Ionomics
From Wikipedia, the free encyclopedia
The ionome is the mineral nutrient and trace element composition of an organism, representing the inorganic component of cellular and organismal systems. Ionomics, the study of the ionome, requires application of high-throughput elemental analysis technologies, and their integration with bioinformatic and genetic tools. Ionomics has the ability to capture information about the functional state of an organism under different conditions, driven by genetic and developmental differences, and by biotic and abiotic factors. The relatively high throughput and low cost of ionomic analysis means that it has the potential to provide a powerful approach to not only the functional analysis of the genes and gene networks that directly control the ionome, but also to the more extended gene networks controlling developmental and physiological processes that indirectly affect the ionome.
Contents |
[edit] Concepts of the Ionome and Ionomics
The term ionome was defined as all the mineral nutrient (dietary minerals) and trace elements found in an organism, and originally investigated in the plant Arabidopsis thaliana (Thale cress)(1). This definition extended the previously used term metallome (2,3) to include biologically significant nonmetals (4). The ionome also includes both essential and non essential elements. The concept of the ionome has also been applied to Saccharomyces cerevisiae (yeast) where the mineral nutrient and trace element profile of 4,385 mutant strains from the Saccharomyces Genome Deletion collection has been quantified (5). The ionome can be thought of as the inorganic subset of the metabolome, and the study of the ionome, called ionomics, is defined as the;
Quantitative and simultaneous measurement of the elemental composition of living organisms, and changes in this composition in response to physiological stimuli, developmental state and genetic modifications.
This definition captures and highlights several critical concepts in the study of the ionome. Firstly, the study of the ionome is predicated on the fact that its study should provide a snapshot of the functional status of a complex biological organism, and this information is held in both the quantitative and qualitative patterns of mineral nutrients and trace elements in the organisms various tissues and cells. Such a concept rests heavily on the early work of Pauling and Robinson in which they developed the notion that a quantitative metabolite profile can be indicative of a particular physiological or disease state (6). To capture this information contained in the ionome the precise and simultaneous quantification of as many of the components of the ionome as possible is necessary. Secondly, the power of ionomics lies in its ability to precisely capture information about the functional state of an organism under different conditions. These conditions may either be driven by genetic differences, developmental differences or by biotic and abiotic factors.
The underlying cause of an alteration in the ionome may either be direct or indirect. For example, alterations in the mineral nutrient levels in the diet or the loss of function of an important ion transporter would be expected to directly affect the ionome. Whereas alterations in cell wall structure or acidification of the apoplast in plants, for example, might be expected to indirectly affect the ionome. Ionomics has the potential to provide a powerful and relatively low cost approach to not only the functional analysis of the genes and gene networks that directly control it, but also to allow analysis of the more extended gene networks controlling developmental and physiological processes that indirectly affect the ionome.
[edit] Analytical Technology Required for Ionomics
To achieve the key analytical requirements of ionomics, that is the quantitative and simultaneous measurement of the elemental composition of living organisms, requires choosing specialized instrumentation and sample preparation protocols based on various selection criteria. These criteria include sample throughput, dynamic quantification range, sensitivity, elements to be measured, sample size available, reliability, cost, portability and the need to measure the ionome in either a bulk sample or with either low spatial resolution (1 – 10 mm), or high spatial resolution (10 – 100 µm) in either 2 or 3 dimensions. It is also worth noting that because most ionomic analyses are generally comparative, for example did the ionome change when gene X was deleted, what is important analytically is precision and not accuracy. Precision is critical if you want to establish that an observed alteration in the ionome is due to the perturbation the experimenter applied to the system rather than uncontrolled analytical or environmental error. High accuracy in ionomics is only required if the experimenter wants to make conclusive statements about the absolute concentration of particular elements in the ionome. For example, “the minimal quota for this element is 2 x 105 atoms of zinc per cell” (3). The need for precision, accuracy or both has numerous implications for the analytical methodology chosen to perform ionomics.
Inductively-Coupled Plasma Optical Emission Spectroscopy (ICP-OES) or Inductively-Coupled Plasma Mass Spectrometry (ICP-MS) can both be effectively used for ionomics. ICP-OES has the advantage of lower cost and simplicity, whereas ICP-MS has an edge in sensitivity and the ability to detect different isotopes of the same element. Although ICP-OES is less sensitive than ICP-MS, some of this sensitivity is won back by the robustness of ICP-OES in more concentrated sample matrices. While ICP-MS struggles with sample matrices with greater than about 0.1% solids, ICP-OES can handle up to about 3% dissolved solids. Both ICP-OES and ICP-MS have been used successfully for large-scale ionomics projects, in yeast (5) which used ICP-OES to measure approximately 10,000 samples over 2-years, and Arabidopsis (1,7,8) which used ICP-MS to measure approximately 80,000 samples between 2001 – 2007.
In the early 90’s, before the ionome or ionomics had been defined, Delhaize and coworkers applied X-Ray Fluorescence (XRF) for the successful multi-element screening of over 100,000 mutagenized Arabidopsis seedlings for the identification of mutants with altered ionomes (9). XRF has also been applied to seed ionomics with the recent use of synchrotron-based microXRF as a rapid screening tool for the possible identification of Arabidopsis seeds with mutant ionomic phenotypes (10).
The use of Neutron activation analysis (NAA) for elemental analysis of biological samples goes back over forty years. NAA has been used to perform multielement quantification on plant samples collected within and across broad phylogenetic groupings, for the identification of trends in mineral nutrient and trace element accumulation in plants across taxa (11), (12). NAA has also been applied to perform ionomics in the study of breast cancer (13), (14), colorectal cancer (15,16) and brain cancer (17), studies where the ionome was shown to be perturbed in the diseased tissues or organism. However, NAA has not yet been used extensively as a high-throughput elemental analysis tool for ionomics.
[edit] Bioinformatics of Ionomics
In any large-scale ionomics project, where many hundreds or thousands of samples are to be analyzed over an extended period of time, it will be critical to implement an information management system to control all aspects of the process. This will include the management of sample acquisition, sample harvesting, sample preparation, elemental analysis and data processing. Such workflow tools will allow, for example, scheduling and tracking of samples for analysis. Critically, workflow tools also provide for the logical organization of the workflow providing a logical framework for the capture of contextual information (metadata, e.g. genotype of sample, culture conditions, date sampled etc) necessary to fully describe the experiment.
The preprocessing of elemental profile, or ionomic data, is a critical step in the ionomic workflow before data can be analyzed for the extraction of knowledge. Because such data preprocessing is best done by the analyst that collected the data, tools to accomplish such data preprocessing need to be incorporated into the work flow at the stage that the analyst interacts with the information management system. For an information management systems to be useful it must also provide tools that allow for the retrieval, display and download of the ionomics data, and associated metadata, which it stores. The Purdue Ionomics Information Management System (PiiMS) is a working example of such an integrated information management system (18). It stores publicly available ionomic data on over 80,000 Arabidopsis thaliana samples and can be accessed at [1]. Ionomic data on approximately 10,000 yeast samples is also stored at the PlantsT database (19), which can be accessed at [2].
[edit] Applications of Ionomics
A central theme of ionomics is the study of changes in the ionome in response to “physiological stimuli, developmental state and genetic modifications”. It is in this context that we will discuss the application of ionomics to the discovery of gene function (functional genomics), and its application for the assessment of the physiological status of plants.
[edit] Functional Genomics
With genotyping, including sequencing and polymorphisms identification, rapidly becoming routine, it is the identification of phenotypic variation, and its association with genotypic variation, that is limiting the leveraging of genomic information for knowledge generation. As a high-throughput phenotyping platform, ionomics offer the possibility of rapidly generating large ionomics data sets on many thousands of individual samples. Utilization of such a phenotyping platform to screen mapping populations, with available modern genetic tools, provides a very powerful approach for the identification of genes and gene networks that regulate the ionome.
[edit] Assessment of physiological status
Given that the ionome of an organism is controlled by a summation of multiple physiological processes, alterations in any of which could potentially affect the ionome. Because of this, the ionome is likely to be very sensitive to the physiological state of an organism, with different ionomic profiles being reflective of different physiological states. Such characteristic ionomic profiles, if they exist, could be useful as biomarkers for the particular physiological condition with which they are associated.
[edit] Conclusion
With the $1000 genome sequence a rapidly approaching reality, high-throughput phenotyping platforms are going to be critical for the association of genotype with phenotype, for the process of gene discovery. Here we have discussed the development and application of ionomics as a high-throughput phenotyping platform, with the capacity to analyze approximately 1000 samples/week with a single analytical instrument. Because the ionome is the summation of many biological processes, a high-throughput ionomics platform offers a viable system for probing the multiple physiological and biochemical activities that affect the ionome, in tens of thousands of individuals. Ionomics, in combination with other phenotyping platforms such as transcript profiling (gene array), proteomics and metabolomics, therefore offer the potential to close the growing gap between our knowledge of genotype and the phenotypes it controls.
[edit] Literature Cited
1. Lahner B, Gong J, Mahmoudian M, Smith EL, Abid KB, Rogers EE, Guerinot ML, Harper JF, Ward JM, McIntyre L, Schroeder JI, Salt DE (2003) Genomic scale profiling of nutrient and trace elements in Arabidopsis thaliana. Nat Biotechnol 21: 1215-1221. [3]
2. Williams RJP (2001) Chemical selection of elements by cells. Coordination Chemistry Reviews 216-217: 583-595.
3. Outten CE, O’Halloran TV (2001) Femtomolar sensitivity of metalloregulatory proteins controlling zinc homeostasis. Science 292: 2488-2492. [4]
4. Salt DE (2004) Update on ionomics. Plant Physiology 136: 2451-2456. [5]
5. Eide DJ, Clark S, Nair TM, Gehl M, Gribskov M, Guerinot ML, Harper JF (2005). Characterization of the yeast ionome: a genome-wide analysis of nutrient mineral and trace element homeostasis in Saccharomyces cerevisiae. Genome Biol 6:R77. [6]
6. Robinson AB, Pauling L (1974) Techniques of orthomolecular diagnosis. Clin Chem 20: 961-965. [7]
7. Rus A, Baxter I, Muthukumar B, Gustin J, Lahner B, Yakubova E and Salt DE (2006) Natural variants of AtHKT1 enhance Na+ accumulation in two wild populations of Arabidopsis. PLoS Genet 2(12): e210. [8]
8. Baxter I, Muthukumar B, Park HC, Buchner P, Lahner B, Danku J, Zhao K, Lee J, Hawkesford MJ, Guerinot ML, Salt DE (2008) Variation in Molybdenum Content Across Broadly Distributed Populations of Arabidopsis thaliana is Controlled By a Mitochondrial Molybdenum Transporter (MOT1). PLoS Genet 4(2): e1000004. [9]
9. Delhaize E, Randall PJ, Wallace PA, Pinkerton A (1993) Screening Arabidopsis for mutants in mineral nutrition. Plant Soil 155/156: 131-134.
10. Young LW, Westcott ND, Attenkofer K, Reaney MJ (2006). A high-throughput determination of metal concentrations in whole intact Arabidopsis thaliana seeds using synchrotron-based X-ray fluorescence spectroscopy. J Synchrotron Radiat 13: 304-313. [10]
11. Ozaki T, Enomoto S, Minai Y, Ambe S, Makide Y (2000). A survey of trace elements in pteridophytes. Biol Trace Elem Res 74: 259-273.
12. Watanabe T, Broadley MR, Jansen S, White PJ, Takada J, Satake K, Takamatsu T, Tuah SJ, Osaki M (2007). Evolutionary control of leaf element composition in plants. New Physiol 174: 516-523. [11]
13. Garg AN, Singh V, Weginwar RG, Sagdeo VN (1994). An elemental correlation study in cancerous and normal breast tissue with successive clinical stages by neutron activation analysis. Biol Trace Elem Res 46: 185-202.
14. Ng KH, Ong SH, Bradley DA, Looi LM (1997). Discriminant analysis of normal and malignant breast tissue based upon INAA investigation of elemental concentration. Appl Radiat Isot 48: 105-109. [12]
15. Shenberg C, Feldstein H, Cornelis R, Mees L, Versieck J, Vanballenberghe L, Cafmeyer J, Maenhaut W (1995). Br, Rb, Zn, Fe, Se and K in blood of colorectal patients by INAA and PIXE. J Trace Elem Med Biol 9: 193-199.
16. Arriola H, Longoria L, Quintero A, Guzman D (1999). INAA of trace elements in colorectal cancer patients. Biol Trace Elem Res 71-72: 563-568.
17. Andrási E, Suhajda M, Sáray I, Bezúr L, Ernyei L, Réffy A (1993). Concentration of elements in human brain: glioblastoma multiforme. Sci Total Environ 139-140: 399-402.
18. Baxter I, Ouzzani M, Orcun S, Kennedy B, Jandhyala SS, Salt DE (2007) Purdue Ionomics Information Management System (PIIMS): An integrated functional genomics platform. Plant Physiol 143: 600-611. [13]
19. Tchieu JH, Fana F, Fink JL, Harper J, Nair TM, Niedner RH, Smith DW, Steube K, Tam TM, Veretnik S, Wang D, Gribskov M (2003) The PlantsP and PlantsT Functional Genomics Databases. Nucleic Acids Red 31: 342-344. [14]
