Linking bacterial identities and ecosystem processes: can 'omic' analyses be more than the sum of their parts?
A major goal in microbial ecology is to link specific microbial populations to environmental processes (e.g. biogeochemical transformations). The cultivation and characterization of isolates using genetic, biochemical and physiological tests provided direct links between organisms and their activities, but did not provide an understanding of the process networks in situ. Cultivation-independent molecular techniques have extended capabilities in this regard, and yet, for two decades, the focus has been on monitoring microbial community diversity and population dynamics by means of rRNA gene abundances or rRNA molecules. However, these approaches are not always well suited for establishing metabolic activity or microbial roles in ecosystem function. The current approaches, microbial community metagenomic and metatranscriptomic techniques, have been developed as other ways to study microbial assemblages, giving rise to exponentially increasing collections of information from numerous environments. This review considers some advantages and limitations of nucleic acid-based 'omic' approaches and discusses the potential for the integration of multiple molecular or computational techniques for a more effective assessment of links between specific microbial populations and ecosystem processes in situ. Establishing such connections will enhance the predictive power regarding ecosystem response to parameters or perturbations, and will bring us closer to integrating microbial data into ecosystem- and global-scale process measurements and models.
Morales
Sergio E
SE
Microbial Ecology Program, Division of Biological Sciences, The University of Montana, Missoula, MT 59812-1006, USA. sergio.morales@mso.umt.edu
Holben
William E
WE
D001419:Bacteria / Q000235:genetics* / Q000302:isolation & purification / Q000378:metabolism; D004463:Ecology / Q000379:methods* / Q000639:trends; D017753:Ecosystem; D004783:Environmental Microbiology; D056186:Metagenomics / Q000639:trends