CBIOMES Project: Microbial Growth, Interactions and Biogeographies from ‘Omics’ Data
As part of the overall multi-institutional program that is aimed at basin-wide models of the distributions and activities of marine plankton, this single lab’s component includes three major aspects to support program goals. These are: (1) “Data mining” to retrieve information about the distributions, activities, and potential activities of a broad variety of marine microorganisms, in large part from gene sequence databases. We would work with data experts and modelers to optimize the use of these data as input towards the development of an ocean atlas of microbial identities and activities. (2) Work with other project investigators, including mathematicians and modelers, for development and improvement of models to use the quantitative data from the atlas and other environmental data sources to determine associations and potential interactions among and between different kinds of microbes. Such associations could include positive ones (organisms in effect working together) or antagonistic ones (such as competition, predation, parasitism or chemical antagonism). (3) Evaluation of a promising new approach to simultaneously determine the growth rates of many different kinds of microbes from the within-genome distributions of DNA extracted from mixed populations. Such growth rate information is extremely valuable when modeling the ecosystem. We expect that for some organisms at least, the information may be extracted from the billions of sequences generated by now-popular metagenomic studies, whereby the entire microbial community DNA is extracted, fragmented and sequenced as random pieces. This new approach requires determining which sequenced fragments belong in which organism and how they are ordered in the genome. We will test this method with laboratory cultures and manipulated field samples at known growth rates.
Jed Fuhrman (S.B., MIT, 1977; Ph.D., Scripps Institution of Oceanography, 1981) is the McCulloch Crosby Chair of Marine Biology at the University of Southern California. He is a Fellow of the American Association for the Advancement of Science, American Academy of Microbiology, Association for the Sciences of Limnology and Oceanography (from which he received the G. Evelyn Hutchinson Medal) and American Academy of Arts and Sciences. His research aims to improve our understanding of the structure and function of marine microbial systems, including viruses, bacteria, archaea and protists, through field measurements, laboratory studies, and modeling. His development (with Farooq Azam) of an isotope-based approach to measure in situ growth of marine bacteria via DNA synthesis showed these bacteria consume about half the marine primary production, ushering widespread recognition of the global importance of the “microbial loop.” His lab was the first to measure the impact of viral infection on marine bacteria and cyanobacteria, showing through observations, experiments and modeling that it has many implications for ecosystem function. His lab also discovered abundant marine archaea that are primary marine agents of nitrification. Initiating one of the longest marine microbial time series in 2000 (off Los Angeles, still running), his lab showed annually repeating and predictable patterns in microbial community composition and long-term stability of average composition, despite many short-term fluctuations, implying feedback controls (competition, cooperation, grazing, viral infection) that he aims to model. His collaboration with computational biologists to introduce the development and application of microbial association networks has begun to map the niche spaces and interactions among microbes and environmental factors. His lab’s recent development of a high-resolution “universal” rRNA sequencing pipeline (for prokaryotes and eukaryotes together) has facilitated that analysis, also furthered by new computational approaches he and his collaborators developed to better extract information about viral infection processes from metagenomic and metatranscriptomic data.