Microbial tRNA pieces regulate nodulation: To fix nitrogen, leguminous plants enter into a symbiotic relationship with nodulating bacteria. Ren et al. now reveal the bacteria as active regulators in this process (see the Perspective by Baldrich and Meyers). Small fragments cleaved from rhizobial tRNA molecules tap into the hosts' RNA interference machinery to silence key host genes. Thus, both host and microbe shape the symbiotic environment. Science , this issue p. 919 ; see also p. 868

Subgenome integrity in bread wheat (Triticum aestivum; BBAADD) makes possible the extraction of its BBAA component to restitute a novel plant type. The availability of such a ploidy-reversed wheat (extracted tetraploid wheat [ETW]) provides a unique opportunity to address whether and to what extent the BBAA component of bread wheat has been modified in phenotype, karyotype, and gene expression during its evolutionary history at the allohexaploid level. We report here that ETW was anomalous in multiple phenotypic traits but maintained a stable karyotype. Microarray-based transcriptome profiling identified a large number of differentially expressed genes between ETW and natural tetraploid wheat (Triticum turgidum), and the ETW-downregulated genes were enriched for distinct Gene Ontology categories. Quantitative RT-PCR analysis showed that gene expression differences between ETW and a set of diverse durum wheat (T. turgidum subsp durum) cultivars were distinct from those characterizing tetraploid cultivars per se. Pyrosequencing revealed that the expression alterations may occur to either only one or both of the B and A homoeolog transcripts in ETW. A majority of the genes showed additive expression in a resynthesized allohexaploid wheat. Analysis of a synthetic allohexaploid wheat and diverse bread wheat cultivars revealed the rapid occurrence of expression changes to the BBAA subgenomes subsequent to allohexaploidization and their evolutionary persistence.

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding.