Biologically Consistent Annotation of Metabolomics Data.

Annotation of metabolites remains a major challenge in liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics. The current gold standard for metabolite identification is to match the detected feature with an authentic standard analyzed on the same equipment and using the same method as the experimental samples. However, there are substantial practical challenges in applying this approach to large data sets. One widely used annotation approach is to search spectral libraries in reference databases for matching metabolites; however, this approach is limited by the incomplete coverage of these libraries. An alternative computational approach is to match the detected features to candidate chemical structures based on their mass and predicted fragmentation pattern. Unfortunately, both of these approaches can match multiple identities with a single feature. Another issue is that annotations from different tools often disagree. This paper presents a novel LC-MS data annotation method, termed Biologically Consistent Annotation (BioCAn), that combines the results from database searches and in silico fragmentation analyses and places these results into a relevant biological context for the sample as captured by a metabolic model. We demonstrate the utility of this approach through an analysis of CHO cell samples. The performance of BioCAn is evaluated against several currently available annotation tools, and the accuracy of BioCAn annotations is verified using high-purity analytical standards.

Unveiling gene trait relationship by cross-platform meta-analysis on Chinese hamster ovary cell transcriptome.

In the past few years, transcriptome analysis has been increasingly employed to better understand the physiology of Chinese hamster ovary (CHO) cells at a global level. As more transcriptome data accumulated, meta-analysis on data sets collected from various sources can potentially provide better insights on common properties of those cells. Here, we performed meta-analysis on transcriptome data of different CHO cell lines obtained using NimbleGen or Affymetrix microarray platforms. Hierarchical clustering, non-negative matrix factorization (NMF) analysis, and principal component analysis (PCA) accordantly showed the samples were clustered into two groups: one consists of adherent cells in serum-containing medium, and the other suspension cells in serum-free medium. Genes that were differentially expressed between the two clusters were enriched in a few functional classes by Database for Annotation, Visualization, and Integrated Discovery (DAVID) of which many were common with the enriched gene sets identified by Gene Set Enrichment Analysis (GSEA), including extracellular matrix (ECM) receptor interaction, cell adhesion molecules (CAMs), and lipid related metabolism pathways. Despite the heterogeneous sources of the cell samples, the adherent and suspension growth characteristics and serum-supplementation appear to be a dominant feature in the transcriptome. The results demonstrated that meta-analysis of transcriptome could uncover features in combined data sets that individual data set might not reveal. As transcriptome data sets accumulate over time, meta-analysis will become even more revealing. Biotechnol. Bioeng. 2017;114: 1583-1592. © 2017 Wiley Periodicals, Inc.

Using Metabolomics to Identify Cell Line-Independent Indicators of Growth Inhibition for Chinese Hamster Ovary Cell-based Bioprocesses.

Chinese hamster ovary (CHO) cells are widely used for the production of biopharmaceuticals. Efforts to improve productivity through medium design and feeding strategy optimization have focused on preventing the depletion of essential nutrients and managing the accumulation of lactate and ammonia. In addition to ammonia and lactate, many other metabolites accumulate in CHO cell cultures, although their effects remain largely unknown. Elucidating these effects has the potential to further improve the productivity of CHO cell-based bioprocesses. This study used untargeted metabolomics to identify metabolites that accumulate in fed-batch cultures of monoclonal antibody (mAb) producing CHO cells. The metabolomics experiments profiled six cell lines that are derived from two different hosts, produce different mAbs, and exhibit different growth profiles. Comparing the cell lines' metabolite profiles at different growth stages, we found a strong negative correlation between peak viable cell density (VCD) and a tryptophan metabolite, putatively identified as 5-hydroxyindoleacetaldehyde (5-HIAAld). Amino acid supplementation experiments showed strong growth inhibition of all cell lines by excess tryptophan, which correlated with the accumulation of 5-HIAAld in the culture medium. Prospectively, the approach presented in this study could be used to identify cell line- and host-independent metabolite markers for clone selection and bioprocess development.

Multi-Omics Reveals Impact of Cysteine Feed Concentration and Resulting Redox Imbalance on Cellular Energy Metabolism and Specific Productivity in CHO Cell Bioprocessing.

Chinese hamster ovary (CHO) cells are currently the primary host cell lines used in biotherapeutic manufacturing of monoclonal antibodies (mAbs) and other biopharmaceuticals. Cellular energy metabolism and endoplasmic reticulum (ER) stress are known to greatly impact cell growth, viability, and specific productivity of a biotherapeutic; but the molecular mechanisms are not fully understood. The authors previously employed multi-omics profiling to investigate the impact of a reduction in cysteine (Cys) feed concentration in a fed-batch process and found that disruption of the redox balance led to a substantial decline in cell viability and titer. Here, the multi-omics findings are expanded, and the impact redox imbalance has on ER stress, mitochondrial homeostasis, and lipid metabolism is explored. The reduced Cys feed activates the amino acid response (AAR), increases mitochondrial stress, and initiates gluconeogenesis. Multi-omics analysis reveals that together, ER stress and AAR signaling shift the cellular energy metabolism to rely primarily on anaplerotic reactions, consuming amino acids and producing lactate, to maintain energy generation. Furthermore, the pathways are demonstrated in which this shift in metabolism leads to a substantial decline in specific productivity and altered mAb glycosylation. Through this work, meaningful bioprocess markers and targets for genetic engineering are identified.

Multi-Omics Study on the Impact of Cysteine Feed Level on Cell Viability and mAb Production in a CHO Bioprocess.

There is continual demand to maximize CHO cell culture productivity of a biotherapeutic while maintaining product quality. In this study, a comprehensive multi-omics analysis is performed to investigate the cellular response to the level of dosing of the amino acid cysteine (Cys) in the production of a monoclonal antibody (mAb). When Cys feed levels are insufficient, there is a significant decrease in protein titer. Multi-omics (metabolomics and proteomics, with support from RNAseq) is performed over the time course of the CHO bioprocess producing an IgG1 mAb in 5 L bioreactors. Pathway analysis reveals that insufficient levels of Cys in the feed lead to Cys depletion in the cell. This depletion negatively impacts antioxidant molecules, such as glutathione (GSH) and taurine, leading to oxidative stress with multiple deleterious cellular effects. In this paper, the resultant ER stress and subsequent apoptosis that affects cell viability and viable cell density has been considered. Key metabolic enzymes and metabolites are identified that can be potentially monitored as the process progresses and/or increased in the cell either by nutrient feeding or genetic engineering. This work reinforces the centrality of redox balance to cellular health and success of the bioprocess as well as the power of multi-omics to provide an in-depth understanding of the CHO cell biology during biopharmaceutical production.

Debottlenecking protein secretion and reducing protein aggregation in the cellular host.

Chinese hamster ovary (CHO) cells have been extensively used for industrial production of biotherapeutics. With advances in cell line development and process optimization, production levels of therapeutic proteins using the CHO expression system have increased to beyond 10g per liter scale. These high-titer processes could challenge the secretory capacity of CHO cells, which can result in degradation and aggregation of the protein of interest. This review discusses bottlenecks in the secretory pathway of CHO cells that lead to inefficient secretion and aggregation of proteins, and summarizes current strategies to tackle these bottlenecks. In addition, emerging technologies that facilitate better understanding of cellular mechanisms in protein production could provide new avenues to improve the secretion and quality of protein therapeutics.

Lipidomics of CHO Cell Bioprocessing: Relation to Cell Growth and Specific Productivity of a Monoclonal Antibody.

As the demand for biological therapeutic proteins rises, there is an increasing need for robust and highly efficient bioprocesses, specifically, maximizing protein production by controlling the cellular nutritional and metabolic needs. A comprehensive lipidomics analysis has been performed, for the first time, over the time course of CHO cells producing an IgG1 monoclonal antibody (mAb) with fed batch 5 L bioreactors. The dynamic nature and importance of the CHO lipidome, especially on cellular growth and specific productivity, is demonstrated. A robust LC-MS method using positive and negative mode ESI was developed for lipid identification and quantitation of 377 unique lipids. The analysis revealed large changes in lipid features between the different days in bioprocessing including accumulation of triacylglycerol (TG) and lysophospholipid species with depletion of diacylglycerol (DG) species. Exploring pathway analysis where the lipid data was combined with polar metabolites and transcriptomics (RNA sequencing) revealed differences in lipid metabolism between the various stages of cellular growth and highlighted the role of key features of lipid metabolism on cell growth and specific productivity. The study demonstrates the importance of lipidomics in the expanding role of 'Omics methodologies in gaining insight into cellular behavior during protein production in a fed batch bioprocess.

In Vitro Pluripotent Stem Cell Differentiation to Hepatocyte Ceases Further Maturation at an Equivalent Stage of E15 in Mouse Embryonic Liver Development.

Hepatocyte-like cells (HLCs) can be derived from pluripotent stem cells (PSCs) by sequential treatment of chemical cues to mimic the microenvironment of embryonic liver development. However, these HLCs do not reach the full maturity level of primary hepatocytes. In this study, we carried out a meta-analysis of cross-species transcriptome data of in vitro differentiation of human PSCs to HLCs and in vivo mouse embryonic liver development to identify the developmental stage at which HLC maturation was blocked at. Systematic variations were found associated with the data source and removed by batch correction. Using principal component analysis, HLCs from different stages of differentiation were aligned with mouse embryonic liver development chronologically. A "unified developmental time" (DT) scale was developed after aligning in vitro HLC differentiation and in vivo embryonic liver development. HLCs were found to cease further maturation at an equivalent stage of mouse embryonic day (E)13-15. Genes with discordant time dynamics were identified by aligning in vivo and in vitro data set onto a common DT scale. These genes may be targets of genetic intervention for enhancing the maturity of PSC-derived HLCs.

Cell Expansion During Directed Differentiation of Stem Cells Toward the Hepatic Lineage.

The differentiation of human pluripotent stem cells toward the hepatocyte lineage can potentially provide an unlimited source of functional hepatocytes for transplantation and extracorporeal bioartificial liver applications. It is anticipated that the quantities of cells needed for these applications will be in the order of 109-1010 cells, because of the size of the liver. An ideal differentiation protocol would be to enable directed differentiation to the hepatocyte lineage with simultaneous cell expansion. We introduced a cell expansion stage after the commitment of human embryonic stem cells to the endodermal lineage, to allow for at least an eightfold increase in cell number, with continuation of cell maturation toward the hepatocyte lineage. The progressive changes in the transcriptome were measured by expression array, and the expression dynamics of certain lineage markers was measured by mass cytometry during the differentiation and expansion process. The findings revealed that while cells were expanding they were also capable of progressing in their differentiation toward the hepatocyte lineage. In addition, our transcriptome, protein and functional studies, including albumin secretion, drug-induced CYP450 expression and urea production, all indicated that the hepatocyte-like cells obtained with or without cell expansion are very similar. This method of simultaneous cell expansion and hepatocyte differentiation should facilitate obtaining large quantities of cells for liver cell applications.

Spheroid culture for enhanced differentiation of human embryonic stem cells to hepatocyte-like cells.

Stem cell-derived hepatocyte-like cells hold great potential for the treatment of liver disease and for drug toxicity screening. The success of these applications hinges on the generation of differentiated cells with high liver specific activities. Many protocols have been developed to guide human embryonic stem cells (hESCs) to differentiate to the hepatic lineage. Here we report cultivation of hESCs as three-dimensional aggregates that enhances their differentiation to hepatocyte-like cells. Differentiation was first carried out in monolayer culture for 20 days. Subsequently cells were allowed to self-aggregate into spheroids. Significantly higher expression of liver-specific transcripts and proteins, including Albumin, phosphoenolpyruvate carboxykinase, and asialoglycoprotein receptor 1 was observed. The differentiated phenotype was sustained for more than 2 weeks in the three-dimensional spheroid culture system, significantly longer than in monolayer culture. Cells in spheroids exhibit morphological and ultrastructural characteristics of primary hepatocytes by scanning and transmission electron microscopy in addition to mature functions, such as biliary excretion of metabolic products and cytochrome P450 activities. This three-dimensional spheroid culture system may be appropriate for generating high quality, functional hepatocyte-like cells from ESCs.

The road to regenerative liver therapies: the triumphs, trials and tribulations.

The liver is one of the few organs that possess a high capacity to regenerate after liver failure or liver damage. The parenchymal cells of the liver, hepatocytes, contribute to the majority of the regeneration process. Thus, hepatocyte transplantation presents an alternative method to treating liver damage. However, shortage of hepatocytes and difficulties in maintaining primary hepatocytes still remain key obstacles that researchers must overcome before hepatocyte transplantation can be used in clinical practice. The unique properties of pluripotent stem cells (PSCs) and induced pluripotent stem cells (iPSCs) have provided an alternative approach to generating enough functional hepatocytes for cellular therapy. In this review, we will present a brief overview on the current state of hepatocyte differentiation from PSCs and iPSCs. Studies of liver regenerative processes using different cell sources (adult liver stem cells, hepatoblasts, hepatic progenitor cells, etc.) will be described in detail as well as how this knowledge can be applied towards optimizing culture conditions for the maintenance and differentiation of these cells towards hepatocytes. As the outlook of stem cell-derived therapy begins to look more plausible, researchers will need to address the challenges we must overcome in order to translate stem cell research to clinical applications.

Stem cell culture engineering - process scale up and beyond.

Advances in stem cell research and recent work on clinical trials employing stem cells have heightened the prospect of stem cell applications in regenerative medicine. The eventual clinical application of stem cells will require transforming cell production from laboratory practices to robust processes. Most stem cell applications will require extensive ex vivo handling of cells, from isolation, cultivation, and directed differentiation to product cell separation, cell derivation, and final formulation. Some applications require large quantities of cells in each defined batch for clinical use in multiple patients; others may be for autologous use and require only small-scale operations. All share a common requirement: the production must be robust and generate cell products of consistent quality. Unlike the established manufacturing process of recombinant protein biologics, stem cell applications will likely see greater variability in their cell source and more fluctuations in product quality. Nevertheless, in devising stem cell-based bioprocesses, much insight could be gained from the manufacturing of biological materials, including recombinant proteins and anti-viral vaccines. The key to process robustness is thus not only the control of traditional process chemical and physical variables, but also the sustenance of cells in the desired potency or differentiation state through controlling non-traditional variables, such as signaling pathway modulators.

Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052.

BACKGROUND: Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications. RESULTS: We present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation--as experimentally observed. CONCLUSIONS: Microbial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. iCM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production.