Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Curr Opin Plant Biol. Author manuscript; available in PMC 2010 April 1.
Published in final edited form as:
PMCID: PMC2683257

Functional genomics of root growth and development in Arabidopsis


Roots are vital for the uptake of water and nutrients, and for anchorage in the soil. They are highly plastic, able to adapt developmentally and physiologically to changing environmental conditions. Understanding the molecular mechanisms behind this growth and development requires knowledge of root transcriptomics, proteomics and metabolomics. Genomics approaches, including the recent publication of a root expression map, root proteome, and environment-specific root expression studies, are uncovering complex transcriptional and post-transcriptional networks underlying root development. The challenge is in further capitalizing on the information in these datasets to understand the fundamental principles of root growth and development. In this review, we highlight progress researchers have made toward this goal.


The simple structure of the Arabidopsis root makes it ideal for studying root development. The root is composed of 15 distinct cell types (Figure 1), with the outer cell types arranged in concentric cylinders around the radial axis. Root cells are produced from stem cells in the meristematic zone at the tip of the root. The stem cells are kept in their undifferentiated state by the quiescent center (QC), a mitotically less active region composed of 4–7 cells in Arabidopsis. As new cells are produced from stem cells, older cells are displaced into single files toward the shoot, passing through the elongation and maturation zones as they age. Thus the root can be thought of as a developmental timeline, with young cells at the tip and differentiated cells more distal. In the past, most work on root development focused on single mutant analysis. Using this approach, researchers have defined many of the processes that pattern the different cell types within the root (recently reviewed in [1*]). Although this approach is as been fruitful, it only begins to reveal the myriad interactions underlying the global complexity of root growth and development.

Figure 1
(A) Diagram of cell types in the Arabidopsis root meristem. The stele is a heterogenous tissue composed of multiple cell types, three of which are shown in the transverse section of the root in (B).

Recent publication of several genome-wide data sets of the root provide researchers the needed tools to understand this complexity. Already, these data are unveiling complex transcriptional and posttranscriptional pathways within the root. Major challenges in future years include moving past purely descriptive studies to understand the principles driving these pathways, integrating these large-scale datasets, and transferring what we learn from model plant systems to agriculturally relevant crop species. In this review we focus on analyses characterizing root development with genome-wide approaches, and discuss examples in which researchers identified genes or underlying pathways involved in root development.

The Arabidopsis “Root Map”


Understanding the underlying biological principles guiding development requires knowledge of transcriptional, proteomic, and metabolic responses occurring in multiple cell types and developmental stages of an organ. Several recent studies are moving us towards this understanding [2, 3**, 46]. Cell-type specific profiling reveals more transcriptional complexity than whole organ profiling, due to dilution of cell-type specific genes in whole organ experiments. Transcriptional profiling of nearly all cell types in the root coupled with 13 longitudinal sections created a complete spatiotemporal map of the root [3**]. The analysis revealed dominant expression patterns between ontologically unrelated cell types and identified those that fluctuate in developmental time. Examination of enriched cis-elements in these groups of coexpressed genes enabled prediction of new transcription factor targets. One pattern with enrichment of auxin biosynthesis functions and MYB-binding sites contained a single member of the MYB transcription factor family, Altered Tryptophan Regulation 1 (ATR1). Of the genes within this pattern containing MYB binding sites, several had known functions in auxin biosynthesis, and all provide potential targets for ATR1 regulation. Understanding the functional importance of these dominant expression patterns will contribute to our understanding of the networks that guide root development in space and time.

Another set of studies has examined the transcriptome of different organs and developmental stages of Arabidopsis, including the root, in response to over 40 conditions [46]. While not at cell-type specific resolution, the AtGenExpress data provide a powerful tool for coexpression analyses, reverse genetics, and functional genomics approaches.

Proteomics and Metabolomics

In addition to gene expression, proteins and metabolites contribute to an organism’s molecular phenotype. A recent global analysis of the Arabidopsis whole root proteome identified approximately 5159 proteins in 10 day old roots, and 4466 in 23 day old roots [7**]. Proteins in GO categories for intracellular protein transport, response to oxidative stress, and toxin catabolic process were overrepresented in their analyses.

Several recent reports analyze the effects of stress, nutrients, or genotype on the Arabidopsis metabolome [812], but these have focused primarily on aerial organs or whole plants. Cell-type specific proteomic and metabolic data are currently in development (Benfey lab, unpublished data). The combination of transcriptional, proteomic and metabolomic cell-type specific data will facilitate not only the functional identification of networks controlling root growth and development, but an understanding of how these networks interact with each other and the emergent properties of the root.

Towards Rice and Maize Root Maps

In contrast to the dicot root system of Arabidopsis, the monocot root system is composed of several different root structures with different functions. Genome-wide analyses of different root types and specific cell types in roots of grasses are important for understanding the differences between monocot and dicot root development. Laser capture microdissection (LCM) is frequently the method of choice for cell type specific analyses in species where GFP-marked lines are not readily available. A LCM transcriptome atlas for rice contains 40 cell types, including 13 from the root [13**]. As with Arabidopsis, GO category enrichment in specific cell types correlated with known biological functions, and new functions could be predicted. Comparisons between Arabidopsis and rice roots showed interesting parallels and dissimilarities. This will be an exciting area for future research.

Though a comprehensive study examining all cell types within the maize root system has yet to emerge, several groups have begun to profile the transcriptome or proteome of different developmental sections of the primary root or different types of roots within the system [1421]. Together these studies demonstrate differences in gene and protein expression between dissimilar root structures and cell types and highlight the need for additional work in this area.


One of the major challenges now facing biologists is how to capitalize on the wealth of information currently available. Integrating these ‘omics-based’ data sets in a systems biology approach is necessary for a complete understanding of the root as a whole. A full evaluation of these tools and approaches is out of the scope of this review, but for recent discussions see [2225*].

Meta-analyses, in which many large-scale data sets are integrated to improve statistical confidence, have revealed global expression views of several root growth responses, including root patterning [26*] and those related to stress [2729]. Using a meta-analysis of three microarray datasets, Levesque et al. [26*] identified 8 putative direct targets and nearly 500 indirect targets of SHORTROOT, a TF important for the developmental pathway regulating root patterning [30,31], The authors suggest a model in which SHR regulation of these targets and the interactions between them determines the outcome of the SHR pathway in root development [26*].

Several studies have combined meta-analyses or data integration analyses with reverse genetics to identify the function of unknown genes [32*–36]. Although these studies are not root-focused, as more high-resolution root-specific datasets are created, similar approaches promise to lead to comparable results. Below, we highlight studies that have revealed novel genes and networks using transcriptomics or proteomics-based datasets in the root. Since a comprehensive review is not possible here, we focus on important examples of the use of genomic approaches to identify genes involved in the modulation of root growth and development by nutrient availability.

Dissecting root growth and development through functional genomics

Nutritional responses at whole root resolution

Soil nutrient deficiencies are one of the most important constraints for plant growth and development. Because nutrients are not evenly distributed in soils, changes in root architecture can profoundly affect the nutrient uptake capacity of plants. The availability of nutrients such as phosphate, nitrate and iron modulates lateral root (LR) and root hair (RH) formation and elongation [37]. Functional genomic analysis of plant responses to nutrient deficiency has been mainly based on transcriptional profiling coupled with the use of T-DNA mutants, overexpression, and RNAi strategies.

Pi starvation led to the differential expression of several hundred genes, of which 40 to 50% were repressed when Pi was re-supplied [3840]. PLDZ1 and PLDZ2 are strongly induced in Pi deprived plants, and mutational analysis showed that the encoded phospholipases release Pi for other cellular activities and regulation of primary root growth by degrading phospholipids. This may be due to reduced Pi recycling or because the biosynthesis of phospholipid-derived signaling molecules is compromised [41,42]. In addition to genes involved in metabolic processes, four TFs induced by Pi deprivation were shown to play roles in root architecture and/or root hair formation. Overexpression of AtZAT6 altered root architecture [43], while mutant analysis of AtbHLH32 reduced RH number and resulted in higher anthocyanin and Pi content [44]. RNAi suppression of AtWRKY75 resulted in an increase in LR and RH number and length [45]. Finally, overexpression of a rice bHLH TF, OsPTF1, led to an increase in root biomass and a concomitant increase in tiller number and Pi content [46*]. It remains to be determined how these transcription factors act to coordinate Pi deprivation responses, however, these data show that genomic analysis can lead to the discovery of genes that could be used to enhance plant productivity in low Pi soil. Additionally, comparative proteome analysis of Pi-responses in maize, indicates that organic acid secretion, sugar metabolism and root-cell proliferation are important components of high tolerance to low Pi conditions [47].

Genomic analysis also led to the discovery of microRNAs involved in Pi homeostasis. Pi starvation rapidly induces the expression of microRNA399 (miR399), which mediates the degradation of the mRNA of PHO2. Arabidopsis pho2 mutants accumulate excessive amounts of Pi in the shoot; suppression of PHO2 by the transcriptional activation of miR399 leads to increased Pi acquisition and translocation to the shoot. miR399 acts as a systemic signal since it undergoes long distance movement from shoot to roots [48,49**].

Low postassium (K) availability also regulates root architecture, and leads to a reduction in the number and length of LRs. Microarray analysis showed that genes related to reactive oxygen species (ROS) are activated, while the TF AtMYB77 is involved in K repressed in K limiting conditions. Further analysis revealed that H2O2 signaling and leads to increased K uptake [50] and that MBY77 modulates auxin sensitivity and LR formation by interacting with Auxin Responsive Factors, suggesting that reduction of LR formation under low K availability may be due to a reduction in auxin response [51*].

In addition to the nutrients discussed above, genome-wide nitrogen (N) responses have been examined in Arabidopsis, maize and Medicago trunculata. Over one thousand genes were found to be responsive in the three plant species, and responses to N deprivation were more rapid than for other nutrients [52,53]. Systemic signals were also shown to trigger specific transcriptome responses depending upon the nitrogen source available to the plants [54]. Although transcription profiling identified many N responsive genes, including several dozen TFs, to date the functional characterization of genes responsive to local or systemic N signals is still lacking.

Environmental responses at cell type resolution

Two recent studies give us the first look at environmental effects on the transcription network in specific cell types. Dinneny, Long and colleagues [55**] examined the response to high salt and iron deficiency in specific cell types and developmental stages of the root. Most responses to salt and iron were cell-type specific and were dependent on specific environmental conditions. The 244 genes unaffected by either stress were enriched for genes functioning in cell-type specification, suggesting that genes necessary for cell identity are environment-independent. Unexpectedly, genes co-regulated by salt and iron are not ubiquitously expressed. This indicates that cell-type specific genes may be targets for stress regulation. The level of conservation among biological functions enriched in cell types under high salt and low iron conditions varied according to cell type and stress, highlighting the need for additional studies in this area.

A similar study illustrates the power of these datasets to reveal novel connections in the root. Gifford et al. [56**] examined the effect of adding back nitrogen (N) on the transcriptional profile of different cell types in N-depleted roots. Their analysis revealed that miR167a,b and its target ARF8 regulate lateral root development in response to nitrogen. The authors identified 126 putative targets of ARF8 and showed that N status coordinately regulates these genes.


We are at an exciting time in root biology. The advent of large-scale, high resolution datasets provides the means to deepen our understanding of single genes and their phenotypic effects. There is a need for cell-type specific proteomic and metabolic data, as well as more high-resolution information regarding the root’s response to different environmental stresses. Integration of these datasets coupled with reverse genetics and cell biology techniques will allow elucidation of root growth and developmental pathways at the systems level (Figure 3). Though much work remains to be done, we are now beginning to identify the complex transcriptional and post-transcriptional circuits that govern root developmental processes, both in standard and changing environmental conditions. Comparisons of Arabidopsis and rice cell-type specific root transcriptomes will reveal basic differences between dicot and monocot root systems. The challenges now include capitalizing on these datasets to extract the underlying biological meaning, translating this information to crop plants for which data are not available, and applying this information to crop improvement. Ultimately, these data will be a valuable tool for understanding root development across many plant species.

Figure 3
Functional ‘-Omic’ approaches for root growth and development: Isolation of specific cell types by FACS or LCM followed by RNA, protein, or metabolite extraction, coexpression analyses, and reverse genetics can identify factors in root ...
Figure 2
Phosphate sensing and signaling in Arabidopsis. Phosphate levels are perceived both by local and systemic sensing systems. Phosphate starvation alters gene expression leading to alterations in primary root length, LR initiation and elongation, and root ...


We thank members of the Benfey lab for critical reading of the manuscript. Work on root growth and development in the Benfey lab is funded by grants from the NIH (RO1-GM 043778, 5 P50 GM081883) and from the NSF AT2010 and PGRP programs. Funding for nutrient stress responses in the Herrera-Estrella lab is funded by HHMI (55005946) and CONACyT (299/43979).


Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.


1*. Iyer-Pascuzzi A, Benfey P. Transcriptional networks in root cell fate specification. BBA Gene Reg Mech. in press. A thorough review covering Arabidopsis root patterning.
2. Birnbaum K, Shasha DE, Wang JY, Jung JW, Lambert GM, Galbraith DW, Benfey PN. A gene expression map of the Arabidopsis root. Science. 2003;302:1956–1960. [PubMed]
3**. Brady SM, Orlando DA, Lee JY, Wang JY, Koch J, Dinneny JR, Mace D, Ohler U, Benfey PN. A high-resolution root spatiotemporal map reveals dominant expression patterns. Science. 2007;318:801–806. In this elegant work, the authors created a spatiotemporal transcriptional map of the Arabidopsis root by profiling nearly all cell types and 12 developmental stages, creating the most comprehensive transcriptional view of any organ to date. They identified dominant transcription patterns and used coexpressed genes to find transcriptional modules. [PubMed]
4. Schmid M, Davison TS, Henz SR, Pape UJ, Demar M, Vingron M, Scholkopf B, Weigel D, Lohmann JU. A gene expression map of Arabidopsis thaliana development. Nat Genet. 2005;37:501–506. [PubMed]
5. Kilian J, Whitehead D, Horak J, Wanke D, Weinl S, Batistic O, D’Angelo C, Bornberg-Bauer E, Kudla J, Harter K. The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant J. 2007;50:347–363. [PubMed]
6. Goda H, Sasaki E, Akiyama K, Maruyama-Nakashita A, Nakabayashi K, Li W, Ogawa M, Yamauchi Y, Preston J, Aoki K, et al. The AtGenExpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access. Plant l. 2008;55:526–542. [PubMed]
7**. Baerenfaller K, Grossmann J, Grobei MA, Hull R, Hirsch-Hoffmann M, Yalovsky S, Zimmermann P, Grossniklaus U, Gruissem W, Baginsky S. Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science. 2008;320:938–941. The first proteomic map of Arabidopsis, this paper gives the first large-scale view into the root proteome. [PubMed]
8. Rowe HC, Hansen BG, Halkier BA, Kliebenstein DJ. Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome. Plant Cell. 2008;20:1199–1216. [PubMed]
9. Keurentjes JJB, Fu JY, de Vos CHR, Lommen A, Hall RD, Bino RJ, van der Plas LHW, Jansen RC, Vreugdenhil D, Koornneef M. The genetics of plant metabolism. Nat Genet. 2006;38:842–849. [PubMed]
10. Nikiforova VJ, Kopka J, Tolstikov V, Fiehn O, Hopkins L, Hawkesford MJ, Hesse H, Hoefgen R. Systems rebalancing of metabolism in response to sulfur deprivation, as revealed by metabolome analysis of arabidopsis plants. Plant Physiol. 2005;138:304–318. [PubMed]
11. Cook D, Fowler S, Fiehn O, Thomashow MF. A prominent role for the CBF cold response pathway in configuring the low-temperature metabolome of Arabidopsis. Proc Natl Acad Sci USA. 2004;101:15243–15248. [PubMed]
12. Hirai MY, Yano M, Goodenowe DB, Kanaya S, Kimura T, Awazuhara M, Arita M, Fujiwara T, Saito K. Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci USA. 2004;101:10205–10210. [PubMed]
13**. Jiao Y, Tausta LS, Gandotra N, Sun N, Liu T, Clay NK, Ceserani T, Chen M, Ma L, Holford M, et al. A transcriptome atlas of rice cell types reveals cellular, functional, and developmental hierarchies. Nat Genet. 2008 in press. The authors used LCM and microarray analysis to profile 40 cell types in rice, including 13 cell types and 3 developmental stages in the root. They identify interesting differences between the rice and Arabidopsis roots that will be an exciting area for future research. [PubMed]
14. Poroyko V, Hejlek LG, Spollen WG, Springer GK, Nguyen HT, Sharp RE, Bohnert HJ. The maize root transcriptome by serial analysis of gene expression. Plant Physiol. 2005;138:1700–1710. [PubMed]
15. Dembinsky D, Woll K, Saleem M, Liu Y, Fu Y, Borsuk LA, Lamkemeyer T, Fladerer C, Madlung J, Barbazuk B, et al. Transcriptomic and proteomic analyses of pericycle cells of the maize primary root. Plant Physiol. 2007;145:575–588. [PubMed]
16. Zhu J, Alvarez S, Marsh EL, LeNoble ME, Cho I-J, Sivaguru M, Chen S, Nguyen HT, Wu Y, Schachtman DP, et al. Cell wall proteome in the maize primary root elongation zone. II. Region-specific changes in water soluble and lightly ionically bound proteins under water deficit. Plant Physiol. 2007;145:1533–1548. [PubMed]
17. Zhu JM, Chen SX, Alvarez S, Asirvatham VS, Schachtman DP, Wu YJ, Sharp RE. Cell wall proteome in the maize primary root elongation zone. I. Extraction and identification of water-soluble and lightly ionically bound proteins. Plant Physiol. 2006;140:311–325. [PubMed]
18. Jiang K, Zhang SB, Lee S, Tsai G, Kim K, Huang HY, Chilcott C, Zhu T, Feldman LJ. Transcription profile analyses identify genes and pathways central to root cap functions in maize. Plant Mol Biol. 2006;60:343–363. [PubMed]
19. Sauer M, Jakob A, Nordheim A, Hochholdinger F. Proteomic analysis of shoot-borne root initiation in maize (Zea mays L) Proteomics. 2006;6:2530–2541. [PubMed]
20. Liu Y, Lamkemeyer T, Jakob A, Mi GH, Zhang FS, Nordheim A, Hochholdinger F. Comparative proteome analyses of maize (Zea mays L. ) primary roots prior to lateral root initiation reveal differential protein expression in the lateral root initiation mutant rum1. Proteomics. 2006;6:4300–4308. [PubMed]
21. Woll K, Borsuk LA, Stransky H, Nettleton D, Schnable PS, Hochholdinger F. Isolation, characterization, and pericycle-specific transcriptome analyses of the novel maize lateral and seminal root initiation mutant rum1. Plant Physiol. 2005;139:1255–1267. [PubMed]
22. Yuan JS, Galbraith DW, Dai SY, Griffin P, Stewart CN. Plant systems biology comes of age. Trend Plant Sci. 2008;13:165–171. [PubMed]
23. Aoki K, Ogata Y, Shibata D. Approaches for extracting practical information from gene co-expression networks in plant biology. Plant Cell Physiology. 2007;48:381–390. [PubMed]
24. Rhee SY, Dickerson J, Xu D. Bioinformatics and its applications in plant biology. Ann Rev Plant Bio. 2006;57:335–360. [PubMed]
25*. Saito K, Hirai MY, Yonekura-Sakakibara K. Decoding genes with coexpression networks and metabolomics - ‘majority report by precogs’ Trend Plant Sci. 2008;13:36–43. A good review covering coexpression analyses and large data sets. [PubMed]
26*. Levesque MP, Vernoux T, Busch W, Cui HC, Wang JY, Blilou I, Hassan H, Nakajima K, Matsumoto N, Lohmann JU, et al. Whole-genome analysis of the SHORT-ROOT developmental pathway in Arabidopsis. Plos Biology. 2006;4:1284–1284. This is a good example of how meta-analysis can lead to identification of downstream targets, network architecture, and new functions for well-studied genes. [PMC free article] [PubMed]
27. Hannah MA, Heyer AG, Hincha DK. A global survey of gene regulation during cold acclimation in Arabidopsis thaliana. Plos Genetics. 2005;1:179–196. [PMC free article] [PubMed]
28. Ma SS, Bohnert HJ. Integration of Arabidopsis thaliana stress-related transcript profiles, promoter structures, and cell-specific expression. Genome Biol. 2007;8 [PMC free article] [PubMed]
29. Walther D, Brunnemann R, Selbig J. The regulatory code for transcriptional response diversity and its relation to genome structural properties in A-thaliana. Plos Genetics. 2007;3:216–229. [PMC free article] [PubMed]
30. Helariutta Y, Fukaki H, Wysocka-Diller J, Nakajima K, Jung J, Sena G, Hauser MT, Benfey PN. The SHORT-ROOT gene controls radial patterning of the Arabidopsis root through radial signaling. Cell. 2000;101:555–567. [PubMed]
31. Nakajima K, Sena G, Nawy T, Benfey PN. Intercellular movement of the putative transcription factor SHR in root patterning. Nature. 2001;413:307–311. [PubMed]
32*. Hirai MY, Sugiyama K, Sawada Y, Tohge T, Obayashi T, Suzuki A, Araki R, Sakurai N, Suzuki H, Aoki K, et al. Omics-based identification of Arabidopsis Myb transcription factors regulating aliphatic glucosinolate biosynthesis. Proc Natl Acad Sci USA. 2007;104:6478–6483. A good example of co-expression and data integration analyses combined with reverse genetics to identify gene function and gene-to-metabolite networks. [PubMed]
33. Sønderby IE, Hansen BG, Bjarnholt N, Ticconi C, Halkier BA, Kliebenstein DJ. A systems biology approach identifies a R2R3 MYB gene subfamily with distinct and overlapping functions in regulation of aliphatic glucosinolates. PLoS ONE. 2007;2:e1322. [PMC free article] [PubMed]
34. Persson S, Wei HR, Milne J, Page GP, Somerville CR. Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets. Proc Natl Acad Sci USA. 2005;102:8633–8638. [PubMed]
35. Yonekura-Sakakibara K, Tohge T, Matsuda F, Nakabayashi R, Takayama H, Niida R, Watanabe-Takahashi A, Inoue E, Saito K. Comprehensive flavonol profiling and transcriptome coexpression analysis leading to decoding gene-metabolite correlations in Arabidopsis. Plant Cell. 2008;20:2160–2176. [PubMed]
36. Gutierez RA, Stokes TL, Thum K, Xu X, Obertello M, Katari MS, Tanurdzic M, Dean A, Nero DC, McClung CR, et al. Systems approach identifies an organic nitrogen-responsive gene network that is regulated by the master clock control gene CCA1. Proc Natl Acad Sci USA. 2008;105:4939–4944. [PubMed]
37. Desnos T. Root branching responses to phosphate and nitrate. Curr Opin Plant Biol. 2008;11:82–87. [PubMed]
38. Misson J, Raghothama KG, Jain A, Jouhet J, Block MA, Bligny R, Ortet P, Creff A, Somerville S, Rolland N, et al. A genome-wide transcriptional analysis using Arabidopsis thaliana Affymetrix gene chips determined plant responses to phosphate deprivation. Proc Natl Acad Sci USA. 2005;102:11934–11939. [PubMed]
39. Morcuende R, Bari R, Gibon Y, Zheng WM, Pant BD, Blasing O, Usadel B, Czechowski T, Udvardi MK, Stitt M, et al. Genome-wide reprogramming of metabolism and regulatory networks of Arabidopsis in response to phosphorus. Plant Cell Environ. 2007;30:85–112. [PubMed]
40. Calderon-Vazquez C, Ibarra-Laclette E, Caballero-Perez J, Herrera-Estrella L. Transcript profiling of Zea mays roots reveals gene responses to phosphate deficiency at the plant- and species-specific levels. J Exp Bot. 2008;59:2479–2497. [PubMed]
41. Cruz-Ramirez A, Oropeza-Aburto A, Razo-Hernandez F, Ramirez-Chavez E, Herrera-Estrella L. Phospholipase DZ2 plays an important role in extraplastidic galactolipid biosynthesis and phosphate recycling in Arabidopsis roots. Proc Natl Acad Sci USA. 2006;103:6765–6770. [PubMed]
42. Li MY, Qin CB, Welti R, Wang XM. Double knockouts of phospholipases D zeta 1 and D zeta 2 in Arabidopsis affect root elongation during phosphate-limited growth but do not affect root hair patterning. Plant Physiol. 2006;140:761–770. [PubMed]
43. Devaiah BN, Nagarajan VK, Raghothama KG. Phosphate homeostasis and root development in Arabidopsis are synchronized by the zinc finger transcription factor ZAT6. Plant Physiol. 2007;145:147–159. [PubMed]
44. Chen ZH, Nimmo GA, Jenkins GI, Nimmo HG. BHLH32 modulates several biochemical and morphological processes that respond to P-i starvation in Arabidopsis. Biochem J. 2007;405:191–198. [PubMed]
45. Devaiah BN, Karthikeyan AS, Raghothama KG. WRKY75 transcription factor is a modulator of phosphate acquisition and root development in arabidopsis. Plant Physiol. 2007;143:1789–1801. [PubMed]
46*. Yi KK, Wu ZC, Zhou J, Du LM, Guo LB, Wu YR, Wu P. OsPTF1, a novel transcription factor involved in tolerance to phosphate starvation in rice. Plant Physiol. 2005;138:2087–2096. This manuscript describes the identification of a phoshorous responsive transcription factor, that when overexpressed in transgenic rice plants leads to an increase in productivity under phosphate limiting conditions. [PubMed]
47. Li KP, Xu CZ, Li ZX, Zhang KW, Yang AF, Zhang JR. Comparative proteome analyses of phosphorus responses in maize (Zea mays L.) roots of wild-type and a low-P-tolerant mutant reveal root characteristics associated with phosphorus efficiency. Plant J. 2008;55:927–939. [PubMed]
48. Lin SI, Chiang SF, Lin WY, Chen JW, Tseng CY, Wu PC, Chiou TJ. Regulatory network of microRNA399 and PHO2 by systemic signaling. Plant Physiol. 2008;147:732–746. [PubMed]
49**. Pant BD, Buhtz A, Kehr J, Scheible WR. MicroRNA399 is a long-distance signal for the regulation of plant phosphate homeostasis. Plant J. 2008;53:731–738. In conjunction with the manuscript by Lin et al. (48), these papers describe the role of micro RNAs as systemic signals that participate in the regulation of phosphorus homeostasis in plants by targeting the UBC24 ubiquitin conjugase mRNA. [PMC free article] [PubMed]
50. Shin R, Schachtman DP. Hydrogen peroxide mediates plant root cell response to nutrient deprivation. Proc Natl Acad Sci USA. 2004;101:8827–8832. [PubMed]
51*. Shin R, Burch AY, Huppert KA, Tiwari SB, Murphy AS, Guilfoyle TJ, Schachtman DP. The Arabidopsis transcription factor MYB77 modulates auxin signal transduction. Plant Cell. 2007;19:2440–2453. This work shows for the first time that nutrient availability impinges into root development by altering the sensitivity of Arabidopsis roots to auxin. [PubMed]
52. Liu JX, Han LL, Chen FJ, Bao J, Zhang FS, Mi GH. Microarray analysis reveals early responsive genes possibly involved in localized nitrate stimulation of lateral root development in maize (Zea mays L) Plant Sci. 2008;175:272–282.
53. Scheible WR, Morcuende R, Czechowski T, Fritz C, Osuna D, Palacios-Rojas N, Schindelasch D, Thimm O, Udvardi MK, Stitt M. Genome-wide reprogramming of primary and secondary metabolism, protein synthesis, cellular growth processes, and the regulatory infrastructure of Arabidopsis in response to nitrogen. Plant Physiol. 2004;136:2483–2499. [PubMed]
54. Ruffel S, Freixes S, Balzergue S, Tillard P, Jeudy C, Martin-Magniette ML, van der Merwe MJ, Kakar K, Gouzy J, Fernie AR, et al. Systemic signaling of the plant nitrogen status triggers specific transcriptome responses depending on the nitrogen source in Medicago truncatula. Plant Physiol. 2008;146:2020–2035. [PubMed]
55**. Dinneny JR, Long TA, Wang JY, Jung JW, Mace D, Pointer S, Barron C, Brady SM, Schiefelbein J, Benfey PN. Cell identity mediates the response of Arabidopsis roots to abiotic stress. Science. 2008;320:942–945. The authors examined the effect of two environmental stresses on cellular responses in the root, using three datasets covering five cell types, four developmental zones, and a whole root time course. This is one of the first reports to examine the effect of stress at this level of resolution. [PubMed]
56**. Gifford ML, Dean A, Gutierrez RA, Coruzzi GM, Birnbaum KD. Cell-specific nitrogen responses mediate developmental plasticity. Proc Natl Acad Sci USA. 2008;105:803–808. Using a similar approach to Dinneny and Long, the authors examine the response to nitrate by different cell types within the root. Using this dataset, they identified a nitrogen-regulated transcriptional module controlling lateral root development. [PubMed]