Rhizosphere study
systems biology an
auspicious approach

Introduction: the rhizosphere and systems biology. Both complex: maybe perfect for each other?

The rhizosphere is complex. Really, really, ridiculously complex. Dauntingly complex even. Quantitatively now, soil has over 10000 distinct microbes in a single gram [7]. The term rhizosphere was coined to describe, literally, the sphere of influence of the plant root (rhiza): a definition which has also come to include not only the area around a root colonized by microorganisms, but also the parts of a root which contain microorganisms [9]. Notwithstanding the multifarious effects of soil chemistry, climate, and other non-biological factors, in the rhizosphere plants, fungi, and bacteria can all develop elaborate symbiotic interactions. The bulk of these obey a simple rule: plants are better at obtaining carbon, and fungi and bacteria are better at obtaining phosphorus, nitrogen, and potassium when these nutrients are scarce. As simple as that sounds, traditional techniques are inadequate to study the actual goings-on of the microbes of interest, what with the over 10000 distinct microbes found in cohabitation with them, particularly since most of the rhizosphere organisms are unculturable [7]. Novel approaches are needed to probe this complex environment, to find out what organisms are of interest, and to find out what they’re up to.

Like the rhizosphere, systems biology is inherently multifaceted. It is given such good treatment over at the SCQ [5, 6] that I include only the briefest of summaries here to help the faint of link1, and to expose those qualities that make it particularly well suited to the task at hand. Systems biology aims to take a holistic look at an entire system, avoiding the specialization that is a hallmark of modern science, aiming for a more interdisciplinary, even postmodern field of perspective. Powerful tools such as genomics, proteomics, metabolomics, metagenomics, and several others afford researchers new ways of looking at biological systems, and although there are many of these daunting-sounding techniques (with the exception of metabolomics, which sounds rather more funny than intimidating), the main idea is that rather than looking at a single unit of minutiae (a single active site on a single enzyme, say), an overarching understanding of an emergent system is sought through the use of high-throughput techniques, and the automated assimilation of the large resultant datasets.

The importance of understanding the rhizosphere

Traditionally the reasons for understanding the interactions in the rhizosphere have centered around agricultural applications, as rhizosphere composition has a profound effect on plant fitness [1], which is increasingly important as industrial agriculture looks for alternatives to the heavy application of pesticides and fertilizers. Beyond agricultural applications, biogeochemical cycling rests upon as yet unquantifiable plant-microbe interactions in the rhizosphere [12]. These in turn boil down to specific molecular events in and around the microbes [1]. Thus an understanding of the rhizosphere would not only allow agriculturalists to produce soil crops in a more balanced, sustainable manner, but a quantifiable understanding of cycling in the rhizosphere could contribute to the understanding of Earth’s overall biogeochemical cycling. This would be not only a coup for systems biology, but could very well end up helping quite a bit as we try to defuse global warming.

Systems biology: two examples of how new techniques could contribute to a holistic understanding of the rhizosphere

Is systems biology the correct approach for studying the rhizosphere? The haiku that precedes this text seems to indicate so, but I present some examples of aspects of systems biology that make it particularly well suited to the elucidation of the rhizosphere’s complex nature.

1. SIP, metagenomics

The unculturable nature of 99% of rhizosphere microbes [10, 12] makes culture-independent approaches such as those commonly used to gather data for bioinformatics studies very appropriate. For instance, the construction of metagenomic libraries can set the stage for genomic analysis of uncultured microbes in the rhizosphere [11]. Since the organisms of interest are those that are actively interacting in the rhizosphere, it does not make sense to gather data on every microbe present in an environmental sample. An elegant technique in development for obtaining DNA exclusively from organisms that are interacting in the rhizosphere is stable isotope probing (SIP)[8, 12]

SIP works as follows: Plants are exposed to 13CO2, which has a heavier carbon atom than regular CO2. This heavier carbon is metabolized by the plant, and deposited in the rhizosphere through rhizodeposition: the transfer by a plant of part of (>10% to 45%) its net C assimilation to the soil [Singh]. This heavier carbon will be used by microbes that ingest it (these are the microbes considered to be under the influence of the plant root) to construct nucleic acids, and thus the nucleic acids of microbes that are participating in the rhizosphere will be heavier, and can be separated from other nucleic acids using density gradient centrifugation [8], yielding the entire genome of all of the participating microbes in the rhizosphere [12].

Once these nucleic acids have been isolated, a metagenomic library can be produced using BAC vectors, as explained at the SCQ in [13]. This library is a valuable resource for performing genomic analyses of the active microbes in the rhizosphere, without having to culture them [8, 11, 12], which very well may not have worked anyhow, or if it did, produced misleading results [7].

2. Transcriptomics/proteomics/metabolomics: functional genomics

These three “-omics” are meant to characterize what is actually happening in a cell in a given environment, and thus will differentiate between a microbe participating in an interaction, and one that is not. Whereas a genome sequence gives a potential for certain tendencies, functional genomics go a step further, and examine what parts of the genome are transcribed (the transcriptome), how much protein is produced from the transcriptome (the proteome), and what this results in for the cell’s metabolism (the metabolome)

The transcriptome, then, is all of the mRNA transcripts in a given cell [8], and thus gives more information about a cell’s state than the genome: it gives a picture of what genes are being transcribed with a view to manufacturing proteins. The use of DNA microarrays is described at the SCQ in [3] An exciting development in the field of plant-microbe interactions is the production of a chip that contains cDNAs for both the host genome and the microbe genome, allowing for investigation of both gene expression patterns at once.

The proteomics approach aims to characterize proteins involved in symbiosis: the proteome is all of the proteins expressed in a cell [8]. An advantage that proteomics holds over transcriptomics is that it gives the quantity of each protein expressed, which mRNA levels will not indicate, particularly if post-translational modification occurs [2]. Proteins are separated by 2D electrophoresis, which consists of an isoelectric focusing step, followed by SDS-PAGE. This process separates proteins according to both pI and size, yielding a pattern where each “spot” represents a particular protein. Patterns can be compared to find proteins that are differentially expressed, and then these proteins can be characterized using mass spectrometry, to identify the protein of interest [2]. To add some systems biological flavour to the mix, it is now possible to perform a transcriptomic and proteomic analysis on the same sample [4].

Metabolomics is yet another step along the Central Dogma. Its bread and butter is the full complement of metabolites in a cell. These are the small molecular weight compounds active in metabolism. This type of study is considered to give the closest “snapshot” of what is functionally happening in a cell of all the –omics [8]. There are various levels of metabolomic study, ranging from target compound analysis which studies only one metabolite, to true metabolomics, which makes a qualitative and quantitative analyses of every metabolite in a biological system. The latter is not yet possible, so a variety of techniques are used to approximate it [8]. It is predicted that this technique will be an important part of a systems biology approach to plant-microbe interactions [14].

What about the rhizosphere’s make up suits it to systems biology?

As an unavoidably complex system to study, the rhizosphere is ideally suited to a systems approach. No single method is sufficient to describe the complex nature of the rhizosphere, and the multitude of interactions in the rhizosphere requires high throughput techniques in order that they can be elucidated in a reasonable timeframe. The techniques discussed here are close to being ready to contribute large amounts of data to this end.

The rhizosphere is composed of biological players whose study comes from different specializations, and in order to understand a rhizosphere interaction, investigators must track activity that crosses traditional disciplines. The study of this “interactome” [8] is therefore necessarily an interdisciplinary effort, one of the hallmarks of systems biology.

1. “faint of link” is a turn of phrase that I wish I had invented, but must attribute to Benjamin Cohen. As far as I can tell (read: I googled it) this was the sole use of this phrase in existence in the entire universe at the time of writing.

1. Barea, J. M., Pozo, M. J., Azcon, R. & Azcon-Aguilar, C. (2005). Microbial co-operation in the rhizosphere. J Exp Bot 56, 1761-1778.

2. Bestel-Corre, G., Dumas-Gaudot, E. & Gianinazzi, S. (2004). Proteomics as a tool to monitor plant-microbe endosymbioses in the rhizosphere. Mycorrhiza 14, 1-10.

3. Coe, B & Antler, C. (2004). Spot your genes – an overview of the microarray. The Science Creative Quarterly, 2. Retrieved Oct 15, 2006 from here.

4. Dumas-Gaudot, E., Amiour, N., Weidmann, S., Bestel-Corre, G., Valot, B., Lenogue, S., Gianinazzi-Pearson, V. & Gianinazzi, S. (2004). A technical trick for studying proteomics in parallel to transcriptomics in symbiotic root-fungus interactions. Proteomics 4, 451-453.

5. Fox, J. (2006). What is Bioinformatics? The Science Creative Quarterly, 2. Retrieved Oct 15, 2006 from here.

6. Jardon, M. (2006). Systems biology: an overview. The Science Creative Quarterly, 2. Retrieved Oct 15, 2006 from here.

7. Kent, A. D. & Triplett, E. W. (2002). Microbial communities and their interactions in soil and rhizosphere ecosystems. Annu Rev Microbiol 56, 211-236.

8. Kiely, P. D., Haynes, J. M., Higgins, C. H., Franks, A., Mark, G. L., Morrissey, J. P. & O’Gara, F. (2006). Exploiting new systems-based strategies to elucidate plant-bacterial interactions in the rhizosphere. Microb Ecol 51, 257-266.

9. Morgan, J. A., Bending, G. D. & White, P. J. (2005). Biological costs and benefits to plant-microbe interactions in the rhizosphere. J Exp Bot 56, 1729-1739.

10. Prosser, J. L. (2002). Molecular and functional diversity in soil microorganisms. Plant Soil 224, 9-17.

11. Rondon, M. R., August, P. R., Bettermann, A. D. & other authors (2000). Cloning the soil metagenome: a strategy for accessing the genetic and functional diversity of uncultured microorganisms. Appl Environ Microbiol 66, 2541-2547.

12. Singh, B. K., Millard, P., Whiteley, A. S. & Murrell, J. C. (2004). Unravelling rhizosphere-microbial interactions: opportunities and limitations. Trends Microbiol 12, 386-393.

13. Shelswell, K. J. (2006). Metagenomics: the science of biological diversity. The Science Creative Quarterly, 2. Retrieved Oct 15, 2006 from here.

14. Weckwerth, W. (2003). Metabolomics in systems biology. Annu Rev Plant Biol 54, 669-689.