BackgroundSolventogenic 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.ResultsWe 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.ConclusionsMicrobial 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.

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.

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.

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.

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.