Omics for understanding microbial functional dynamics

Abstract

Omics for understanding microbial functional dynamics Janet K. Jansson 1 *, Josh D. Neufeld 2 , Mary Ann Moran 3 , Jack A. Gilbert 4,5 Lawrence Berkeley National Laboratory, Earth Sciences Division Berkeley, CA, USA. Department of Biology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada. Department of Marine Sciences, University of Georgia, Athens, GA 30602, USA. Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA. Department of Ecology and Evolution, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA. As a discipline, the field of microbial ecology has generally been limited by access to tools that have sufficient depth of resolution to enable exploration of complex microbial communities to determine ‘who is there?’ and ‘what they are doing?’. Until the mid 1980s most of our knowledge was based on the study of cultured microbial isolates or net enzymatic activities measured from laboratory enrich- ment experiments or environmental samples, without knowledge of the particular members of the community that were contributing to those functions. Since then, microbiologists have added a variety of molecular tools to our toolbox, allowing us to directly assess the identities and functions of microbial communities in a variety of environments without the necessity of prior cultivation. Most of the advances in molecular tools for assessing microbial community function have focused on the sequencing of nucleic acids. Sequencing of genomic or metagenomic DNA provides phylogenetic and functional gene information about specific organisms or communi- E-mail jrjansson@lbl.gov; Tel. (+1) 510 486 7487; Fax (+1) 510 486 7152. The submitted manuscript has been created in part by U. Chicago Argonne, LLC, Operator of Argonne National Laboratory (‘Argonne’). Argonne, a US Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02- 06CH11357. The US Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. ties respectively. These types of data have proven to be highly valuable for characterizing microbes that inhabit the various habitats on Earth, and importantly, their pos- sible roles in the function of that ecosystem. However, one should keep in mind that DNA sequence data only repre- sents the potential metabolic capacity of a system. In many environments, DNA that is sequenced may origi- nate from dead or dormant cells. Further, even actively growing microbes express only a fraction of their genes at any given time. For example, Escherichia coli genes are tightly regulated depending on, for example, the concen- tration of nutrients in their media (Tao et al., 1999). Only a third to a half of the genes in a given organism are expressed at any given time (Passalacqua et al., 2009). Methodology to address the longstanding dilemma of ‘which organisms are active?’ include the use of viability stains to first sort (e.g. with fluorescently activated cell sorting; FACS) and then sequence genomes from active populations, using stable isotopes to detect community members that are metabolically active (e.g. stable-isotope probing; SIP; Neufeld et al., 2007) and the use of bro- modeoxyuridine (BrdU), a thymidine analogue, to specifi- cally label DNA from growing cells that can then be isolated and sequenced (Edlund and Jansson, 2008; Mou et al., 2008). Alternatively, one can directly extract and sequence RNA to determine which microbes are active and which genes are transcribed. When applied to an entire microbial community, this analysis is referred to as ‘metatranscriptomics’. The metatranscriptomic approach has proven to be more challenging than metagenomics because RNA is less stable than DNA and because most of the RNA that is extracted is ribosomal RNA, usually representing over 90% of the total RNA (Urich et al., 2008). Ribosomal RNA sequences can inform about active community members due to a correlation between metabolic activity and ribosome abundance (Rehman et al., 2010), but do not reveal the genes and pathways expressed under a given set of conditions. In order to specifically focus on mRNA transcripts, several methods have been developed to enrich the mRNA fraction of the total RNA pool, usually by subtraction of the ribosomal RNA prior to sequencing (Fig. 1). The problem with loss of mRNA during the multiple processing steps is particularly