The rice histone methylation regulates hub species of the root microbiota

Interkingdom multi-omics analysis reveals the effects of nitrogen application on growth and rhizosphere microbial community of Tartary buckwheat

Tartary buckwheat (Fagopyrum tataricum Gaertn.) is an important pseudocereal crop with excellent edible, nutritional and medicinal values. However, the yield of Tartary buckwheat (TB) is very low due to old-fashioned cultivation techniques, particularly unreasonable application of nitrogen fertilizer. To improve the understanding on the theories of nitrogen use in TB, the effects of nitrogen application on growth, as well as chemical properties and microbial community of rhizosphere soil were investigated in this study. Nitrogen application could promote the plant height, stem diameter, nitrogen accumulation and yield of TB. The relative abundance and diversity of bacteria and fungi in the rhizosphere soil of TB were improved by nitrogen fertilizer. Nitrogen application increased the abundance of beneficial bacteria such as Lysobacter and Sphingomonas in rhizosphere soil, and decreased the abundance of pathogenic fungi such as Fusarium and Plectosphaerella. The results indicated that nitrogen application changed the distribution of microbial communities in TB rhizosphere soil. Furthermore, the specific enriched or depleted microorganisms in the rhizosphere soil of four TB varieties were analyzed at OTU level. 87 specific nitrogen-responsive genes with sequence variation were identified in four varieties by integrating genomic re-sequencing and transcriptome analysis, and these genes may involve in the recruitment of specific rhizosphere microorganisms in different TB varieties. This study provided new insights into the effects of nitrogen application on TB growth and rhizosphere microbial community, and improved the understanding on the mechanisms of TB root-microbe interactions.

Host genetics regulate the plant microbiome

Plants recruit a taxonomically diverse microbial community, collectively termed the plant microbiome, that includes mutualists, pathogens, and commensals. These myriad microorganisms are robustly intertwined with their hosts and can determine plant fate by influencing fitness and growth or offering protection from detrimental bacteria, fungi, and herbivores. Recent studies have revealed significant effects of host genome diversity on plant-microbiome assembly and how host genetics determine microbiome composition, which is crucial for beneficial functions. The few host loci identified through genome-wide association studies suggest that genes involved in plant development, immunity, nutrient uptake, and root exudates regulate plant-microbiome community structure. Elucidating the role of host genetics in plant-microbiome assembly is key to understanding how plant-microbiome interactions are evolving and how to unlock the breeding and engineering potential of the microbiome for sustainable agriculture.

EasyAmplicon: An easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research

It is difficult for beginners to learn and use amplicon analysis software because there are so many software tools to choose from, and all of them need multiple steps of operation. Herein, we provide a cross-platform, open-source, and community-supported analysis pipeline EasyAmplicon. EasyAmplicon has most of the modules needed for an amplicon analysis, including data quality control, merging of paired-end reads, dereplication, clustering or denoising, chimera detection, generation of feature tables, taxonomic diversity analysis, compositional analysis, biomarker discovery, and publication-quality visualization. EasyAmplicon includes more than 30 cross-platform modules and R packages commonly used in the field. All steps of the pipeline are integrated into RStudio, which reduces learning costs, keeps the flexibility of the analysis process, and facilitates personalized analysis. The pipeline is maintained and updated by the authors and editors of WeChat official account "Meta-genome." Our team will regularly release the latest tutorials both in Chinese and English, read the feedback from users, and provide help to them in the WeChat account and GitHub. The pipeline can be deployed on various platforms, and the installation time is less than half an hour. On an ordinary laptop, the whole analysis process for dozens of samples can be completed within 3 h. The pipeline is available at GitHub (https://github.com/YongxinLiu/EasyAmplicon) and Gitee (https://gitee.com/YongxinLiu/EasyAmplicon).

Land use intensification in a dry-hot valley reduced the constraints of water content on soil microbial diversity and multifunctionality but increased CO2 production

Conversion of abandoned land (mainly savanna) into cropland generally occurs in fragile ecosystems such as dry-hot valleys (DHVs) in southwest China, with the intent of increasing land productivity and conducting ecological restoration. However, the effects of conversion on soil microbial communities and carbon turnover of savanna ecosystems remain unclear, since savannas could be a vital but overlooked carbon sink. To illustrate the ecological consequences of land-use change (LUC) for savanna ecosystems, a 1-year field experiment was conducted in DHVs of southwest China. The soil properties, microbial respiration, and metagenomics from two different land-use types (grassland and mango plantation) were examined to reveal the effects of regional LUC on soil C turnover and microbial traits. Conversion from degraded grassland into cropland increased the contribution of soil microclimate to the microbial community composition, reduced the constraints of soil water content (SWC), and further decreased nutrient availability. LUC reshaped the composition and structure of soil bacterial communities. Specifically, soil dominant microbes that belonged to Actinobacteria and Proteobacteria were significantly enriched by conversion, while rare microbes that belonged to a wider range of phyla were generally depleted, leading to an overall decrease in community diversity. In addition, LUC-induced changes in soil characteristics and microbial communities further decreased soil multifunctionality as well as the carbon use efficiency of microbes. Intensified microbial respiration and a significant increase in the soil CO₂ efflux were observed following LUC, which could drive changes in soil microbial community composition and functions (such as growth and regeneration). In summary, through simultaneously reducing constraints on SWC and decreasing nutrient availability, conversion from degraded grassland to cropland in a DHV decreased soil microbial diversity and multifunctionality, and increased microbial respiration and soil CO₂ efflux. Our study provides new insights for understanding the role and mechanisms of LUC in soil carbon turnover in ecologically fragile areas such as DHVs.

Dissecting Plant Gene Functions Using CRISPR Toolsets for Crop Improvement

The CRISPR-based gene editing technology has become more and more powerful in genome manipulation for agricultural breeding, with numerous improved toolsets springing up. In recent years, many CRISPR toolsets for gene editing, such as base editors (BEs), CRISPR interference (CRISPRi), CRISPR activation (CRISPRa), and plant epigenetic editors (PEEs), have been developed to clarify gene function and full-level gene regulation. Here, we comprehensively summarize the application and capacity of the different CRISPR toolsets in the study of plant gene expression regulation, highlighting their potential application in gene regulatory networks' analysis. The general problems in CRISPR application and the optimal solutions in the existing schemes for high-throughput gene function analysis are also discussed. The CRISPR toolsets targeting gene manipulation discussed here provide new solutions for further genetic improvement and molecular breeding of crops.

Spatiotemporal Heterogeneity and Intragenus Variability in Rhizobacterial Associations with Brassica rapa Growth

Microbial communities in the rhizosphere are distinct from those in soils and are influenced by stochastic and deterministic processes during plant development. These communities contain bacteria capable of promoting growth in host plants through various strategies. While some interactions are characterized in mechanistic detail using model systems, others can be inferred from culture-independent methods, such as 16S amplicon sequencing, using machine learning methods that account for this compositional data type. To characterize assembly processes and identify community members associated with plant growth amid the spatiotemporal variability of the rhizosphere, we grew Brassica rapa in a greenhouse time series with amended and reduced microbial treatments. Inoculation with a native soil community increased plant leaf area throughout the time series by up to 28%. Despite identifying spatially and temporally variable amplicon sequence variants (ASVs) in both treatments, inoculated communities were more highly connected and assembled more deterministically overall. Using a generalized linear modeling approach controlling for spatial variability, we identified 43 unique ASVs that were positively or negatively associated with leaf area, biomass, or growth rates across treatments and time stages. ASVs of the genus Flavobacterium dominated rhizosphere communities and showed some of the strongest positive and negative correlations with plant growth. Members of this genus, and growth-associated ASVs more broadly, exhibited variable connectivity in networks independent of growth association (positive or negative). These findings suggest host-rhizobacterial interactions vary temporally at narrow taxonomic scales and present a framework for identifying rhizobacteria that may work independently or in concert to improve agricultural yields.

IMPORTANCE The rhizosphere, the zone of soil surrounding plant roots, is a hot spot for microbial activity, hosting bacteria capable of promoting plant growth in ways like increasing nutrient availability or fighting plant pathogens. This microbial system is highly diverse and most bacteria are unculturable, so to identify specific bacteria associated with plant growth, we used culture-independent community DNA sequencing combined with machine learning techniques. We identified 43 specific bacterial sequences associated with the growth of the plant Brassica rapa in different soil microbial treatments and at different stages of plant development. Most associations between bacterial abundances and plant growth were positive, although similar bacterial groups sometimes had different effects on growth. Why this happens will require more research, but overall, this study provides a way to identify native bacteria from plant roots that might be isolated and applied to boost agricultural yields.