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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.

Establishing an effective way to propagate a wide range of arbuscular mycorrhizal (AM) fungi species is desirable for mycorrhizal research and agricultural applications. Although the success of mycorrhizal formation is required for spore production of AM fungi, the critical factors for its construction in the in vitro monoxenic culture protocol remain to be identified. In this study, we evaluated the growth of hairy roots from carrot, flax, and chicory, and investigated the effects of the phosphorus (P) concentration in the mother plate, as well as the levels of P, sucrose, and macronutrients in a cocultivation plate with a hairy root, amount of medium of the cocultivation plate, and location of spore inoculation, by utilizing the Bayesian information criterion model selection with greater than 800 units of data. We found that the flax hairy root was suitable for in vitro monoxenic culture, and that the concentration of P in the cocultivation plate was a critical factor for mycorrhizal formation. We showed that an extremely low concentration of P (3 μM) significantly improved mycorrhizal formation for AM fungi belonging to the Glomerales order, while a high concentration of P (30 μM) was suitable for Diversisporales fungi. Therefore, we anticipate that the refining the P concentration will contribute to future culture collections of a wide range of AM fungi.

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.

A fungal endophyte, Ramichloridium cerophilum , was identified as a Class 2 endophytes species obtained from the leaf of common sowthistle ( Sonchus oleraceus L). This fungus was found to grow endophylically in the roots of Chinese cabbage seedlings. Light microscopy of cross-sections of colonized Chinese cabbage roots showed that the fungus penetrates through the outer epidermal cells, grows further into the inner cortex, and colonizes the cortical tissue. Isolates of R. cerophilum 28L-9 have shown the ability to increase biomass of a nonmycorrhizal plant, Chinese cabbage. This finding is the first report of R. cerophilum could help to improve nonmycorrhizal plant. Keywords: Ramichloridium cerophilum , endophytic fungus, Chinese cabbage, plant growth promoting

A novel species of Cladophialophora is herein described from the natural environment of secondary forest soil in Japan, which was able to be colonized by the host plant root. Morphological observations indicated that the isolate is distinct from previously identified species, and, thus, is described as the new species, C. inabaensis sp. nov.