Further investigation found that transcription factor NAC4 acted downstream of AFB3 to regulate lateral root growth in response to nitrate, but did not affect primary root growth, indicating that AFB3 is likely to be involved in two dependent pathways to modulate root system architecture. Furthermore, AFB3 and NAC4 gene expression in response to nitrate treatment depends on the nitrate transport function of NRT1.1. Moreover, peptide-mediated signaling has been found in N control of root growth. The Arabidopsis CLE gene was found to be induced by N-deficiency, and over expression of CLE inhibits lateral root elongation but not initiation. The peptide sequence of CLE is homologous to CLV3, which binds to CLV1 and the clv1 mutant showed increased lateral root length under low N conditions. The transcript levels of CLE were increased in the clv1 mutant, suggesting a feedback regulation of CLE by CLV1. Transgenic lines with increased CLE levels in clv1 did not inhibit lateral root growth, indicating that the inhibition of CLE3 on lateral root development requires CLV1. Altogether, the N-responsive CLE-CLV1 peptide-receptor signaling module restricts expansion of the lateral root system in N-deficient environments. Although nitrate is a crucial nutrient and signaling molecule, its distribution in soils is heterogeneous. To adapt the prevailing nitrate conditions, plants have evolved a systemic response mechanism. NRT2.1 was the first molecular target identified in long-distance signaling reflecting root responses to environmental nitrate conditions.
Plants were grown using a 1 mM NO3 − solution, then the root was split into two parts,vertical aeroponic tower garden one subjected to N-free treatment and the other one treated with 1 mM NO3 −. Both 15NO3 − influx and the transcript level of NRT2.1 were increased in the NO3 −-fed root. Recent findings revealed that the C-terminally encoded peptide originated from N-starved roots; located in xylem vessels, it sends root-derived ascending signals to the shoot before being recognized by a leucine-rich repeat receptor kinase, CEPReceptor 1 , and then inducing the expression of CEPD polypeptides. CEPD sent long-distance mobile signals translocated to each root and upregulated the expression of NRT2.1. The activity and expression of NRT2.1 in plants were inhibited when supplied with high N. Lepetit’s lab configured a forward genetic approach using a transgenic line expressing the pNRT2.1::LUC construct as a reporter gene. The mutant hni9, showing increased expression of NRT2.1 under high N supply, was isolated and the mutation was found in IWS1, a component of the RNAPII complexes. Further investigation revealed that the levels of the H3K27me3 on NRT2.1 chromatin decreased, resulting in the upregulated expression of NRT2.1 in response to high N supply in the iws1 mutants. Thus IWS1 is likely to be involved in the transduction of N systemic signals through controlling the expression of NRT2.1 in plants. Another important player participating in root foraging, TCP20, was identified by Crawford’s lab using the yeast one-hybrid system to screen the transcription factors that can bind to the fragment of nitrate enhance DNA. TCP20 was found to be able to bind to the promoters of NIA1, NRT1.1, and NRT2.1.
The tcp20 mutants exhibited deficiencies in preferential lateral root growth on heterogeneous media in split-root experiments, indicating that TCP20 can regulate the preferential growth of lateral roots in high nitrate zones, thus playing an important role in the systemic signaling pathway. Recently, using an electrophoretic mobility shift assay , the DNA-binding sites of TCP20 in a 109 bp NIA1 enhancer fragment were found to be in close proximity to NLP7 and NLP6 binding sites. Yeast two-hybrid and bimolecular fluorescence complementation assays showed that NLP7 and NLP6 can interact with TCP20 and both the PB1 domains of NLP6&7 and the glutamine-rich domain of TCP20 are necessary for protein–protein interaction. Further work will be needed to elucidate the underlying molecular mechanism explaining the involvement of TCP20 in systemic signaling.Root microbiota associate with every land plant and show community compositions and dynamics that are distinct from the surrounding soil microbial community . Both rhizosphere and root endosphere microbiomes affect plant health and soil health via processes such as mineral and nutrient turnover and pathogen suppression . Attribution of specific processes to distinct microbial players or populations is challenging because soil ecosystems are among the most complex environments on Earth . Soils are made up of a multitude of heterogeneous abiotic and biotic components that interact in a dynamic fashion over a range of spatial and temporal scales.Soil type, together with climatic characteristics, allows for the development and activity of biological constituents that are specific to a given soil in a particular location and can vary dramatically among soils and locations .
Those biological constituents can include plants, insects, bacteria, archaea, and fungi, which all contribute to and feed off of the bio-geochemical cycles in a given soil. The resulting complex network of interactions is extremely challenging to disentangle due to technological limitations and insufficient information in biological and chemical reference databases . Furthermore, soils contain a vast diversity of microorganisms, which are heterogeneously distributed and engage in frequent horizontal gene transfer. Despite this, most root microbiome studies present data from single time points or single locations and primarily conduct amplicon sequencing combined with limited information on plant or environment. Although the average values provided by such studies may suggest some interactions or mechanisms, few studies follow up with the comprehensive sampling necessary to definitively understand these mechanisms and interactions. In addition, single-point studies are difficult to compare or extrapolate to other environments or plants because measured values can vary dramatically over time . Soil and other environmental characteristics can be important indicators of bio-geochemical processes that have occurred in the past or are ongoing. Generally, few root and soil microbiome studies take advantage of the relatively inexpensive techniques to measure soil characteristics. Data on parameters such as pH, volumetric water content, temperature, and salt concentration could allow researchers to draw correlations between microbial activity, plant productivity, and environmental parameters and facilitate opportunities to cross reference studies conducted under comparable conditions. In the last decade, the root microbiome research community has made tremendous progress in understanding the complexity of soil ecosystems through improvements in experimental methods at both laboratory and field scales. These exciting technological and scientific advancements pave the way forward in root microbiome research. This review summarizes recent technological advancements and the resulting research opportunities categorized by ecosystem component and scale ,vertical gardening in greenhouse and ends with an outlook and potential applications for phytobiome research.Microbial colonization of the root and rhizosphere can significantly affect root phenology and metabolism. Roots demonstrate enormous phenotypic plasticity with respect to anatomy, shape, cell type, cellular structure, metabolism, and biochemical composition, and these characteristics contribute tremendously to root exudation variation and, as a result, to microbial community differentiation . These reciprocal interactions between roots and microbes are not well understood but their direct link showcases the fact that, for understanding root microbiomes, a foundational understanding of root biology is required. Although hyperspectral imaging of leaves has been broadly applied to monitor plant health, even simple imaging of intact roots has lagged behind due to the challenges presented by the opaqueness of soil . Ideally, imaging of root architecture, microbes, and chemical composition as well as visualization of fluxes such as carbon flow through plant compartments and into the soil would be conducted at multiple temporal and spatial scales. Most current methods for analyzing root growth either require artificial growing conditions , are severely restricted in the fraction of roots detectable , or are destructive . For example, many root phenotypic datasets have employed coring or “shovelomics”, subsequent root picking and washing, and imaging using light imagers such as the RhizoVision Crown platform . This method provides valuable information about root architecture; however, it is extremely laborious, it is often not feasible to excavate deep roots, it can remain unknown how much of the root system was recovered and scanned, and root excavation often times terminates the experiment for the selected plants. All of these methods are severely limited because they are destructive, low throughput, or artificial. The later point is particularly important because root architecture can be significantly affected by plant genetics, environmental conditions, soil type, and rootcolonizing bacteria and fungi . Magnetic resonance imaging presents a noninvasive modality that addresses some of the limitations of other root measurement techniques.
When coupled with an analysis pipeline in an automated system, MRI can monitor root mass, length, diameter, tip number, growth angles , and spatial distribution in a high-throughput manner . Similarly, X-ray computed tomography scanning can provide a comprehensive picture of root systems as long as the roots have a diameter larger than the instruments’ resolution . Hence, small plants or young roots are not likely to be resolved well. Another limitation common to both MRI and CT technology is that plants must be grown in pots that fit into the imaging machines and the applicability of MRI and X-ray CT in three-dimensional imaging of root systems across various pot sizes was recently evaluated . Although both MRI and CT were able to resolve high-quality 3D images of root systems in vivo, the reconstructed length and image details differed significantly between the two methods. In small pots, CT outperformed MRI and provided more details thanks to higher resolution whereas, in large pots, MRI was able to display root systems more comprehensively than CT. Soil features such as minerals and burrows can be resolved with CT, while MRI can measure water content in roots and soil. Both CT and MRI, struggled with roots thinner than 400 mm .There are a number of imaging tools that can resolve root vasculature. Using Synchrotron X-ray microtomography, Milien et al. contrasted the 3D images of vascular systems of successful and unsuccessful graft interfaces in vine rootstocks. Others have applied synchrotron X-ray microtomography to visualize drought-induced embolism in various plant species , to correlate root hair with rhizosphere soil structure formation , and to quantify root-induced changes of rhizosphere physical properties . Although synchrotron X-ray micro-CT can render unprecedented detail into the microanatomy of plants and microorganisms, the focus window is relatively limited and biological samples tend to lose viability as a result of the intense X-ray radiation. There are various other imaging methods that have been recently developed or applied to phytobiome research, including super resolution confocal imaging, which can enhance 3D mapping of root and microbial or fungal cells and showcase green fluorescent proteins , and correlative confocal and focused ion beam tool with integrated scanning electron microscope, which allows for extremely fine-scaled 3D mapping . When applied individually or in combination, the above mentioned imaging methods will provide opportunities to visualize plant tissue and attached or internally residing bacteria, fungi, and viruses at unprecedented resolution, as well provide information about their physical and chemical context. Because root development is vital for plant health, expansion of root image databases and novel correlations between above- and below ground plant features will enhance our understanding of plant response to environmental and biological stimuli.An important goal of the plant-microbiome field is to discover beneficial or deleterious effects of microbes. This means that recording and understanding plant phenotypes and linking them to microbiome variation is key. Similarly, plant microbiomes are intimately tied to the background soil; hence, monitoring soil characteristics is important but can be challenging and labor intensive at appropriate temporal or spatial scales.Unmanned aerial vehicles equipped with RGB cameras, infrared cameras, multi-spectral and hyperspectral cameras, GPS, navigation systems, programmable controllers, and automated flight planning have emerged as powerful tools for nondestructive, high-throughput field phenotyping that can be performed throughout the growth season . This has removed a bottleneck in phenotyping but automated processing of this data still presents various challenges, which are discussed elsewhere . Monitoring of agricultural fields using drones has become popular among researchers to more accurately plan and manage their experimental operations. Drones can produce precise maps of soil characteristics and plant characteristics , as well as determine irrigation needs, nitrogen levels, and pest occurrence . RGB, IR, and hyper- as well as multi-spectral cameras attached to drones can collect images of the above ground portion in a range of wavelengths.