Here we focus on two frequently used technologies that offer potential for the discovery and characterization of ncRNAs in horticultural plants: that is, NGS and ribosome profiling. NGS as a new powerful tool for the prediction of ncRNAs The ncRNAs can be identified through the direct detection of the transcribed RNAs.Initially, direct cloning approach has been used to discover ncRNAs in plants. Subsequently, the hybridization-based micro-array technology has been used to discover a large number of ncRNAs in the intergenic regions of A. thaliana and rice.However, the ability of these hybridization-based technologies suffer several limitations such as reduced dynamic range, high false positives6 and difficultly defining splice junctions and connecting transcribed regions into transcript models.NGS overcomes the challenges related to microarray technology,providing a powerful tool for defining the ncRNA domain. For example, miRNAs were previously thought to be dominant members in the sRNAs landscape; however, recent global analysis of plant transcriptomes revealed millions of siRNAs, making them the most abundant class of sRNAs in plants.77 More recently, circRNAs were recognized as a large new category of RNAs with thousands of members in animals and plants through high-throughput transcriptome sequencing followed by ncRNA prediction based on RNA-Seq data using new computational algorithms customized for ncRNAs .With advancement of NGS technology, many ncRNAs are being discovered in an expanding list of horticultural plant species.The length of 18 to 30 nucleotides is the threshold commonly used for the prediction of miRNA whereas the length of greater than 200 nucleotides is often used as the threshold for lncRNAs prediction.
Presence of an open reading frame of at least 100 amino acids is the threshold commonly used for defining a protein-coding transcript and as such,vertical hydroponics many important small proteins were not annotated in plants.More recently, a large number of protein sequences have been predicted by translation of the longest ORFs without any further experimental evidence.It is possible that some of the predicted protein-coding genes, based on an arbitrary ORF length, might be mis-annotated. For example, some well characterized human lncRNAs, such as H19, Hotair, Kcnq1ot1, Meg3 and Xist, contain ORFs of 100 aa or longer.Most of predicted lncRNAs contain putative ORFs, which may be translated into non-functional proteins or may be unable to be translated at all.Recently, ribosome profiling, which uses deep sequencing to monitor in vivo translation, has shown high potential for the genome-wide examination of protein-coding potential . Ribosome profiling has been used to segregate several hundred small proteins from predicted lncRNAs in zebra fish and humans.Also,Pamudurti et al.demonstrated that a group of circRNAs was associated with translating ribosomes by performing ribosome profiling from fly heads and found a circRNA generated from the muscle blind locus encodes a protein. In Arabidopsis, 237 protein-encoding transcripts from the existing compendia of ncRNAs were found based on the ribosome profiling technology.Thus, the ribosome profiling technology can be used as a high-throughput tool for removing false positives in the ncRNAs predictions of horticultural plants.Thanks to the advance in the aforementioned new technologies, the universe of ncRNAs is currently expanding at an increasing rate. However, the biological function of these ncRNAs remains largely unknown.Various approaches have been developed for functional studies of ncRNAs . The primary goal of functional studies on ncRNAs is to understand the biological processes in which the ncRNAs are involved. To achieve this goal, many researchers have used gain-of-function and loss-of-function mutants for functional characterization of ncRNA genes.
CRISPR/ Cas9, a new genome-editing technology, holds great potential for generating knockout and knock-in mutants in plants, as demonstrated in a range of plant species,and recently demonstrated in horticultural plant species, for example, Citrus sinensis,Malus pumila,Solanum lycopersicum and Solanum tuberosum.Compared with RNA inference that has several limitations such as incomplete gene knock-down and extensive off-target activities, CRISPR/Cas9 technology has the advantage of complete gene knockout with relatively ow off-target activities.In addition, the action of RNAi is restricted in cytoplasm where RNA-induced silencing complexes are located.However, many ncRNAs have been shown to be localized in the nucleus, which cannot be manipulated in similar manner using RNAi.Thus, CRISPR/Cas9 provides an efficient and effective alternative to RNAi for characterizing the function of ncRNAs. In fact, this new genome-editing technology has been used to knockout several ncRNAs in animals such as humans, mouse, zebrafish,as well as in plants such as soybean.Once the CRISPR/Cas9-mediated knockout and knock-in mutation is created, the NGS technology, mentioned above, can be used to profile the expression of target transcripts and other downstream genes in the biological pathways . After identification of the biological roles of ncRNAs, it is important to understand the molecular mechanism underlying these biological roles . Examination of the secondary structure of ncRNAs is informative in studying the function of ncRNAs at the molecular level. Several experimental approaches, such as selective 2′-hydroxyl acylation analyzed by primer extension , parallel analysis of RNA structure or dimethyl sulfate-modified RNA for sequencing , can be used for deciphering of the secondary structure of ncRNAs.To understand where and how the ncRNAs function, chromatin isolation by RNA purification , capture hybridization analysis of RNA targets , cross linking, ligation, sequencing of hybrids and cross linking IP have been developed to detect the interactions between ncRNAs and DNA, RNA or protein.Recently, Shechner et al.used CRISPR/ dCas9, based on a catalytically dead variant of Cas9, to deploy lncRNAs cargos to DNA loci by incorporating the cargo into the sgRNA, thus providing initial insights into the utility of CRISPR/dCas9 for studying the function of ncRNAs. Besides its potential for validating ncRNA prediction, ribosome profiling can also be used to unravel the function of ncRNAs. For example, using ribosome profiling, Guo et al.100 studied the effects of miRNAs on protein production from their target mRNAs and found that the destabilization of target mRNAs by the miRNAs is the predominant reason for reduced protein output. Similarly, Bazzini et al.101 studied the impact of miR430 on endogenous mRNAs in zebrafish using ribosome profiling and found that this sRNA reduced translation. These technologies provide new approaches for functional characterization of ncRNAs in horticultural plants.
The nitrogen limitation in arctic systems will, however, be perturbed by climate warming in at least two ways: warming will accelerate soil nitrogen mineralization in surface soils [Rustad et al., 2001; Schmidt et al., 2002] and release frozen nitrogen at the permafrost boundary as the active layer thickens [Keuper et al., 2012]. A global meta-analysis concluded that experimental warming enhanced surface soil net nitrogen mineralization by 46% [Rustad et al., 2001], with the highest enhancement in tundra ecosystems [Jonasson et al., 1993]. Globally, warming resulted in ~0.5 g N m 2 yr 1 additional nitrogen in the surface organic layer , which substantially stimulated plant growth and increased ecosystem carbon storage [Rustad et al., 2001]. Also, the soil that is expected to thaw at the permafrost boundary over the next decades may result in even more plant available nitrogen than from warming in the surface soil. In an study of subarctic peatland soils,hydroponic vertical farming system consistently higher net nitrogen mineralization and plant nitrogen uptake rates were observed near the permafrost boundary than in the current rooting zone [Keuper et al., 2012]. The elevated inorganic nitrogen supply at depth came from both direct release of frozen inorganic nitrogen and microbial decomposition of thawed organic nitrogen.Given that warming will alter nitrogen availability along the entire soil profile, vertically resolved 15N tracer experiments are needed to inform the vertical pattern of plant uptake. The prevailing hypothesis regarding the vertical pattern of plant uptake is that fine-root biomass density, as functionally absorptive tissues, exerts first-order controls on nutrient uptake [De Baets et al., 2007; Vamerali et al., 2003]. Therefore, the plant nitrogen uptake profile should follow the fine-root biomass density distribution . Evidence from some 15N tracer studies has been consistent with this idea. For example, grass [Bowman et al., 2002; Jumpponen et al., 2002; Xu et al., 2011] and crops [Andrews and Newman, 1970; Kristensen and Thorup-Kristensen, 2004] take up most soil nitrogen from soil layers where the rooting biomass density is the highest. In contrast, Liao et al. [2006] found that three wheat species take up more nitrogen from the middle soil layer than from the upper soil layer , although root biomass density was lower in the middle layer. For tundra ecosystems, the factors controlling plant nitrogen uptake have not been fully evaluated. Therefore, the first objective of this study was to quantify vertical patterns of nitrogen uptake for three dominant arctic tundra species using results from a vertically explicit 15N tracer field experiment. The observed vertical patterns of plant nitrogen uptake are complicated by soil nutrient competition, i.e., plants dynamically competing for nutrients with microbes [Schmidt et al., 2002] and abiotic processes [Jones et al., 2005; Petrone et al., 2006]. Microbial nitrogen demands are commonly higher in surface soils and lower in subsurface soils, due to strong energy limitation and temperature constraints in the deeper soil [Fontaine et al., 2007; Xu et al., 2013]. Therefore, roots growing in surface soils face potentially intense competition for nitrogen from microbes.
Plants could directly compete with microbial decomposers in surface soils by enabling highly efficient nutrient uptake systems, increasing fine-root density, developing more effective fine roots , or establishing mycorrhizal fungi associations [Kuzyakov and Xu, 2013; Miller and Cramer, 2005; Smith and Read, 2010]. Alternatively, plants can invest carbon to grow fine roots deeper in the soil to avoid direct competition with microbes in the surface soil [Iversen et al., 2011]. In other words, plant species’ distinct competitive functional traits, e.g., maximum rooting depths, root profiles, and uptake kinetics, will determine their vertical uptake pattern under the stress of microbial demand. To better understand the observed plant N uptake patterns, we used a recently developed model that mechanistically considers competitive interactions among fine roots, microbial decomposers, nitrifiers, denitrifiers, and mineral surfaces by applying the Equilibrium Chemistry Approximation [Tang and Riley, 2013; Zhu and Riley, 2015; Zhu et al., 2016]. Our second objective in this study was to use N-COM to understand the complicated competitive interactions and explain the observed plant nitrogen uptake patterns. Mechanistic consideration of plant-microbe competition is absent in current generation Earth System Models , which are used to predict coupling between terrestrial carbon dynamics and climate. Prevailing ESMs with prognostic N cycling did not explicitly account for root traits to represent plant competitiveness but rather applied either a “Relative Demand” [Thornton et al., 2007] or a “Microbes Win” [Gerber et al., 2010] concept. The Relative Demand concept hypothesizes that plant nutrient competitiveness is dependent on plant nutrient demand. Higher plant demand will lead to higher competitiveness, regardless of rooting conditions . The Microbes Win competition hypothesis assumes that microbes always get priority to access the available nutrient pool and plants use the leftover nutrients, without considering the uptake capacity of roots. In contrast, our ECA framework explicitly considers essential root traits and plant-microbe competition mechanism. Our third objective in this study was to evaluate the importance of root traits in modeling plant-microbe nutrient competition and vertical patterns of plant nutrient uptake, by comparing three plant-microbe competition hypotheses: Relative Demand, Microbes Win, and ECA against the 15N data. We hypothesize that models must mechanistically consider essential root traits that control plant-microbe competitive interactions to accurately estimate how much additional N plants can acquire under future warming.In late July 2013, replicate plots of the three dominant vascular plant species of interest were located in wet tundra near the intensively monitored NGEE Arctic sites . Prior to 15N addition, the initial above ground biomass of each species was determined by clipping all vascular material to the moss surface in plots that were 9 cm ff 9 cm . Vegetation was immediately processed in the laboratory, where it was sorted by species, green leaves, senesced leaves, and stems . Plant parts were oven dried at 70°C for more than 48 h, and the mass of living plant parts was summed to determine above ground biomass per unit ground area. After clipping, a soil core was manually collected to the permafrost boundary from the center of each plot. Cores were sectioned into 5 cm depth increments in the field .