Washington, D.C. : United States. Dept. of Energy. ; Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2011
Book — 1 online resource.
Cultivation of oleaginous microalgae for fuel generally requires growth of the intended species to the maximum extent supported by available light. The presence of undesired competitors, pathogens and grazers in cultivation systems will create competition for nitrate, phosphate, sulfate, iron and other micronutrients in the growth medium and potentially decrease microalgal triglyceride production by limiting microalgal health or cell density. Pathogenic bacteria may also directly impact the metabolism or survival of individual microalgal cells. Conversely, symbiotic bacteria that enhance microalgal growth may also be present in the system. Finally, the use of agricultural and municipal wastes as nutrient inputs for microalgal production systems may lead to the introduction and proliferation of human pathogens or interfere with the growth of bacteria with beneficial effects on system performance. These considerations underscore the need to understand bacterial community dynamics in microalgal production systems in order to assess microbiome effects on microalgal productivity and pathogen risks. Here we focus on the bacterial component of microalgal production systems and describe a pipeline for metagenomic characterization of bacterial diversity in industrial cultures of an oleaginous alga, Nannochloropsis salina. Environmental DNA was isolated from 12 marine algal cultures grown at Solix Biofuels, a region of the 16S rRNA gene was amplified by PCR, and 16S amplicons were sequenced using a 454 automated pyrosequencer. The approximately 70,000 sequences that passed quality control clustered into 53,950 unique sequences. The majority of sequences belonged to thirteen phyla. At the genus level, sequences from all samples represented 169 different genera. About 52.94% of all sequences could not be identified at the genus level and were classified at the next highest possible resolution level. Of all sequences, 79.92% corresponded to 169 genera and 70 other taxa. We apply a principal component analysis across the initial sample set to draw correlations between sample variables and changes in microbiome populations.