Baxter Ivan R, Vitek Olga, Lahner Brett, Muthukumar Balasubramaniam, Borghi Monica, Morrissey Joe, Guerinot Mary Lou, Salt David E
Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA.
Proc Natl Acad Sci U S A. 2008 Aug 19;105(33):12081-6. doi: 10.1073/pnas.0804175105. Epub 2008 Aug 12.
The contention that quantitative profiles of biomolecules contain information about the physiological state of the organism has motivated a variety of high-throughput molecular profiling experiments. However, unbiased discovery and validation of biomolecular signatures from these experiments remains a challenge. Here we show that the Arabidopsis thaliana (Arabidopsis) leaf ionome, or elemental composition, contains such signatures, and we establish statistical models that connect these multivariable signatures to defined physiological responses, such as iron (Fe) and phosphorus (P) homeostasis. Iron is essential for plant growth and development, but potentially toxic at elevated levels. Because of this, shoot Fe concentrations are tightly regulated and show little variation over a range of Fe concentrations in the environment, making them a poor probe of a plant's Fe status. By evaluating the shoot ionome in plants grown under different Fe nutritional conditions, we have established a multivariable ionomic signature for the Fe response status of Arabidopsis. This signature has been validated against known Fe-response proteins and allows the high-throughput detection of the Fe status of plants with a false negative/positive rate of 18%/16%. A "metascreen" of previously collected ionomic data from 880 Arabidopsis mutants and natural accessions for this Fe response signature successfully identified the known Fe mutants frd1 and frd3. A similar approach has also been taken to identify and use a shoot ionomic signature associated with P homeostasis. This study establishes that multivariable ionomic signatures of physiological states associated with mineral nutrient homeostasis do exist in Arabidopsis and are in principle robust enough to detect specific physiological responses to environmental or genetic perturbations.
生物分子的定量概况包含有关生物体生理状态信息的观点推动了各种高通量分子概况分析实验。然而，从这些实验中无偏倚地发现和验证生物分子特征仍然是一项挑战。在这里，我们表明拟南芥叶片离子组或元素组成包含此类特征，并且我们建立了统计模型，将这些多变量特征与特定的生理反应联系起来，例如铁（Fe）和磷（P）稳态。铁对植物生长发育至关重要，但在高水平时可能有毒。因此，地上部铁浓度受到严格调控，并且在环境中铁浓度的一定范围内变化很小，这使得它们难以作为植物铁状态的有效指标。通过评估在不同铁营养条件下生长的植物的地上部离子组，我们建立了拟南芥铁反应状态的多变量离子组特征。该特征已通过已知的铁反应蛋白进行验证，并且能够以18%/16%的假阴性/阳性率高通量检测植物的铁状态。对先前从880个拟南芥突变体和自然种质中收集的离子组数据进行的“元筛选”，成功识别出了已知的铁突变体frd1和frd3。我们还采用了类似的方法来识别和利用与磷稳态相关的地上部离子组特征。这项研究表明，拟南芥中确实存在与矿质营养稳态相关的生理状态的多变量离子组特征，并且原则上足够稳健，能够检测对环境或遗传扰动的特定生理反应。
