MALDI-TOF characterization of hGH1 produced by hairy root cultures of Brassica oleracea var. italica grown in an airlift with mesh bioreactor.

Expression systems based on plant cells, tissue, and organ cultures have been investigated as an alternative for production of human therapeutic proteins in bioreactors. In this work, hairy root cultures of Brassica oleracea var. italica (broccoli) were established in an airlift with mesh bioreactor to produce isoform 1 of the human growth hormone (hGH1) as a model therapeutic protein. The hGH1 cDNA was cloned into the pCAMBIA1105.1 binary vector to induce hairy roots in hypocotyls of broccoli plantlets via Agrobacterium rhizogenes. Most of the infected plantlets (90%) developed hairy roots when inoculated before the appearance of true leaves, and keeping the emerging roots attached to hypocotyl explants during transfer to solid Schenk and Hildebrandt medium. The incorporation of the cDNA into the hairy root genome was confirmed by PCR amplification from genomic DNA. The expression and structure of the transgenic hGH1 was assessed by ELISA, western blot, and MALDITOF-MS analysis of the purified protein extracted from the biomass of hairy roots cultivated in bioreactor for 24 days. Production of hGH1 was 5.1 ± 0.42 µg/g dry weight (DW) for flask cultures, and 7.8 ± 0.3 µg/g DW for bioreactor, with productivity of 0.68 ± 0.05 and 1.5 ± 0.06 µg/g DW*days, respectively, indicating that the production of hGH1 was not affected by the growth rate, but might be affected by the culture system. These results demonstrate that hairy root cultures of broccoli have potential as an alternative expression system for production of hGH1, and might also be useful for production of other therapeutic proteins.

Analysis of Electromyographic Signals from Rats' Stomaches for Detection and Classification of Motility.

This paper presents the analysis of the electromyographic signals from rat stomaches to identify and classify contractions. The results were validated with both visual identification and an ultrasonic system to guarantee the reference. Some parameters were defined and associated to the energy of the signal in frequency domain and grouped in a P vector. The parameters were statistically analyzed and according to the results, an artificial neuronal network was designed to use the P vectors as inputs to classify the electrical signals related to the contraction conditions. A first approach classification was performed with and without contraction classes (CR and NCR), then the same database were subdivided in four classes: with induced contraction (ICR), spontaneous contraction (SCR), without contraction due a post mortem condition (PMR) or under physiological conditions (PNCR). In a two-class classifier, performance was 86%, 93% and 91% of detections for each electrogastromyografic (EGMG) signal from each of three pairs of electrodes considered. Because in the four-class classifier, enough data was not collected for the first pair, then a three-class classifier with 82% of performance was used. For the other two EGMG signals electrode pairs, performance was of 76% and 86% respectively. Based in the results, the analysis of P vectors could be used as a contraction detector in motility studies due to different stimuli in a rat model.