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Results for “Yield penalty”

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8 recordsLinked to original sources
PubMed2026

Balancing growth and immunity of potato by humidity-dependent expression of a late blight resistance gene.

Inducible expression of resistance genes is an effective approach to balance plant growth and immunity, thus facilitating the development of disease-resistant crop cultivars. While pathogen-responsive and immunity-related promoters have been adopted for this purpose, alternative design strategies remain to be explored. High relative humidity (RH) has been recognized as a crucial permissive environmental condition for the occurrence of devastating plant diseases including tomato and potato late blight. Here, we identified humidity-activated cis-regulatory elements (HAEs) in Solanum lycopersicum through an integrative analysis of transcriptomics and chromatin accessibility data. Sequence homology-inferred HAEs in S. tuberosum can predict humidity-elicited changes in downstream gene expression. Transgenic S. tuberosum lines expressing a late blight resistance gene driven by an artificial humidity-inducible promoter containing a natural S. tuberosum HAE were generated. These transgenic lines exhibited comparable late blight resistance levels to the lines overexpressing the same resistance gene in controlled zoospore inoculation bioassays, while avoiding growth suppression and tuber yield penalties in common garden experiments. Our findings highlight the importance of plant cis-regulatory elements in the transcriptional responses to high RH and provide a proof-of-concept for a humidity-inducible environment-responsive resistance gene deployment strategy to engineer disease-resistant crop cultivars without compromising growth and yield.

Phytophthora infestans↗
PubMed2026

Hormone priming and metabolic engineering of phytohormone crosstalk in rice under combined biotic and abiotic stresses: a multi-omics perspective for climate-resilient crop development.

Rice (Oryza sativa L.) is the caloric backbone for more than half of humanity, yet it remains one of the most vulnerable crops to the simultaneous biotic and abiotic stresses exacerbated by climate change. Phytohormone priming and the complex crosstalk networks governed by transcription factor hubs like WRKY, MYB, and NAC serve as the central adaptive mechanism for stress resilience. This review synthesizes how multi-omics integration, including spatial and single-cell transcriptomics, is resolving the molecular architecture of hormonal priming and epigenetic stress memory. We critically evaluate advanced metabolic engineering and genome-editing strategies such as CRISPR-Cas9, base/prime editing, and synthetic gene circuits that enable precision modifications to decouple stress tolerance from historical yield penalties. Furthermore, we discuss the emerging roles of microbiome-assisted priming via synthetic consortia and the application of artificial intelligence and digital twins (continuously updated computational models of crop physiology) for predictive stress management. By integrating these diverse technological pillars, we propose a systems-level roadmap for developing climate-resilient rice cultivars capable of maintaining yield stability across a volatile combinatorial stress landscape. This synthesis provides a framework for translating mechanistic hormonal insights into field-applicable cultivars to ensure global food security.

CRISPR↗
PubMed2025

Inactivation of β-1,3-glucan synthase-like 5 confers broad-spectrum resistance to Plasmodiophora brassicae pathotypes in cruciferous plants.

Clubroot disease, caused by the obligate intracellular rhizarian protist Plasmodiophora brassicae, is devastating to cruciferous crops worldwide. Widespread field P. brassicae pathotypes frequently overcome the pathotype-specific resistance of modern varieties, posing a challenge for durable control of this disease. Here a genome-wide association study of 3 years of data comprising field clubroot phenotyping of 244 genome-resequenced Brassica napus accessions identified a strong association of β-1,3-glucan synthase-like 5 (GSL5) with clubroot susceptibility. GSL5 was evolutionarily conserved, and inactivation of GSL5 by genome editing in Arabidopsis, B. napus, Brassica rapa and Brassica oleracea conferred broad-spectrum, high-level resistance to P. brassicae pathotypes without yield penalties in B. napus. GSL5 inactivation derepressed the jasmonic acid-mediated immunity during P. brassicae secondary infection, and this immune repression was possibly reinforced through stabilization of GSL5 by a P. brassicae effector, facilitating clubroot susceptibility. Our study provides durable resistance resources for cruciferous clubroot disease control and insights into plant resistance against intracellular eukaryotic phytopathogens.

Disease Resistance↗
PubMed2026

The Wild Soybean C3HC4-Type RING Zinc-Finger Protein ZFP4 Enhances Resistance to Soybean Mosaic Virus.

Soybean [Glycine max (L.) Merr.] is a globally important source of protein and edible oil, but is severely threatened by soybean mosaic virus (SMV). Wild soybean [Glycine soja Sieb. & Zucc.], the wild ancestor of cultivated soybean, exhibits high genetic diversity and strong resistance to pathogens. In this study, we identified a novel SMV resistance locus RSC7-4 and its candidate gene ZFP4 from wild soybean, encoding a C3HC4-type RING zinc-finger protein. The knockout mutants of ZFP4 showed enhanced susceptibility to SMV strains SC7 and SC3, while its overexpressing lines conferred resistance without yield penalty; ZFP4 mediates resistance by inhibiting GSTT1 to increase glutathione and reduce excessive reactive oxygen species accumulation. Domestication analysis revealed reduced genetic diversity of ZFP4 in cultivated soybean, with the resistant ZFP4Hap1 underutilized in breeding. In summary, this study provides not only excellent genetic resources for SMV-resistant soybean breeding but also new insights into the regulatory mechanisms of soybean resistance to SMV.

ZFP4↗
PubMed2026

miR9772, a Triticum-specific miRNA involved in regulating wheat salt tolerance and grain size.

Salt stress severely impairs crop productivity worldwide. MicroRNAs (miRNAs) are a class of endogenous small noncoding RNAs, which played the crucial role in regulating plant growth, development as well as stress responses at the posttranscriptional level. However, the significance of miRNA on salt response in wheat is not well understood at present. In this study, we identified a salt-responsive miRNA from wild emmer wheat, miR9772, which appears to be specific to Triticum species. Under salt stress, the expression of miR9772 was significantly induced and upregulated. Functional analyses revealed that overexpression of miR9772 increased salt sensitivity in wheat, whereas silencing of miR9772 using Short Tandem Target Mimic (STTM) technology markedly enhanced salt tolerance, demonstrated its crucial role in regulating wheat's salt response. Furthermore, we revealed that miR9772 could target on CYP76C4 to decline its expression abundance to affect wheat's salt resistance. Additionally, agronomic and yield-related traits of transgenic wheat lines based on field experiments showed that miR9772-silenced lines exhibited larger grain size and higher grain yield per plant, indicating that miR9772 simultaneously regulated the salt tolerance and grain development. Collectively, this study provided a new target for improving wheat salt tolerance without yield penalty through genome editing breeding.

Triticum↗
PubMed2026

Beyond the salt barrier: CRISPR-mediated DNA reprogramming to uncouple yield from tolerance in Rice: A review.

Rice (Oryza sativa L.) feeds half of humanity, yet its cultivation is increasingly threatened by soil salinization, which now affects 1.4 billion hectares globally. Decades of breeding and engineering have focused on Na+ exclusion, principally through the Saltol QTL and the xylem-unloading transporter OsHKT1;5, yet this strategy has reached a physiological ceiling. Excluder genotypes survive salinity but fail to fill grain, because the ATP-intensive cost of continuous ion extrusion starves reproductive sinks, while ABA-mediated stomatal closure imposes chronic carbon limitation. The resulting "survival-yield gap" exposes a fundamental flaw in single-trait approaches to a polygenic stress. In this review, we argue that durable, yield-stable salt tolerance requires a coordinated systems-level intervention spanning five mechanistic tiers: (i) CRISPR/Cas9-mediated removal of negative regulatory brakes (OsRR22, RST1, PC1) that suppress plant's latent stress-adaptive capacity; (ii) reinforcement of actin-myosin cytoskeletal transport to sustain SOS1, NHX1, and HKT1;5 delivery under ionic stress; (iii) importation of halophyte design principles from Oryza coarctata, including salt gland architecture and superior Na+ compartmentalization; (iv) recalibration of the ROS-photosynthesis axis via the DHHC09-STRK1-CatC molecular switch and stomatal density engineering; and (v) pyramiding these modules into a "Salt-Shield Rice" genotype through multiplex editing, marker-assisted introgression, speed breeding, and genomic selection. We propose a phased ten-year roadmap that integrates synthetic biology circuit design with conventional breeding to deliver field-ready, multi-module varieties with greater than 70% yield stability at 8-10 dS m-1. This remains an aspirational design target rather than a demonstrated outcome, as three of the five tiers-halophyte-derived structural traits, cytoskeletal reinforcement, and full multi-module pyramiding-remain unvalidated in rice.

CRISPR/Cas9↗
PubMed2026

Molecular Bases and Genetic Design of Rice Disease Resistance for Optimized Yield and Sustainable Agriculture.

Rice diseases continue to undermine yield stability and threaten the sustainability of rice production. The central challenge is therefore not simply to maximize immune activation, but to identify genetic interventions that remain effective across diverse pathogen races and environmental conditions without imposing excessive penalties on growth or yield. Here, we synthesize the molecular basis of rice immunity from a design-oriented perspective. We first examine cell-surface pattern-recognition receptors and intracellular nucleotide-binding leucine-rich repeat receptors, and then assess the shared signaling hubs and defence outputs that connect pathogen perception to antimicrobial responses. Rather than treating these components as equivalent breeding targets, we compare their translational potential according to resistance spectrum, anticipated durability, tunability, pleiotropic risk, and the strength of field evidence. We further discuss breeding strategies based on receptor engineering, editing of susceptibility genes and cis-regulatory elements, post-translational motif engineering, pathogen-inducible and upstream open reading frame-mediated regulation, resistance-gene stacking and artificial intelligence-assisted prediction. We argue that rational resistance design in rice should move beyond constitutive immune activation toward allele-specific, quantitative, spatially restricted and infection-responsive regulation. Integrating mechanistic insights with precision genome editing, accelerated breeding and responsible deployment offers a practical route to durable, yield-compatible disease resistance while reducing dependence on chemical control.

breeding strategy↗
PubMed2025

A novel high-dimensional model for identifying regional DNA methylation QTLs.

Varying coefficient models offer the flexibility to learn the dynamic changes of regression coefficients. Despite their good interpretability and diverse applications, in high-dimensional settings, existing estimation methods for such models have important limitations. For example, we routinely encounter the need for variable selection when faced with a large collection of covariates with nonlinear/varying effects on outcomes, and no ideal solutions exist. One illustration of this situation could be identifying a subset of genetic variants with local influence on methylation levels in a regulatory region. To address this problem, we propose a composite sparse penalty that encourages both sparsity and smoothness for the varying coefficients. We present an efficient proximal gradient descent algorithm that scales to high-dimensional predictor spaces, providing sparse solutions for the varying coefficients. A comprehensive simulation study has been conducted to evaluate the performance of our approach in terms of estimation, prediction and selection accuracy. We show that the inclusion of smoothness control yields much better results over sparsity-only approaches. An adaptive version of the penalty offers additional performance gains. We further demonstrate the utility of our method in identifying regional mQTLs from asymptomatic samples in the CARTaGENE cohort. The methodology is implemented in the R package sparseSOMNiBUS, available on GitHub.

Humans↗