Genome shuffling and ribosome engineering of Streptomyces virginiae for improved virginiamycin production.

The production of virginiamycin (VGM) from Streptomyces virginiae was improved by genome shuffling and ribosome engineering companied with a high-throughput screening method integrating deep-well cultivation and the cylinder-plate detecting. First, a novel high-throughput method was developed to rapidly screen large numbers of VGM-producing mutants. Then, the starting population of genome shuffling was obtained through ultraviolet (UV) and microwave mutagenesis, and four mutants with higher productivity of VGM were selected for genome shuffling. Next, the parent protoplasts were inactivated by UV and heat when a fusant probability was about 98%. Streptomycin resistance was used as an evolutionary pressure to extend positive effects on VGM synthesis. Finally, after five rounds of genome shuffling, a genetically stable strain G5-103 was obtained and characterized to be able to yield 251 mg/L VGM, which was 3.1- and 11.6-fold higher than that of the mutant strain UV 1150 and the wild-type strain, respectively.

Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem.

A new method for spike sorting is proposed which partly solves the overlapping problem. Principal component analysis and subtractive clustering techniques are used to estimate the number of neurons contributing to multi-unit recording. Spike templates (i.e. waveforms) are reconstructed according to the clustering results. A template-matching procedure is then performed. Firstly all temporally displaced templates are compared with the spike event to find the best-fitting template that yields the minimum residue variance. If the residue passes the chi(2)-test, the matching procedure stops and the spike event is classified as the best-fitting template. Otherwise the spike event may be an overlapping waveform. The procedure is then repeated with all possible combinations of two templates, three templates, etc. Once one combination is found, which yields the minimum residue variance among the combinations of the same number of component templates and makes the residue pass the chi(2)-test, the matching procedure stops. It is unnecessary to check the remaining combinations of more templates. Consequently, the computational effort is reduced and the over-fitting problem can be partly avoided. A simulated spike train was used to assess the performance of the proposed method, which was also applied to a real recording of chicken retina ganglion cells.