Rhizobia are soil bacteria that can establish a nitrogen-fixing symbiosis with legume plants. As horizontally transmitted symbionts, the life cycle of rhizobia includes a free-living phase in the soil and a plant-associated symbiotic phase. Throughout this life cycle, rhizobia are exposed to a myriad of other microorganisms that interact with them, modulating their fitness and symbiotic performance. In this review, we describe the diversity of interactions between rhizobia and other microorganisms that can occur in the rhizosphere, during the initiation of nodulation, and within nodules. Some of these rhizobia-microbe interactions are indirect, and occur when the presence of some microbes modifies plant physiology in a way that feeds back on rhizobial fitness. We further describe how these interactions can impose significant selective pressures on rhizobia and modify their evolutionary trajectories. More extensive investigations on the eco-evolutionary dynamics of rhizobia in complex biotic environments will likely reveal fascinating new aspects of this well-studied symbiotic interaction and provide critical knowledge for future agronomical applications.

Gynostemma pentaphyllum an important medicinal herb, can absorb high amounts of cadmium (Cd) which can lead to excessive Cd contamination during the production of medicines and tea. Hence, it is crucial to investigate the response mechanism of G. pentaphyllum under Cd stress to develop varieties with low Cd accumulation and high tolerance. Physiological response analysis, transcriptomics and metabolomics were performed on G. pentaphyllum seedlings exposed to Cd stress. Herein, G. pentaphyllum seedlings could significantly enhance antioxidant enzyme activities (POD, CAT and APX), proline and polysaccharide content subject to Cd stress. Transcriptomics analysis identified the secondary metabolites, carbohydrate metabolism, amino acid metabolism, lipid metabolism, and signal transduction pathways associated with Cd stress, which mainly involved the XTH, EXP and GST genes. Metabolomics analysis identified 126 differentially expressed metabolites, including citric acid, flavonoid and amino acids metabolites, which were accumulated under Cd stress. Multi-omics integrative analysis unraveled that the phenylpropanoid biosynthesis, starch, and sucrose metabolism, alpha-linolenic acid metabolism, and ABC transporter were significantly enriched at the gene and metabolic levels in response to Cd stress in G. pentaphyllum. In conclusion, the genetic regulatory network sheds light on Cd response mechanisms in G. pentaphyllum.

Biodiversity plays important roles in ecosystem functions and genetic diversity is a key component of biodiversity. While effects of genetic diversity on ecosystem functions have been extensively documented, no study has tested how genetic diversity of plants influences greenhouse gas fluxes from plant-soil systems. We assembled experimental populations consisting of 1, 4 or 8 genotypes of the clonal plant Hydrocotyle vulgaris in microcosms, and measured fluxes of CO₂ and CH₄ from the microcosms. The fluxes of CO₂ and CO₂ equivalent from the microcosms with the 1-genotype populations of H. vulgaris were significantly lower than those with the 4- and 8-genotype populations, and such an effect increased significantly with increasing the growth period. The cumulative CO₂ flux was significantly negatively related to the growth of the H. vulgaris populations. However, genotypic diversity did not significantly affect the flux of CH₄. We conclude that genotypic diversity of plant populations can influence CO₂ flux from plant-soil systems. The findings highlight the importance of genetic diversity in regulating greenhouse gas fluxes.

To address the challenges of insufficient accuracy in detecting tomato disease object detection caused by dense target distributions, large-scale variations, and poor feature information of small objects in complex backgrounds, this study proposes the tomato disease object detection method that integrates prior knowledge attention mechanism and multi-scale features (PKAMMF). Firstly, the visual features of tomato disease images are fused with prior knowledge through the prior knowledge attention mechanism to obtain enhanced visual features corresponding to tomato diseases. Secondly, a new feature fusion layer is constructed in the Neck section to reduce feature loss. Furthermore, a specialized prediction layer specifically designed to improve the model’s ability to detect small targets is incorporated. Finally, a new loss function known as A-SIOU (Adaptive Structured IoU) is employed to optimize the performance of the model in terms of bounding box regression. The experimental results on the self-built tomato disease dataset demonstrate the effectiveness of the proposed approach, and it achieves a mean average precision (mAP) of 91.96%, which is a 3.86% improvement compared to baseline methods. The results show significant improvements in the detection performance of multi-scale tomato disease objects.

Soybean (Glycine max) productivity is significantly reduced by drought stress. Breeders are aiming to improve soybean grain yields both under well-watered (WW) and drought-stressed (DS) conditions, however, little is known about the genetic architecture of yield-related traits. Here, a panel of 188 soybean germplasm was used in a genome wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers linked to yield-related traits including pod number per plant (PN), biomass per plant (BM) and seed weight per plant (SW). The SLAF-seq genotyping was conducted on the population and three phenotype traits were examined in WW and DS conditions in four environments. Based on best linear unbiased prediction (BLUP) data and individual environmental analyses, 39 SNPs were significantly associated with three soybean traits under two conditions, which were tagged to 26 genomic regions by linkage disequilibrium (LD) analysis. Of these, six QTLs qPN-WW19.1, qPN-DS8.8, qBM-WW1, qBM-DS17.4, qSW-WW4 and qSW-DS8 were identified controlling PN, BM and SW of soybean. There were larger proportions of favorable haplotypes for locus qPN-WW19.1 and qSW-WW4 rather than qBM-WW1, qBM-DS17.4, qPN-DS8.8 and qSW-DS8 in both landraces and improved cultivars. In addition, several putative candidate genes such as Glyma.19G211300, Glyma.17G057100 and Glyma.04G124800, encoding E3 ubiquitin-protein ligase BAH1, WRKY transcription factor 11 and protein zinc induced facilitator-like 1, respectively, were predicted. We propose that the further exploration of these locus will facilitate accelerating breeding for high-yield soybean cultivars.

Nitrogen (N) limits crop production, yet more than half of N fertilizer inputs are lost to the environment. Developing maize hybrids with improved N use efficiency can help minimize N losses and in turn reduce adverse ecological, economical, and health consequences. This study aimed to identify single nucleotide polymorphisms (SNPs) associated with agronomic traits (plant height, grain yield, and anthesis to silking interval) under high and low N conditions. A genome-wide association study (GWAS) was conducted using 181 doubled haploid (DH) lines derived from crosses between landraces from the Germplasm Enhancement of Maize (BGEM lines) project and two inbreds, PHB47 and PHZ51. These DH lines were genotyped using 62,077 SNP markers. The same lines from the per se trials were used as parental lines for the testcross field trials. Plant height, anthesis to silking interval, and grain yield were collected from high and low N conditions in three environments for both per se and testcross trials. We used three GWAS models, namely, general linear model (GLM), mixed linear model (MLM), and Fixed and Random model Circulating Probability Unification (FarmCPU) model. We observed significant genetic variation among the DH lines and their derived testcrosses. Interestingly, some testcrosses of exotic introgression lines were superior under high and low N conditions compared to the check hybrid, PHB47/PHZ51. We detected multiple SNPs associated with agronomic traits under high and low N, some of which co-localized with gene models associated with stress response and N metabolism. The BGEM panel is, thus, a promising source of allelic diversity for genes controlling agronomic traits under different N conditions.

CRISPR-Cas9, the “genetic scissors”, is being presaged as a revolutionary technology, having tremendous potential to create designer crops by introducing precise and targeted modifications in the genome to achieve global food security in the face of climate change and increasing population. Traditional genetic engineering relies on random and unpredictable insertion of isolated genes or foreign DNA elements into the plant genome. However, CRISPR-Cas based gene editing does not necessarily involve inserting a foreign DNA element into the plant genome from different species but introducing new traits by precisely altering the existing genes. CRISPR edited crops are touching markets, however, the world community is divided over whether these crops should be considered genetically modified (GM) or non-GM. Classification of CRISPR edited crops, especially transgene free crops as traditional GM crops, will significantly affect their future and public acceptance in some regions. Therefore, the future of the CRISPR edited crops is depending upon their regulation as GM or non-GMs, and their public perception. Here we briefly discuss how CRISPR edited crops are different from traditional genetically modified crops. In addition, we discuss different CRISPR reagents and their delivery tools to produce transgene-free CRISPR edited crops. Moreover, we also summarize the regulatory classification of CRISPR modifications and how different countries are regulating CRISPR edited crops. We summarize that the controversy of CRISPR-edited plants as GM or non-GM will continue until a universal, transparent, and scalable regulatory framework for CRISPR-edited plants will be introduced worldwide, with increased public awareness by involving all stakeholders.