxes through the SMS membrane were also observed to be higher than those through a PERVAP 2510 membrane, resulting in a substantial decrease in separation performance.

Effective development of host cells for therapeutic protein production is hampered by the poor characterization of cellular transfection. Here, we employed a multi-omics-based systems biotechnology approach to elucidate the genotypic and phenotypic differences between a wild-type and recombinant antibody-producing Chinese hamster ovary (CHO) cell line. At the genomic level, we observed extensive rearrangements in specific targeted loci linked to transgene integration sites. Transcriptional re-wiring of DNA damage repair and cellular metabolism in the antibody producer, via changes in gene copy numbers, was also detected. Subsequent integration of transcriptomic data with a genome-scale metabolic model showed a substantial increase in energy metabolism in the antibody producer. Metabolomics, lipidomics, and glycomics analyses revealed an elevation in long-chain lipid species, potentially associated with protein transport and secretion requirements, and a surprising stability of N-glycosylation profiles between both cell lines. Overall, the proposed knowledge-based systems biotechnology framework can further accelerate mammalian cell-line engineering in a targeted manner.

The selection of suitable mammalian cell lines with high specific productivities is a crucial aspect of large-scale recombinant protein production. This study utilizes a metabolomics approach to elucidate the key characteristics of Chinese hamster ovary (CHO) cells with high monoclonal antibody productivities (q(mAb)). Liquid chromatography-mass spectrometry (LC-MS)-based intracellular metabolite profiles of eight single cell clones with high and low q(mAb) were obtained at the mid-exponential phase during shake flask batch cultures. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) subsequently revealed key differences between the high and low q(mAb) clones, as indicated by the variable importance for projection (VIP) scores. The mass peaks were further examined for their potential association with q(mAb) across all clones using Pearson's correlation analysis. Lastly, the identities of metabolites with high VIP and correlation scores were confirmed by comparison with standards through LC-MS-MS. A total of seven metabolites were identified-NADH, FAD, reduced and oxidized glutathione, and three activated sugar precursors. These metabolites are involved in key cellular pathways of citric acid cycle, oxidative phosphorylation, glutathione metabolism, and protein glycosylation. To our knowledge, this is the first study to identify metabolites that are associated closely with q(mAb). The results suggest that the high producers had elevated levels of specific metabolites to better regulate their redox status. This is likely to facilitate the generation of energy and activated sugar precursors to meet the demands of producing more glycosylated recombinant monoclonal antibodies.

A systematic computational framework is proposed for studying the underlying mechanisms of hyperosmotic conditions on GS-NS0 antibody production and to predict the optimal hyperosmotic induction time. Both IgG mRNA and polypeptide chain concentrations were positively related to the specific antibody productivity ( q Ab ) for normal and hyperosmotic conditions throughout. Hyperosmotic conditions resulted in 100% increase in specific IgG mRNA transcription rates; however, mRNA half-lives were 25% lower at both the mid-exponential and stationary phases. The IgG specific translation rates were higher (24%) at the mid-exponential phase for hyperosmotic cultures but were comparable in later phases. The main mechanism through which hyperosmotic conditions improve q Ab was concluded to be the heightened specific transcription rates. The predictive capability of the model was experimentally verified by identifying the optimal hyperosmotic induction time for biphasic GS-NS0 cultures at 72 h. The systematic approach that seamlessly combined experimentation and mathematical modelling, allowed both for the model based design of experiments that yielded valuable biological insight and for the prediction of the optimal hyperosmotic induction time. This framework enables “closing-the-loop” in mammalian cell bioprocess modelling by guiding experimentation through modelling.

A liquid chromatography–mass spectrometry (LC–MS) based metabolomics platform was previously established to identify and profile extracellular metabolites in culture media of mammalian cells. This presented an opportunity to isolate novel apoptosis-inducing metabolites accumulating in the media of antibody-producing Chinese hamster ovary (CHO mAb) fed-batch bioreactor cultures. Media from triplicate cultures were collected daily for the metabolomics analysis. Concurrently, cell pellets were obtained for determination of intracellular caspase activity. Metabolite profiles from the LC–MS data were subsequently examined for their degree of correlation with the caspase activity. A panel of extracellular metabolites, the majority of which were nucleotides/nucleosides and amino acid derivatives, exhibited good ( R 2 > 0.8) and reproducible correlation. Some of these metabolites, such as oxidized glutathione, AMP and GMP, were later shown to induce apoptosis when introduced to fresh CHO mAb cultures. Finally, metabolic engineering targets were proposed to potentially counter the harmful effects of these metabolites.

An intact-cell mass spectrometry (ICM) method using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) was evaluated for the screening of stable recombinant Chinese hamster ovary (CHO) cell lines, an important mammalian cell line in bioprocessing. With rapid and simple cell pretreatments, viabilities of cells could be rapidly distinguished on the different fingerprints of mass spectra. Detectable m/z values on cell surfaces and their relative intensities were processed by two biostatistical methods, principle components analysis (PCA) and partial least squares (PLS), with promising results. Discrimination among cell lines with different expressed recombinant proteins or different productivities could be achieved. The ICM method has the advantage of providing multiple parameters simultaneously and possesses the potential to become a powerful method for routine monitoring of bioprocesses.

A metabolomics-based approach was used to time profile extracellular metabolites in duplicate fed-batch bioreactor cultures of recombinant Chinese Hamster Ovary (CHO) cells producing monoclonal IgG antibody. Culture medium was collected and analysed using a high-performance liquid chromatography (HPLC) system in tandem with an LTQ-Orbitrap mass spectrometer. An in-house software was developed to pre-process the LC/MS data in terms of filtering and peak detection. This was followed by principal component analysis (PCA) to assess variance amongst the samples, and hierarchical clustering to categorize mass peaks by their time profiles. Finally, LC/MS2 experiments using the LTQ-Orbitrap (where standard was available) and SYNAPT HDMS (where standard was unavailable) were performed to confirm the identities of the metabolites. Two groups of identified metabolites were of particular interest; the first consisted of metabolites that began to accumulate when the culture entered stationary phase. The majority of them were amino acid derivatives and they were likely to be derived from the amino acids in the feed media. Examples included acetylphenylalanine and dimethylarginine which are known to be detrimental to cell growth. The second group of metabolites showed a downward trend as the culture progressed. Two of them were medium components--tryptophan and choline, and these became depleted midway into the culture despite the addition of feed media. The findings demonstrated the potential of utilizing metabolomics to guide medium design for fed-batch culture to potentially improve cell growth and product titer.

Inosine is a prevalent RNA modification in animals and is formed when an adenosine is deaminated by the ADAR family of enzymes. Traditionally, inosines are identified indirectly as variants from Illumina RNA-sequencing data because they are interpreted as guanosines by cellular machineries. However, this indirect method performs poorly in protein-coding regions where exons are typically short, in non-model organisms with sparsely annotated single-nucleotide polymorphisms, or in disease contexts where unknown DNA mutations are pervasive. Here, we show that Oxford Nanopore direct RNA sequencing can be used to identify inosine-containing sites in native transcriptomes with high accuracy. We trained convolutional neural network models to distinguish inosine from adenosine and guanosine, and to estimate the modification rate at each editing site. Furthermore, we demonstrated their utility on the transcriptomes of human, mouse and Xenopus. Our approach expands the toolkit for studying adenosine-to-inosine editing and can be further extended to investigate other RNA modifications. Nanyang Technological University National Medical Research Council (NMRC) National Research Foundation (NRF) M.H.T. is supported by a National Research Foundation Singapore grant (NRF2017-NRF-ISF002–2673), an Open Fund - Individual Research Grant from the National Medical Research Council (NMRC/OFIRG/0017/2016), an EMBO Global Investigatorship, an ASPIRE League seed grant from Nanyang Technological University, core funds from the Genome Institute of Singapore, and funds for Final Year Project (FYP) and the International Genetically Engineering Machine (iGEM) competition from the School of Chemical and Biomedical Engineering. J.W.J.H. is supported by a Ph.D. research scholarship from the School of Chemical and Biomedical Engineering. Y.S.H. is supported by core funds from the Bioprocessing Technology Institute. We also acknowledge the funding support for this project from Nanyang Technological University under the URECA Undergraduate Research Programme.