PubMed · 2622356
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H Kaku, I J Goldstein. 1989. Snowdrop lectin.. https://doi.org/10.1016/0076-6879(89)79135-7
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Amino acid (AA) profiles from body fluids such as blood and urine are clinical indicators for diagnosing metabolic and hepatic diseases. Current quantitative methods, such as liquid chromatography-mass spectrometry (LC-MS) with isotopically labelled internal standards (ISs), are costly and technically demanding. This study proposes a cost-efficient alternative using structural isomers as ISs in a direct liquid infusion (DLI) tandem mass spectrometry (MS/MS) approach. The method leverages chimeric spectra and fragment intensity ratios to quantify AAs, demonstrating high linearity and precision even with a 3D ion trap mass analyser. This approach offers a viable strategy for AA quantification in preventive medicine, particularly for screening metabolic diseases such as phenylketonuria, diabetes, and liver dysfunction.
Elevated pCO(2) inhibits cell growth. This growth inhibition is accompanied by a decrease in intracellular pH (pHi), as well as a decrease in glycolysis. Elevated concentrations (mM) of some amino acids have been shown by others to protect cells exposed to two very different environmental stresses: nutrient starvation and hyperosmolality. The fact that many of the amino acids shown to have protective effects against other stresses are transported into the cell through a pHi-sensitive transporter led us to study the possibility of using these amino acids as protective agents under elevated pCO(2). Screening experiments using 5, 15, and 25 mM of each amino acid showed that not all amino acids that protect cells from hyperosmolality protect them from elevated pCO(2). Glycine betaine and glycine were chosen for further characterization in both hybridoma and CHO cells. Asparagine and threonine were also tested in hybridoma and CHO cells, respectively. All amino acids tested under 195 mm Hg pCO(2)/435 mOsm/kg (50% growth inhibition) restored the specific growth rate (mu) in hybridoma cells to that observed under control conditions (40 mm Hg/320 mOsm/kg). Addition of each amino acid resulted in an increase in the consumption rate and intracellular accumulation of that amino acid. In CHO cells, glycine betaine also restored mu to control values, while glycine and threonine partially restored mu. In hybridoma cells, the higher specific antibody productivity obtained at elevated pCO(2) was maintained with the lowest amino acid concentration (5 mM). Productivity decreased toward control values with increasing amino acid concentrations. Elevated pCO(2) decreased the specific tPA productivity in the CHO cell line studied. Only glycine betaine resulted in a 20% increase in productivity at 195 mm Hg/435 mOsm/kg. With the exception of glycine betaine in hybridoma cells, amino acids did not mitigate the associated pHi decrease of at least 0.2 pH units at 195 mm Hg/435 mOsm/kg. pHi in hybridoma cells under elevated pCO(2) in the presence of glycine betaine was about 0.1 pH units below that of control. Amino acids had no effect on the cell size response of hybridoma cells, while they partially offset the increase in CHO cell size at elevated pCO(2). Glycine betaine, asparagine, and glycine increased the specific glucose consumption rate observed at 195 mm Hg/435 mOsm/kg (50% of control) to values greater than 70% of control in hybridoma cells. In CHO cells, only glycine betaine increased q(glc) (by 20%) under elevated pCO(2). All amino acids tested improved the cell yield from glutamine at 195 mm Hg/435 mOsm/kg in both cell lines.
The application of the unscented Kalman filter to control starvation-induced programmed cell death-apoptosis-in Chinese hamster ovary cells was investigated. Neural network-based sensitivity analysis identified glutamine and asparagine as two major amino acids that play a key role in the suppression of apoptosis. Dynamic equations that accounted for the dependence of apoptotic cells on the concentrations of viable cells, glutamine, and asparagine were derived. These state equations were highly nonlinear and included nine state variables. An oxygen mass balance was written in the liquid phase. It served as the output equation for the unscented Kalman filter. Using the oxygen uptake rate as the observer, it was possible to estimate the states. A model predictive controller was then implemented once the apoptotic cells in the bioreactor approached a concentration of 1.5 x 10(4) cells/mL, taking into account the operating range of the flow cytometer and measurement error. The manipulated variables were the flow rates of glucose, glutamine, and asparagine. Simulation results showed that the controller was able to keep the apoptotic cells at a concentration of 1.5 x 10(4) cells/mL.