Fluorescent-protein co-expression to select CHO cells expressing high quantities of vaccine antigens.

Cell line development for production of vaccine antigens or therapeutic proteins typically involves transfection, selection, and enrichment for high-expressing cells. Enrichment methods include minipool enrichment, antibody-based enrichment, and enrichment based on co-expressed fluorescent biosensor proteins. However, these methods have limitations regarding labor and cost intensity, the generation of antibodies and assurance of their viral safety, and potential expression-interference or signal-saturation of the co-expressed fluorescent protein. To improve the method of fluorescent-protein co-expression, expression constructs were created that constitutively express a model vaccine antigen together with one of three fluorescent proteins having translation initiation controlled by a wildtype or mutant internal ribosome entry site (IRES), for a total of six constructs. The constructs were transfected into Chinese hamster ovary cells (CHO) cells, enriched for high fluorescence, cultured, and tested in a mini bioreactor to identify the most promising construct. The fluorescent protein, Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) with a mutant IRES performed best and was further tested with three additional vaccine antigens. Across the four vaccine antigens, the FUCCI fluorescent protein yielded productivity enhancements, without the need for generating an antibody and assuring its viral safety. Furthermore, FUCCI protein was present in negligible quantities in the cell supernatant, indicating a low risk for contaminating drug substances or vaccine antigen.

13C-metabolic flux analysis of Clostridium ljungdahlii illuminates its core metabolism under mixotrophic culture conditions.

Carbon dioxide-fixing acetogenic bacteria (acetogens) utilizing the Wood-Ljungdahl Pathway (WLP) play an important role in CO2 fixation in the biosphere and in the development of biological processes - alone or in cocultures, under both autotrophic and mixotrophic conditions - for production of chemicals and fuels. To date, limited work has been reported in experimentally validating and quantifying reaction fluxes of their core metabolic pathways. Here, the core metabolic model of the acetogen Clostridium ljungdahlii was interrogated using 13C-metabolic flux analysis (13C-MFA), which required the development of a new defined culture medium. Autotrophic, heterotrophic, and mixotrophic growth in defined medium was possible by adding 1 mM methionine to replace yeast extract. Our 13C-MFA found an incomplete TCA cycle and inactive core pathways/reactions, notably those of the oxidative pentose phosphate pathway, Entner-Doudoroff pathway, and malate dehydrogenase. 13C-MFA during mixotrophic growth using the parallel tracers [1-13C]fructose, [1,2-13C]fructose, [1,2,3-13C]fructose, and [U-13C]asparagine found that externally supplied CO2 contributed the majority of carbon consumed. All internally-produced CO2 from the catabolism of asparagine and fructose was consumed by the WLP. While glycolysis of fructose was active, it was not a major contributor to overall production of ATP, NADH, and acetyl-CoA. Gluconeogenic reactions were active despite the availability of organic carbon. Asparagine was catabolized equally via conversion to threonine and subsequent cleavage to produce acetaldehyde and glycine, and via deamination to fumarate and then the anaplerotic conversion of malate to pyruvate. Both pathways for asparagine catabolism produced acetyl-CoA, either directly via pyruvate or indirectly via the WLP. Cofactor stoichiometry based on our data predicted an essentially zero flux through the ferredoxin-dependent transhydrogenase (Nfn) reaction. Instead, nearly all of NADPH generated from the hydrogenase reaction was consumed by the WLP. Reduced ferredoxin produced by the hydrogenase reaction and glycolysis was mostly used for ATP generation via the RNF/ATPase system, with the remainder consumed by the WLP. NADH produced by RNF/ATPase was entirely consumed via the WLP.

Lung Nodule Malignancy Prediction From Longitudinal CT Scans With Siamese Convolutional Attention Networks.

Goal: We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure. Methods: Our proposed framework is evaluated for single- and multi-time-point classification to explore the value that temporal information, such as nodule growth, adds to malignancy prediction. Results: Our results show that the proposed method outperforms a comparable 3-D network with less than half the parameters on single-time-point classification and further achieves performance gains on multi-time-point classification. Conclusions: Attention-based, Siamese 2-D pre-trained CNNs lead to fast training times and are effective for malignancy prediction from single-time-point or multiple-time-point imaging data.