The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis

Lohr V, Haedicke O, Genzel Y, et al. The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis. BMC Biotechnology. 2014;14(1): 72.Background: In human vaccine manufacturing some pathogens such as Modified Vaccinia Virus Ankara, measles, mumps virus as well as influenza viruses are still produced on primary material derived from embryonated chicken eggs. Processes depending on primary cell culture, however, are difficult to adapt to modern vaccine production. Therefore, we derived previously a continuous suspension cell line, AGE1.CR.pIX, from muscovy duck and established chemically-defined media for virus propagation. Results: To better understand vaccine production processes, we developed a stoichiometric model of the central metabolism of AGE1.CR.pIX cells and applied flux variability and metabolic flux analysis. Results were compared to literature dealing with mammalian and insect cell culture metabolism focusing on the question whether cultured avian cells differ in metabolism. Qualitatively, the observed flux distribution of this avian cell line was similar to distributions found for mammalian cell lines (e.g. CHO, MDCK cells). In particular, glucose was catabolized inefficiently and glycolysis and TCA cycle seem to be only weakly connected. Conclusions: A distinguishing feature of the avian cell line is that glutaminolysis plays only a minor role in energy generation and production of precursors, resulting in low extracellular ammonia concentrations. This metabolic flux study is the first for a continuous avian cell line. It provides a basis for further metabolic analyses to exploit the biotechnological potential of avian and vertebrate cell lines and to develop specific optimized cell culture processes, e.g. vaccine production processes

Fluorescence based cell counting in collagen monolayer cultures of primary hepatocytes.

Accurate determination of cell number is essential for the quantitative description of biological processes. The changes should be related to a measurable reference e.g. in the case of cell culture, the viable cell number is a very valuable reference parameter. Indirect methods of cell number/viability measurements may have up to 10 % standard deviation. This can lead to undesirable large deviations in the analysis of "-omics" data as well as time course studies. Such data should be preferably normalized to the exact viable cell number at a given time to allow meaningful interpretation and understanding of the biological processes. Manual counting of cell number is very laborious and not possible in certain experimental setups. We therefore, developed a simple and reliable fluorescence based method with an accuracy of 95-98 % for the determination of the viable cell number in situ. We optimized the seeding cell densities for primary rat hepatocytes for optimal cell adhesion. This will help in efficient use of primary cells which are usually limited in availability. The method will be very useful in the application of "-omics" techniques, especially metabolome analysis where the specific rates of uptake/production of metabolites can be reliably calculated

Assessing group differences in biodiversity by simultaneously testing a user-defined selection of diversity indices

Comparing diversities between groups is a task biologists are frequently faced with, for example in ecological field trials or when dealing with metagenomics data. However, researchers often waver about which measure of diversity to choose as there is a multitude of approaches available. As Jost (2008, Molecular Ecology, 17, 4015) has pointed out, widely used measures such as the Shannon or Simpson index have undesirable properties which make them hard to compare and interpret. Many of the problems associated with the use of these ‘raw’ indices can be corrected by transforming them into ‘true’ diversity measures. We introduce a technique that allows the comparison of two or more groups of observations and simultaneously tests a user-defined selection of a number of ‘true’ diversity measures. This procedure yields multiplicity-adjusted P-values according to the method of Westfall and Young (1993, Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment, 49, 941), which ensures that the rate of false positives (type I error) does not rise when the number of groups and/or diversity indices is extended. Software is available in the R package ‘simboot’