Sorghum root epigenetic landscape during limiting phosphorus conditions

Constitutive basis of root system architecture: uncovering a promising trait for breeding nutrient- and drought-resilient crops

Root system architecture (RSA) plays a pivotal role in efficient uptake of essential nutrients, such as phosphorous (P), nitrogen (N), and water. In soils with heterogeneous nutrient distribution, root plasticity can optimize acquisition and plant growth. Here, we present evidence that a constitutive RSA can confer benefits for sorghum grown under both sufficient and limiting growth conditions. Our studies, using P efficient SC103 and inefficient BTx635 sorghum cultivars, identified significant differences in root traits, with SC103 developing a larger root system with more and longer lateral roots, and enhanced shoot biomass, under both nutrient sufficient and deficient conditions. In addition to this constitutive attribute, under P deficiency, both cultivars exhibited an initial increase in lateral root development; however, SC103 still maintained the larger root biomass. Although N deficiency and drought stress inhibited both root and shoot growth, for both sorghum cultivars, SC103 again maintained the better performance. These findings reveal that SC103, a P efficient sorghum cultivar, also exhibited enhanced growth performance under N deficiency and drought. Our results provide evidence that this constitutive nature of RSA can provide an avenue for breeding nutrient- and drought-resilient crops.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-023-00112-w.

Dynamic transcriptome analysis unravels key regulatory genes of maize root growth and development in response to potassium deficiency

MAIN CONCLUSION

Integrated root phenotypes and transcriptome analysis have revealed key candidate genes responsible for maize root growth and development in potassium deficiency. Potassium (K) is a vital macronutrient for plant growth, but our understanding of its regulatory mechanisms in maize root system architecture (RSA) and K+ uptake remains limited. To address this, we conducted hydroponic and field trials at different growth stages. K+ deficiency significantly inhibited maize root growth, with metrics like total root length, primary root length, width and maximum root number reduced by 50% to 80% during early seedling stages. In the field, RSA traits exhibited maximum values at the silking stage but continued to decline thereafter. Furthermore, K deprivation had a pronounced negative impact on root morphology and RSA growth and grain yield. RNA-Seq analysis identified 5972 differentially expressed genes (DEGs), including 17 associated with K+ signaling, transcription factors, and transporters. Weighted gene co-expression network analysis revealed 23 co-expressed modules, with enrichment of transcription factors at different developmental stages under K deficiency. Several DEGs and transcription factors were predicted as potential candidate genes responsible for maize root growth and development. Interestingly, some of these genes exhibited homology to well-known regulators of root architecture or development in Arabidopsis, such as Zm00001d014467 (AtRCI3), Zm00001d011237 (AtWRKY9), and Zm00001d030862 (AtAP2/ERF). Identifying these key genes helps to provide a deeper understanding of the molecular mechanisms governing maize root growth and development under nutrient deficient conditions offering potential benefits for enhancing maize production and improving stress resistance through targeted manipulation of RSA traits in modern breeding efforts.

Design, execution, and interpretation of plant RNA-seq analyses

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.