Plant cells accumulate small RNA molecules that regulate plant development, genome stability, and environmental responses. These small RNAs fall into three major classes based on their function and mechanisms of biogenesis—microRNAs, heterochromatic small interfering RNAs, and secondary small interfering RNAs—plus several other less well-characterized categories. Biogenesis of each small RNA class requires a pathway of factors, some specific to each pathway and others involved in multiple pathways. Diverse sequenced plant genomes, along with rapid developments in sequencing, imaging, and genetic transformation techniques, have enabled significant progress in understanding the biogenesis, functions, and evolution of plant small RNAs, including those that had been poorly characterized because they were absent or had low representation in Arabidopsis (Arabidopsis thaliana). Here, we review recent findings about plant small RNAs and discuss our current understanding of their biogenesis mechanisms, targets, modes of action, mobility, and functions in Arabidopsis and other plant species, including economically important crops.
Salt-Tolerant Crops: Time to Deliver
Despite the numerous advances made in our understanding of the physiology and molecular genetics of salinity tolerance, there have been relatively few applications of these to improve the salt tolerance of crops. The most significant advances have historically utilized intraspecific variation, introgression of traits from close crop wild relatives, or, less frequently, introgression from more distant relatives. Advanced lines often fail due to difficulties in the introgression or tracking of traits or due to yield penalties associated with the alleles in nonsaline environments. However, the greatest limitation is that salinity is not a primary trait for breeders. We must close the gap between research and delivery, especially for farmers who have precious few alternatives. These efforts should include a reassessment of old techniques such as grafting current crops with salt-tolerant hybrid rootstocks. Alternatively, future crops can be produced via domestication of salt-tolerant wild species—an approach that is now feasible in our lifetime.
Decoding the Auxin Matrix: Auxin Biology Through the Eye of the Computer
The plant hormone auxin is certainly the most studied developmental regulator in plants. The many functions of auxin during development, from the embryo to the root and shoot construction, are mediated by an ever-growing collection of molecular regulators, with an overwhelming degree of both ubiquity and complexity that we are still far from fully understanding and that biological experiments alone cannot grasp. In this review, we discuss how bioinformatics and computational modeling approaches have helped in recent years to explore this complexity and to push the frontiers of our understanding of auxin biology. We focus on how analysis of massive amounts of genomic data and construction of computational models to simulate auxin-regulated processes at different scales have complemented wet experiments to increase the understanding of how auxin acts in the nucleus to regulate transcription and how auxin movement between cells regulates development at the tissular scale.
Between-Plant Signaling
Parasitic plants use a special organ, the haustorium, to attach to and penetrate host tissues, forming phloem and/or xylem fusion with the host vascular systems. Across this haustorium–host interface, not only water and nutrients are extracted from the host by the parasitic plant, but also secondary metabolites, messenger RNAs, noncoding RNAs, proteins, and systemic signals are transported between the parasite and host and even among different hosts connected by a parasite. Furthermore, mycorrhizal fungi can form common mycelial networks (CMNs) that simultaneously interconnect multiple plants. Increasing lines of evidence suggest that CMNs can function as conduits, transferring stress-related systemic signals between plants. Between-plant signaling mediated by haustoria and CMNs likely has a profound impact on plant interactions with other organisms and adaptation to environmental factors. Here, we summarize the findings regarding between-plant transfer of biomolecules and systemic signals and the current understanding of the physiological and ecological implications of between-plant signaling.
Functional identification of OsMADS6 and OsMADS17 in floral organ development in rice
Abstract
Rice floral organs are closely related to rice yield and quality, and MADS family genes play an important role in rice floral organ development. In this study, we identified functions of two MADS genes in floral organ development by bioinformatics analysis and CRISPR/Cas9 technology. Our results showed that MADS6 and MADS17 were a pair of highly homologous genes, which had highly similar sequences, motifs and expression profiles. The floral organs of mads6 and mads6/17 mutants were abnormal, whereas mads17 mutants showed no difference with wild type. Collectively, these results reveal that there are functional differentiations between conserved gene MADS6 and MADS17. Our findings provide a theoretical basis for further analysing the function of MADS family homologous genes and unravelling their molecular mechanism and regulatory network.
Proteomic analysis of near‐isogenic lines reveals key biomarkers on wheat chromosome 4B conferring drought tolerance
Abstract
Drought is a major constraint for wheat production that is receiving increased attention due to global climate change. This study conducted isobaric tags for relative and absolute quantitation proteomic analysis on near-isogenic lines to shed light on the underlying mechanism of qDSI.4B.1 quantitative trait loci (QTL) on the short arm of chromosome 4B conferring drought tolerance in wheat. Comparing tolerant with susceptible isolines, 41 differentially expressed proteins were identified to be responsible for drought tolerance with a p-value of < 0.05 and fold change >1.3 or <0.7. These proteins were mainly enriched in hydrogen peroxide metabolic activity, reactive oxygen species metabolic activity, photosynthetic activity, intracellular protein transport, cellular macromolecule localization, and response to oxidative stress. Prediction of protein interactions and pathways analysis revealed the interaction between transcription, translation, protein export, photosynthesis, and carbohydrate metabolism as the most important pathways responsible for drought tolerance. The five proteins, including 30S ribosomal protein S15, SRP54 domain-containing protein, auxin-repressed protein, serine hydroxymethyltransferase, and an uncharacterized protein with encoding genes on 4BS, were suggested as candidate proteins responsible for drought tolerance in qDSI.4B.1 QTL. The gene coding SRP54 protein was also one of the differentially expressed genes in our previous transcriptomic study.
Effects of drought stress on the oxidation of the reaction center chlorophyll of photosystem I and grain yield in paddy-field grown rice plants
Multi‐trait genomic selection improves the prediction accuracy of end‐use quality traits in hard winter wheat
Abstract
Improvement of end-use quality remains one of the most important goals in hard winter wheat (HWW) breeding. Nevertheless, the evaluation of end-use quality traits is confined to later development generations owing to resource-intensive phenotyping. Genomic selection (GS) has shown promise in facilitating selection for end-use quality; however, lower prediction accuracy (PA) for complex traits remains a challenge in GS implementation. Multi-trait genomic prediction (MTGP) models can improve PA for complex traits by incorporating information on correlated secondary traits, but these models remain to be optimized in HWW. A set of advanced breeding lines from 2015 to 2021 were genotyped with 8725 single-nucleotide polymorphisms and was used to evaluate MTGP to predict various end-use quality traits that are otherwise difficult to phenotype in earlier generations. The MTGP model outperformed the ST model with up to a twofold increase in PA. For instance, PA was improved from 0.38 to 0.75 for bake absorption and from 0.32 to 0.52 for loaf volume. Further, we compared MTGP models by including different combinations of easy-to-score traits as covariates to predict end-use quality traits. Incorporation of simple traits, such as flour protein (FLRPRO) and sedimentation weight value (FLRSDS), substantially improved the PA of MT models. Thus, the rapid low-cost measurement of traits like FLRPRO and FLRSDS can facilitate the use of GP to predict mixograph and baking traits in earlier generations and provide breeders an opportunity for selection on end-use quality traits by culling inferior lines to increase selection accuracy and genetic gains.