Ying Swan Ho

Combined In Silico Modeling and Metabolomics Analysis to Characterize Fed-Batch CHO Cell Culture

The increasing demand for recombinant therapeutic proteins highlights the need to constantly improve the efficiency and yield of these biopharmaceutical products from mammalian cells, which is fully achievable only through proper understanding of cellular functioning. Towards this end, the current study exploited a combined metabolomics and in silico modeling approach to gain a deeper insight into the cellular mechanisms of Chinese hamster ovary (CHO) fed‐batch cultures. Initially, extracellular and intracellular metabolite profiling analysis shortlisted key metabolites associated with cell growth limitation within the energy, glutathione, and glycerophospholipid pathways that have distinct changes at the exponential‐stationary transition phase of the cultures. In addition, biomass compositional analysis newly revealed different amino acid content in the CHO cells from other mammalian cells, indicating the significance of accurate protein composition data in metabolite balancing across required nutrient assimilation, metabolic utilization, and cell growth. Subsequent in silico modeling of CHO cells characterized internal metabolic behaviors attaining physiological changes during growth and non‐growth phases, thereby allowing us to explore relevant pathways to growth limitation and identify major growth‐limiting factors including the oxidative stress and depletion of lipid metabolites. Such key information on growth‐related mechanisms derived from the current approach can potentially guide the development of new strategies to enhance CHO culture performance. Biotechnol. Bioeng. 2012; 109:1415–1429.

Metabolomics-driven approach for the improvement of Chinese hamster ovary cell growth: Overexpression of malate dehydrogenase II

We have established a liquid chromatography-mass spectrometry based metabolomics platform to identify extracellular metabolites in the medium of recombinant Chinese hamster ovary (CHO) fed-batch reactor cultures. Amongst the extracellular metabolites identified, malate accumulation was the most significant. The contributing factors to malate efflux were found to be the supply of aspartate from the medium, and an enzymatic bottleneck at malate dehydrogenase II (MDH II) in the tricarboxylic acid cycle. Subsequent metabolic engineering to overexpress MDH II in CHO resulted in increases in intracellular ATP and NADH, and up to 1.9-fold improvement in integral viable cell number.

Metabolomics profiling of extracellular metabolites in recombinant Chinese Hamster Ovary fed-batch culture.

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.

LC-MS-based metabolic characterization of high monoclonal antibody-producing Chinese hamster ovary cells

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 (qmAb). Liquid chromatography‐mass spectrometry (LC‐MS)‐based intracellular metabolite profiles of eight single cell clones with high and low qmAb 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 qmAb clones, as indicated by the variable importance for projection (VIP) scores. The mass peaks were further examined for their potential association with qmAb across all clones using Pearsons 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 qmAb. 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. Biotechnol. Bioeng. 2012; 109: 3103–3111.

Metabolomics-based identification of apoptosis-inducing metabolites in recombinant fed-batch CHO culture media.

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 (R² > 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.

Development and application of microwave-assisted extraction technique in biological sample preparation for small molecule analysis

Microwave-assisted extraction (MAE) has emerged as an efficient extraction technique for various kinds of biological samples due to its low usage of extraction solvents and shorter extraction time. This review will focus on the recent developments and advantages of incorporating MAE in sample preparation protocols for the analysis of small molecules in plant, food and clinical samples in recent years. The operating principles of this technique and the key parameters influencing its extraction efficiency, including the nature of solvent, temperature, power and extraction time and their limitations are first mentioned. This is followed by a discussion on the advantages of applying MAE to extract organic contaminants in food for routine food safety analysis and active ingredients recovery. The successful application of MAE technique to recover bioactive compounds from plants in drug discovery studies and quality control purposes is then described. Additionally, the feasibility of using green solvents such as water, micelle and ionic liquids with MAE for plant metabolite profiling studies is evaluated and the associated challenges discussed. Finally, the application of MAE in clinical samples is highlighted. The use of MAE in this field is currently limited to the targeted detection of small molecules in human samples, due to a lack of knowledge of its effects on thermally labile metabolites. Consequently, the need for additional studies on how MAE impacts the recoveries of different metabolite classes in mammalian samples is discussed. The outcome of these studies can potentially broaden MAE applications in the clinical field.

Precursor mass prediction by clustering ionization products in LC-MS-based metabolomics

Liquid chromatography-mass spectrometry (LC-MS) is becoming the dominant technology in metabolomics, involving the comprehensive analysis of small molecules in biological systems. However, its use is still limited mainly by challenges in global high-throughput identification of metabolites: LC-MS data is highly complex, particularly due to the formation of multiple ionization products from individual metabolites. To address the limitation in metabolite identification, we developed a principled approach, designed to exploit the multi-dimensional information hidden in the data. The workflow first clusters candidate ionization products of the same metabolite together which typically have similar retention time, then searches for mass relationships among them in order to determine their ion types and metabolite identity. The robustness of our approach was demonstrated by its application to the LC-MS profiles of cell culture supernatant, which accurately predicted most of the known media components in the samples. Compared to conventional methods, our approach was able to generate significantly fewer candidate metabolites without missing out valid ones, thus reducing false-positive matches. Additionally, improved confidence in identification is achieved since each prediction comes with a probable combination of known ion types. Hence, our integrative workflow provides precursor mass predictions with high confidence by identifying various ionization products which account for a large proportion of detected peaks, thus minimizing false positives.

Lipidomic Profiling of Lung Pleural Effusion Identifies Unique Metabotype for EGFR Mutants in Non-Small Cell Lung Cancer

Cytology and histology forms the cornerstone for the diagnosis of non-small cell lung cancer (NSCLC) but obtaining sufficient tumour cells or tissue biopsies for these tests remains a challenge. We investigate the lipidome of lung pleural effusion (PE) for unique metabolic signatures to discriminate benign versus malignant PE and EGFR versus non-EGFR malignant subgroups to identify novel diagnostic markers that is independent of tumour cell availability. Using liquid chromatography mass spectrometry, we profiled the lipidomes of the PE of 30 benign and 41 malignant cases with or without EGFR mutation. Unsupervised principal component analysis revealed distinctive differences between the lipidomes of benign and malignant PE as well as between EGFR mutants and non-EGFR mutants. Docosapentaenoic acid and Docosahexaenoic acid gave superior sensitivity and specificity for detecting NSCLC when used singly. Additionally, several 20- and 22- carbon polyunsaturated fatty acids and phospholipid species were significantly elevated in the EGFR mutants compared to non-EGFR mutants. A 7-lipid panel showed great promise in the stratification of EGFR from non-EGFR malignant PE. Our data revealed novel lipid candidate markers in the non-cellular fraction of PE that holds potential to aid the diagnosis of benign, EGFR mutation positive and negative NSCLC.

An LC–MS-based lipidomics pre-processing framework underpins rapid hypothesis generation towards CHO systems biotechnology

IntroductionGiven a raw LC–MS dataset, it is often required to rapidly generate initial hypotheses, in conjunction with other ‘omics’ datasets, without time-consuming lipid verifications. Furthermore, for meta-analysis of many datasets, it may be impractical to conduct exhaustive confirmatory analyses. In other cases, samples for validation may be difficult to obtain, replicate or maintain. Thus, it is critical that the computational identification of lipids is of appropriate accuracy, coverage, and unbiased by a researcher’s experience and prior knowledge.ObjectivesWe aim to prescribe a systematic framework for lipid identifications, without usage of their characteristic retention-time by fully exploiting their underlying mass features.ResultsInitially, a hybrid technique, for deducing both common and distinctive daughter ions, is used to infer parent lipids from deconvoluted spectra. This is followed by parent confirmation using basic knowledge of their preferred product ions. Using the framework, we could achieve an accuracy of ~ 80% by correctly identified 101 species from 18 classes in Chinese hamster ovary (CHO) cells. The resulting inferences could explain the recombinant-producing capability of CHO-SH87 cells, compared to non-producing CHO-K1 cells. For comparison, a XCMS-based study of the same dataset, guided by a user’s ad-hoc knowledge, identified less than 60 species of 12 classes from thousands of possibilities.ConclusionWe describe a systematic LC–MS-based framework that identifies lipids for rapid hypothesis generation.