Both inorganic fertilizer inputs and crop yields have increased globally, with the concurrent increase in the pollution of water bodies due to nitrogen leaching from soils. Designing agroecosystems that are environmentally friendly is urgently required. Since agroecosystems are highly complex and consist of entangled webs of interactions between plants, microbes, and soils, identifying critical components in crop production remain elusive. To understand the network structure in agroecosystems engineered by several farming methods, including environmentally friendly soil solarization, we utilized a multiomics approach on a field planted withBrassica rapa. We found that the soil solarization increased plant shoot biomass irrespective of the type of fertilizer applied. Our multiomics and integrated informatics revealed complex interactions in the agroecosystem showing multiple network modules represented by plant traits heterogeneously associated with soil metabolites, minerals, and microbes. Unexpectedly, we identified soil organic nitrogen induced by soil solarization as one of the key components to increase crop yield. A germ-free plant in vitro assay and a pot experiment using arable soils confirmed that specific organic nitrogen, namely alanine and choline, directly increased plant biomass by acting as a nitrogen source and a biologically active compound. Thus, our study provides evidence at the agroecosystem level that organic nitrogen plays a key role in plant growth.

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Most plants show remarkable developmental plasticity in the generation of diverse types of new organs upon external stimuli, allowing them to adapt to their environment. Haustorial formation in parasitic plants is an example of such developmental reprogramming, but its molecular mechanism is largely unknown. In this study, we performed field-omics using transcriptomics and metabolomics to profile the molecular switch occurring in haustorial formation of the root parasitic plant, Thesium chinense, collected from its natural habitat. RNA-sequencing with de novo assembly revealed that the transcripts of very long chain fatty acid (VLCFA) biosynthesis genes, auxin biosynthesis/signaling-related genes and lateral root developmental genes are highly abundant in the haustoria. Gene co-expression network analysis identified a network module linking VLCFAs and the auxin-responsive lateral root development pathway. GC-TOF-MS analysis consistently revealed a unique metabolome profile with many types of fatty acids in the T. chinense root system, including the accumulation of a 25-carbon long chain saturated fatty acid in the haustoria. Our field-omics data provide evidence supporting the hypothesis that the molecular developmental machinery used for lateral root formation in non-parasitic plants has been co-opted into the developmental reprogramming of haustorial formation in the linage of parasitic plants.

One-sentence summary 31Three-dimensional image reconstruction was used to visualize the spatial organization of cell types in 32 the haustoria of parasitic plants with special reference to their interaction with host roots. 33 34 Author contributions 35 SC and SY conceived the idea of this study, designed the experiment, analyzed the data and wrote the 36 manuscript. NM was responsible for color coding, FE-SEM and 3-D reconstruction. YSu and YSa 37 2 developed methods to align section images, automated the color coding process, and provided crucial 38 technical assistance for 3-D reconstruction. YI, AS and KK assisted with image handling. KT, MW and 39 MS prepared serial thin sections and performed FE-SEM. KF analyzed the AtPEAR promoter. KS 40 provided critical comments on the manuscript. 41 42 Abstract 51 Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of 52 several cell types each of which has unique morphological features and physiological roles associated 53 with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance 54 in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of 55 haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice and 56 the facultative parasite Phtheirospermum japonicum infecting Arabidopsis. Our images reveal the 57 spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-58 D internal structures of haustoria highlight differences between the two parasites, particularly at the 59 xylem connection site with the host. Our study provides structural insights into how organs interact 60 between hosts and parasitic plants. 61 62 65 Approximately 4,500 species in 28 families, representing 1% of all angiosperm species, are proposed 66 to be parasitic plants (Westwood et al., 2010; Heide-Jørgensen, 2013). The common feature of parasitic 67 plants is the ability to form a specialized invasive organ called a haustorium. Unlike the unicellular 68 haustoria found in plant-infecting pathogenic fungi, haustoria in parasitic plants are multicellular organs 69 that function in host attachment, host tissue invasion and establishing a vascular connection between 70 the parasite and host for material transfer (Yoshida et al., 2016). Haustoria facilitate water and nutrient 71 acquisition as well as translocation of RNA molecules, peptides and plant hormones between the host 72 and parasite (Kim et al., 2014; Spallek et al., 2017; Shahid et al., 2018; Liu et al., 2019; Yoshida et al., 73 3 2019). Thus, haustoria act as efficient biological channels for interspecies material transfer, but the 74 internal structures and physiological functions of haustoria are largely unexplored. 75 The Orobanchaceae family contains the largest number of root parasitic species and present various 76 degrees of host dependency. An exception among Orobanchaceae family members is in the genus 77 L...

Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of several cell types, each of which has unique morphological features and physiological roles associated with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice (Oryza sativa) and the facultative parasite Phtheirospermum japonicum infecting Arabidopsis (Arabidopsis thaliana). In addition, field-emission scanning electron microscopy observation revealed the presence of various cell types in haustoria. Our images reveal the spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-D internal structures of haustoria highlight differences between the two parasites, particularly at the xylem connection site with the host. Our study provides cellular and structural insights into haustoria of S. hermonthica and P. japonicum and lays the foundation for understanding haustorium function.

Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plant science and agriculture. Here, we have increased sample handling efficiency in a two-step PCR amplification protocol for 16S rRNA gene sequencing of plant root microbiota, improving DNA extraction using AMPure XP magnetic beads and PCR purification using exonuclease. These modifications reduce sample handling and capture microbial diversity comparable to that obtained by the manual method. We found a buffer with AMPure XP magnetic beads enabled efficient extraction of microbial DNA directly from plant roots. We also demonstrated that purification using exonuclease before the second PCR step enabled the capture of higher degrees of microbial diversity, thus allowing for the detection of minor bacteria compared with the purification using magnetic beads in this step. In addition, our method generated comparable microbiome profile data in plant roots and soils to that of using common commercially available DNA extraction kits, such as DNeasy PowerSoil Pro Kit and FastDNA SPIN Kit for Soil. Our method offers a simple and high-throughput option for maintaining the quality of plant root microbial community profiling.

Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plant science and agriculture. Here, we have developed a novel high-throughput method based on a two-step PCR amplification protocol, involving DNA extraction using magnetic beads and PCR purification using exonuclease, for 16S rRNA gene amplicon sequencing of plant root microbiota. This method reduces sample handling and captures microbial diversity comparable to that obtained by the standard method. We found that using a buffer with magnetic beads enabled efficient extraction of microbial DNA directly from plant roots. In addition, we demonstrated that purification using exonuclease before the second PCR step enabled the capture of higher degrees of microbial diversity, thus allowing for the detection of minor bacteria compared with the purification using magnetic beads in this step. Our method offers a simple and high-throughput solution for maintaining the quality of plant root microbial community profiling.

Background The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the approach of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge. Results We propose a novel approach to improve the performance of the widely used DMM approach by combining three ideas: (i) we propose an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; (ii) to address the computational burden of high-dimensional microbiome data, we propose a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation; and (iii) we extend the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a variational parameter. Using the proposed method, stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment, the human gut microbiome data from three published datasets of large-scale case-control studies and the healthy human microbiome data from the Human Microbiome Project. Conclusions SVVS demonstrates a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1000 samples. Furthermore, a core set of representative microbial species is identified using SVVS that can improve the interpretability of Bayesian mixture models for a wide range of microbiome studies.