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Although there have been numerous studies describing plant growth systems for root exudate collection, a common limitation is that these systems require disruption of the plant root system to facilitate exudate collection. Here, we present a newly designed semi-hydroponic system that uses glass beads as solid support to simulate soil impedance, which combined with drip irrigation, facilitates growth of healthy maize plants, collection and analysis of root exudates, and phenotyping of the roots with minimal growth disturbance or root damage.

This system was used to collect root exudates from seven maize genotypes using water or 1 mM CaCl₂, and to measure root phenotype data using standard methods and the Digital imaging of root traits (DIRT) software. LC–MS/MS (Liquid Chromatography—Tandem Mass Spectrometry) and GC–MS (Gas Chromatography—Mass Spectrometry) targeted metabolomics platforms were used to detect and quantify metabolites in the root exudates. Phytohormones, some of which are reported in maize root exudates for the first time, the benzoxazinoid DIMBOA (2,4-Dihydroxy-7-methoxy-1,4-benzoxazin-3-one), amino acids, and sugars were detected and quantified. After validating the methodology using known concentrations of standards for the targeted compounds, we found that the choice of the exudate collection solution affected the exudation and analysis of a subset of analyzed metabolites. No differences between collection in water or CaCl₂were found for phytohormones and sugars. In contrast, the amino acids were more concentrated when water was used as the exudate collection solution. The collection in CaCl₂required a clean-up step before MS analysis which was found to interfere with the detection of a subset of the amino acids. Finally, using the phenotypic measurements and the metabolite data, significant differences between genotypes were found and correlations between metabolites and phenotypic traits were identified.

A new plant growth system combining glass beads supported hydroponics with semi-automated drip irrigation of sterile solutions was implemented to grow maize plants and collect root exudates without disturbing or damaging the roots. The validated targeted exudate metabolomics platform combined with root phenotyping provides a powerful tool to link plant root and exudate phenotypes to genotype and study the natural variation of plant populations.

 

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.

 

MicroRNAs (miRNAs) are essential regulators of gene expression in metazoans and plants. In plants, most miRNAs are generated from primary miRNA transcripts (pri‐miRNAs), which are processed by the Dicer‐like 1 (DCL1) complex along with accessory proteins.

Serrate‐Associated Protein 1 (SEAP1), a conserved splicing‐related protein, has been studied in human and yeast. However, the functions of SEAP1 in plants remain elusive.

Lack ofSEAP1results in embryo lethality and knockdown ofSEAP1by an artificial miRNA (amiR^SEAP1) causes pleiotropic developmental defects and reduction in miRNA accumulation. SEAP1 associates with the DCL1 complex, and may promote the interaction of the DCL1 complexes with pri‐miRNAs. SEAP1 also enhances pri‐miRNA accumulation, but does not affect pri‐miRNA transcription, suggesting it may indirectly or directly stabilize pri‐miRNAs. In addition, SEAP1 affects the splicing of some pri‐miRNAs and intron retention of messenger RNAs at global levels.

Our findings uncover both conserved and novel functions of SEAP1 in plants. Besides the role as a splicing factor, SEPA1 may promote miRNA biogenesis by positively modulating pri‐miRNA splicing, processing and/or stability.

 

Rice, an important food resource, is highly sensitive to salt stress, which is directly related to food security. Although many studies have identified physiological mechanisms that confer tolerance to the osmotic effects of salinity, the link between rice genotype and salt tolerance is not very clear yet. Association of gene co‐expression network and rice phenotypic data under stress has penitential to identify stress‐responsive genes, but there is no standard method to associate stress phenotype with gene co‐expression network. A novel method for integration of gene co‐expression network and stress phenotype data was developed to conduct a system analysis to link genotype to phenotype. We applied aLASSO‐based method to the gene co‐expression network of rice with salt stress to discover key genes and their interactions for salt tolerance‐related phenotypes. Submodules in gene modules identified from the co‐expression network were selected by theLASSOregression, which establishes a linear relationship between gene expression profiles and physiological responses, that is, sodium/potassium condenses under salt stress. Genes in these submodules have functions related to ion transport, osmotic adjustment, and oxidative tolerance. We argued that these genes in submodules are biologically meaningful and useful for studies on rice salt tolerance. This method can be applied to other studies to efficiently and reliably integrate co‐expression network and phenotypic data.

 

Plant seed lipid metabolism is an area of intensive research, including many examples of transgenic events in which oil composition has been modified. In the selected examples described in this review, progress towards the predictive manipulation of metabolism and the reconstitution of desired traits in a non‐native host is considered. The advantages of a particular oilseed crop,Camelina sativa, as a flexible and utilitarian chassis for advanced metabolic engineering and applied synthetic biology are considered, as are the issues that still represent gaps in our ability to predictably alter plant lipid biosynthesis. Opportunities to deliver useful bio‐based products via transgenic plants are described, some of which represent the most complex genetic engineering in plants to date. Future prospects are considered, with a focus on the desire to transition to more (computationally) directed manipulations of metabolism.

 

Like metazoans, plants use small regulatoryRNAs (sRNAs) to direct gene expression. Several classes ofsRNAs, which are distinguished by their origin and biogenesis, exist in plants. Among them, microRNAs (miRNAs) andtrans‐acting small interferingRNAs (ta‐siRNAs) mainly inhibit gene expression at post‐transcriptional levels. In the past decades, plant miRNAs and ta‐siRNAs have been shown to be essential for numerous developmental processes, including growth and development of shoots, leaves, flowers, roots and seeds, among others. In addition, miRNAs and ta‐siRNAs are also involved in the plant responses to abiotic and biotic stresses, such as drought, temperature, salinity, nutrient deprivation, bacteria, virus and others. This review summarizes the roles of miRNAs and ta‐siRNAs in plant physiology and development.

 

Abstract Rapid development of transcriptome sequencing technologies has resulted in a data revolution and emergence of new approaches to study transcriptomic regulation such as alternative splicing, alternative polyadenylation, CRISPR knockout screening in addition to the regular gene expression. A full characterization of the transcriptional landscape of different groups of cells or tissues holds enormous potential for both basic science as well as clinical applications. Although many methods have been developed in the realm of differential gene expression analysis, they all geared towards a particular type of sequencing data and failed to perform well when applied in different types of transcriptomic data. To fill this gap, we offer a negative beta binomial t-test (NBBt-test). NBBt-test provides multiple functions to perform differential analyses of alternative splicing, polyadenylation, CRISPR knockout screening, and gene expression datasets. Both real and large-scale simulation data show superior performance of NBBt-test with higher efficiency, and lower type I error rate and FDR to identify differential isoforms and differentially expressed genes and differential CRISPR knockout screening genes with different sample sizes when compared against the current very popular statistical methods. An R-package implementing NBBt-test is available for downloading from CRAN ( https://CRAN.R-project.org/package=NBBttest ). 

The root-associated microbiome (rhizobiome) affects plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here, we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize ( Zea mays L .) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We analyze the maize rhizobiome in terms of 150 abundant and consistently reproducible microbial groups and we show that the abundance of many root-associated microbes is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in -N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association study was conducted using the abundance of microbial groups as rhizobiome traits, and n=622 plant loci were identified that are linked to the abundance of n=104 microbial groups in the maize rhizosphere. In 62/104 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. We provide comprehensive datasets about the three-way interaction of host genetics, microbe abundance, and plant performance under two N treatments to facilitate targeted experiments toward harnessing the full potential of root-associated microbial symbionts in maize production.