In the present study, we identified 10 significant single nucleotide polymorphisms (SNPs), 3 stable quantitative trait loci (QTLs) and 3 potential candidate genes associated with peanut kernel resistance to Aspergillus flavus stress. Peanut (Arachis hypogaea L.) is highly susceptible to A. flavus infection, producing highly carcinogenic aflatoxins. Breeding resistant varieties is an effective and sustainable approach to address this issue, and identifying novel genetic sources and loci underlying resistance is crucial. In this study, 353 A. hypogaea accessions were evaluated for resistance to A. flavus infection and aflatoxin production across three environments, leading to the identification of 13 accessions with stable resistance to both infection and aflatoxin production. A genome-wide association study (GWAS) was performed by integrating phenotypic data from multiple environments with 935,231 SNP markers, resulting in the detection of 10 significant marker-trait associations (MTAs). Of these, one showed consistent association with infection resistance in at least two out of three environments, and two were associated with resistance to both aflatoxin B1 (AFB1) and aflatoxin B2 (AFB2) production. Based on linkage disequilibrium (LD) decay estimates (70 kb upstream and downstream of significant SNPs), three novel stable QTLs were identified on chromosomes A03 and A05, designated qII_A05, qAF_A03, and qAF_A05, and 13 candidate genes were initially selected within these QTL regions. Haplotype analysis further validated 3 key candidate genes: Arahy.7046BI.1 (encoding a PH/START domain-containing protein), Arahy.TX2FLU.1 (encoding a receptor-like kinase), and Arahy.ASR4JM.1 (encoding a TIR-NBS-LRR class disease resistance protein). These genes exhibited significant haplotype-trait associations, with favorable haplotypes showing high frequencies (85.43

High-temperature (HT) stress impairs soybean yield by disrupting anther function. miRNAs are critical regulators of plant stress responses, but their roles in soybean anther thermotolerance remain largely unexplored. We performed small RNA sequencing of anthers from HT-tolerant (JD21) and HT-sensitive (HD14) soybean lines under HT and control conditions. Comparative analysis revealed that HT- stress JD21 anthers (TJA) exhibited 16 up-regulated and 19 down-regulated differentially expressed miRNAs (DEMs) compared to controls (CJA), while HT-stress HD14 anthers (THA) showed 17 up-regulated and 24 down-regulated DEMs relative to its control (CHA). The HD14 exhibited more DEMs, potentially explaining JD21 ' s superior HT resistance. Integrated analysis of miRNA and mRNA expression identified 23 DEMs with targeted regulatory relationships to 43 differentially expressed genes (DEGs) (p<0.05). Bioinformatics analysis indicated that these target genes were primarily enriched in metabolic and cellular processes, response to stimuli, calvin cycle carbon fixation, and carbon metabolism. From this group, eight candidate miRNAs, including novel-m0226-5p (miR226-5p), miR5037c, and miR159e-5p, were selected for further investigation. Functional validation in Arabidopsis thaliana demonstrated that overexpression of miR226-5p, miR5037c, or miR159e-5p resulted in premature anthers non-dehiscence under HT stress, respectively. Furthermore, miR159e-5p overexpression lines specifically showed significantly reduced expression of heat shock transcription factors (HSFA1s) and heat shock proteins (HSPs), supporting a role for this miRNA in regulating thermotolerance. This study establishes as a central regulator of the soybean anther HT response via modulation of the HSFA1s-HSPs network, providing molecular insights into thermotolerance and potential targets for breeding HT-resistant soybean varieties.

BACKGROUND:Pyroptosis, a pro-inflammatory form of regulated cell death mediated by gasdermin pore formation and typically triggered by inflammasome activation, has been increasingly recognized as an important contributor to liver inflammation and fibrosis in metabolic dysfunction-associated steatohepatitis (MASH). Despite accumulating evidence linking pyroptosis to MASH pathogenesis, the diagnostic value of pyroptosis-related genes in this disease remains largely undefined. Therefore, the present study aims to identify key pyroptosis-associated molecular signatures with potential utility for the diagnosis of MASH. METHODS:Transcriptomic datasets and corresponding clinical information for MASH patients and healthy individuals were retrieved from the Gene Expression Omnibus (GEO) database. Differential expression analysis using the Limma package, followed by pathway enrichment analyses, was conducted to identify pyroptosis-related genes associated with MASH. Machine learning approaches were applied to systematically screen for core pyroptosis-associated markers and construct predictive models for MASH diagnosis. The robustness of selected gene signatures was further validated in independent datasets and in vivo animal models and vitro cellular models. Prognostic risk assessment was performed using a nomogram informed by key pyroptosis-related genes. Additionally, molecular subtyping of MASH based on pyroptosis gene expression profiles was explored to delineate disease heterogeneity. RESULTS:Through integrative bioinformatics and machine learning, five principal pyro-related genes-LPL, FABP4, STMN2, AKR1B10 and EEF1A2-were identified in MASH. Validation studies in animal model and cell culture systems confirmed the differential expression patterns of these genes. Among evaluated algorithms, Random Forest achieved the highest AUC (0.957) for diagnostic performance. All the five symbols were subsequently included in logistic regression and nomogram models, both demonstrating strong predictive value for MASH diagnosis. Molecular subtyping uncovered substantial variation in pyroptosis gene signatures, immune microenvironment characteristics, and pathway enrichment across MASH subgroups. CONCLUSION:This study highlights the relevance of pyroptosis-related gene signatures in MASH, providing a basis for enhanced diagnostic accuracy and paving the way for individualized therapeutic interventions targeting disease subtypes.

Phomopsis seed decay (PSD) resistance in soybean is commonly evaluated using inoculation-based phenotyping, which is destructive and labor-intensive. Therefore, this study investigated a non-destructive approach for predicting PSD resistance grades based on near-infrared spectroscopy, Raman spectroscopy, and multispectral imaging of soybean seeds. A multimodal framework, MAFM-SWOM, was developed by combining a Multimodal Attention Fusion Module for intra- and inter-modal feature fusion with a Sample Weight Optimization Module for reducing redundant feature correlations in the fused representation. In SWOM, fused multimodal features are projected into a random Fourier feature space, and sample weights are optimized by minimizing the off-diagonal elements of the weighted covariance matrix. Across five repeated runs with different random seeds, MAFMSWOM achieved a mean accuracy of 91.16 +/- 0.73% and a mean Macro-F1 of 91.13 +/- 0.73%, showing stable performance. These results indicate that MAFM-SWOM can support non-destructive PSD resistance-grade screening of soybean seeds within the evaluated soybean varieties.

Epigenetic inheritance is fundamental to human development and disease, yet the mechanisms governing the transmission of DNA methylation across generations remain incompletely understood. In this study, we performed haplotype-resolved, whole-genome DNA methylation profiling in a healthy three-generation Chinese family, leveraging high-depth Oxford Nanopore Technologies (ONT) and PacBio HiFi long-read sequencing, anchored to a proband-specific telomere-to-telomere (T2T) genome assembly. We observed globally conserved bimodal methylation landscapes across all individuals and generations. Stratified analyses revealed clear functional compartmentalization of methylation marks, characterized by distinct hypomethylation in centromeres and hypermethylation in retrotransposons and repetitive elements. Chromosome-resolved analysis of ribosomal DNA (rDNA) arrays demonstrated a domain-specific methylation pattern with hypomethylation in the transcriptional core and hypermethylation in the intergenic spacer, with evidence for age-associated epigenetic drift in the transcriptional core domain. Through de novo identification and validation, we mapped 23 high-confidence imprinting control regions (ICRs) showing robust parent-of-origin-specific methylation, all overlapping known imprinted genes and enriched for regulatory element signatures. Haplotype-resolved X chromosome analysis further uncovered sex- and allele-specific methylation patterns linked to X inactivation dynamics. Together, this pedigree-scale, high-resolution study delineates the landscape and principles of intergenerational DNA methylation inheritance, revealing both conserved and dynamic features shaping the human epigenome.

As soil salinization becomes increasingly severe, most crops face significant challenges, with soybean being particularly sensitive to salt stress. In saline environments, soybean yields frequently exhibit substantial declines. In recent years, considerable efforts have been devoted to achieving high and stable soybean production in such adverse conditions. Significant progress has been achieved in the breeding of salt-tolerant varieties and in elucidating the signaling pathways and molecular mechanisms underlying soybean salt tolerance. This review systematically outlines the major mechanisms by which plants respond to salt stress, including the Salt Overly Sensitive (SOS) signaling pathway and hormonal regulation of ion homeostasis, with a particular focus on soybean-specific adaptive responses. It summarizes the regulatory roles of over 30 functional genes associated with salt tolerance. Furthermore, it proposes effective strategies to enhance soybean productivity in saline soils, such as improving soil fertility through rhizobial nitrogen fixation, optimizing carbon allocation, and employing seed coating and other pre-sowing treatments to improve plant stress resilience. Additionally, the article discusses the potential applications of cutting-edge technologies, including single-cell omics and gene editing, in accelerating the development of salt-tolerant soybean cultivars. These advances are expected to facilitate the development of more efficient breeding strategies and promote the sustainable development of the soybean industry.

The cultivated peanut (Arachis hypogaea L.) originates from the hybridization and subsequent polyploidization of two wild species harboring the A and B genomes, respectively. The B genome wild relatives of peanut provide a precious genetic reservoir and lay an important germplasm foundation for peanut genetic improvement and innovation. In the present study, the chromosome-specific oligonucleotide (oligo) probe pools for B genome chromosome based on the reference genomes of A. ipaensis, A. duranensis, and the cultivated peanut Tifrunner were developed to achieve the first specific recognition of the B genome chromosome and establish the consensus karyotype for Arachis species. Comparative analyses of the consistent karyotypes and homologous chromosomal banding patterns elucidated the genomic relationships and interspecific divergence among the Arachis genus. Notably, this study accurately identified six variant materials associated with structural and numerical variations in B subgenome chromosomes, overcoming the challenges that exist in identifying variations within the B subgenome. Furthermore, the technical system developed herein provides valuable support for distant germplasm innovation and precise subgenome chromosome identification in peanut breeding programs.

Phomopsis longicolla-induced soybean Phomopsis seed decay (PSD) threatens yield and food security. The method combing near-infrared spectroscopy with chemometrics was applied to predict the resistance levels of soybeans to fungal diseases in this study. Chemical composition analysis of soybean germplasms with different resistance levels indicated resistance may relate to the content of saponins, which can enhance the plant defense system. The spectral data showed saponin content differences can be reflected in the 1660-1700 nm region via different quantities of molecules with functional groups such as C-H bonds. Near-infrared spectroscopy (NIRS) employed chemometric methods to establish predictive models. These approaches integrated spectral preprocessing with machine learning algorithms. Preprocessed NIR spectral was used to build seven classification models for resistance identification. Especially, the Particle Swarm Optimization (PSO) algorithm was employed for data feature selection to optimize the support vector machine (SVM) and Bayesian-optimization multilayer perceptron (BO-MLP). Particle Swarm Optimization-Bayesian-optimization multilayer perceptron (PSO-BO-MLP) model demonstrated the best effect for classification, with the prediction accuracy to 89.18% and loss to 0.49. This study established a "spectral-biochemical-resistance" correlation, providing an accurate, non-destructive, and efficient method for evaluating the resistance of PSD in soybean, thereby offering a new tool for food-oriented soybean seed raw materials screening.

BACKGROUND: The nitrate transporter 1/peptide transporter family (NPF) plays a key role in nitrate uptake, transport, and nitrogen use efficiency in plants. Although NPF genes have been widely studied in many species, their genomic organization, evolutionary patterns, and functional roles in soybean remain unclear. Soybean is an important legume with high nitrogen demand and the ability to fix atmospheric nitrogen through symbiosis. RESULTS: In this study, 126 GmNPF genes were identified in the Wm82.a4.v1 genome. These genes were classified into 8 subfamilies and were unevenly distributed across 19 chromosomes. Family expansion was mainly driven by segmental duplication. Ka/Ks analysis indicated strong purifying selection. Promoter analysis revealed cis-regulatory elements associated with light response, phytohormone signaling, and abiotic stress. Expression profiling across tissues showed clear spatial and temporal patterns for 112 GmNPF genes. GmNPF6.8 was predominantly expressed in roots. Under low-nitrogen conditions, many GmNPF genes were differentially expressed. GmNPF5.13, GmNPF5.5, GmNPF7.13, GmNPF7.12, GmNPF7.14, and GmNPF2.11 were significantly upregulated, whereas GmNPF6.8 and GmNPF6.9 were significantly downregulated in soybean roots. Genetic diversity analysis of GmNPF6.8 in 4,068 soybean accessions identified 3 coding-region haplotypes. GmNPF6.8Hap1 showed clear evidence of strong artificial selection. Subcellular localization assays confirmed that GmNPF6.8 is localized to the plasma membrane. Overexpression of GmNPF6.8 in Arabidopsis and soybean hairy roots significantly reduced root length and root density. It also altered the expression of key genes involved in root development. Further analysis showed that GmARF11 directly binds to the promoter of GmNPF6.8 and represses its transcription. CONCLUSIONS: This study clarified the genomic and evolutionary features of the GmNPF family and identified GmNPF6.8 as a negative regulator of root development. These findings provide a potential target for improving nitrogen use efficiency in soybean breeding.

The cultivated peanut (Arachis hypogaea L.) is an important oilseed and economic crop worldwide, with seed protein content being a key target for quality improvement. The Sugars Will Eventually be Exported Transporter (SWEET) gene family encodes sugar transporters that play crucial roles in plant growth and development, stress tolerance, pathogen interactions, and seed filling. In this study, 43 AhSWEET genes were identified in the cultivated peanut genome and classified into four phylogenetic clades. Members within the same clade generally show similar exon-intron structures and conserved motif compositions, suggesting evolutionary conservation within subgroups. Promoter cis-regulatory elements analysis indicated that AhSWEET genes contain multiple elements associated with light response, hormone signaling, stress response, and growth and development, implying their potential involvement in diverse biological processes. Subcellular localization predictions indicated that most AhSWEET proteins are likely localized to the plasma membrane, consistent with their putative roles in transmembrane sugar transport. This was further supported by transient expression analysis of selected AhSWEET-eGFP fusion proteins in Nicotiana benthamiana leaves. Comparative phylogenetic and synteny analyses reveal that AhSWEET5, AhSWEET21, AhSWEET27, and AhSWEET43 are closely related to soybean GmSWEET10a and GmSWEET10b, which are known to regulate seed size and storage-compound accumulation. Transcriptome data and quantitative reverse transcriptase PCR analysis showed that these candidate genes are preferentially expressed in seed-related tissues, particularly the testa and embryo, suggesting possible sugar allocation during peanut seed development. Overall, this study provides a systematic characterization of the AhSWEET gene family and identifies several candidate genes for future functional studies aimed at improving peanut seed quality, including protein accumulation.

Soybeans play a crucial role in global food security, and the increasing severity of heat stress due to climate change in recent years has highlighted the growing importance of phenotypic assessment for breeding. While unmanned aerial vehicle (UAV) remote sensing offers a low-cost, high-throughput, and non-destructive approach for canopy phenotyping, variations among soybean genotypes and across growing seasons lead to scarce labeled data, which limits the applicability of traditional deep learning models. This study aims to develop a novel few-shot learning algorithm for identifying soybean canopy phenotypes under high-temperature stress (HT) and control (CK) conditions, utilizing two years of UAV RGB and near-infrared (NIR) imagery. This study proposes the Soybean Contrastive Prototypical Network (SCProtoNet), a metric-based few-shot learning model designed for accurate phenotypic analysis of soybean canopies. Specifically, SCProtoNet employs a dual-branch architecture to jointly process RGB and NIR images, effectively capturing phenotypic differences in soybean canopies caused by genotypes by optimizing prototype representations and contrastive learning instance discrimination based on few-shot scenarios. As a result, in the first year, SCProtoNet achieves an accuracy of 91.56% in a 1-shot training setting. In the second year, weights transferred from the first year are used to validate the generalization performance of the model across experimental years. Retraining the model in a 16-shot setting achieves experimental results of 87.65% and 100%. This few-shot learning model has been effectively tested on canopy images of 3232 different soybean genotypes. Experimental results demonstrate the robustness and generalization performance of the model across different experimental periods. SCProtoNet provides a valuable reference for implementing soybean genotype selection and lays the foundation for phenotypic analysis tasks in other crops under few-shot conditions. The source code and trained models are available at https://github.com/Lzylearn/SCProtoNet-Soybean.

Introduction Seed weight and nutritional composition (protein and oil content) are critical agronomic traits that collectively determine the yield and quality in soybean. However, the genetic architecture and regulatory mechanisms governing these traits remain poorly understood. Objectives This study aimed to identify the key genes and molecular mechanisms governing 100-seed weight and nutritional quality in soybean, providing a theoretical framework for the dual-enhancement of seed weight and protein content. Methods A genome-wide association study (GWAS) was conducted using 1,702 diverse soybean cultivars to identify candidate loci associated with seed weight. Functional characterization was conducted through CRISPR-mediated knockout and overexpression analyses. Population genomic analyses were further performed to elucidate the evolutionary history and selection signals of the candidate gene. Results We identified GmSW6 (Seed Weight 6), encoding a 2-oxoglutarate Fe(II)-dependent dioxygenase (2OGD), as a master regulator of 100-seed weight. Knockout of GmSW6 markedly enhanced seed weight and protein content while simultaneously reducing oil content. Mechanistically, we demonstrated that the transcription factor GmSW13 directly activates GmSW6 expression. This GmSW13-GmSW6 module, in turn, upregulates GmOLEO1 to coordinately modulate both seed weight and quality. Population genomic analysis revealed that the elite allele, GmSW6G, is significantly associated with increased seed weight and has undergone intense positive selection during soybean domestication and modern improvement. Conclusion Our findings elucidate a hierarchical genetic pathway governing soybean seed development and provide potent molecular targets for simultaneous improvement of seed weight and protein content in future ’designer’ varieties.

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder, characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta and the accumulation of Lewy bodies. Over recent decades, various cellular mechanisms underlying PD have been elucidated, including autophagy, mitochondrial dysfunction, neuroinflammation, and axonal transport. Among them, axonal transport plays a critical role in maintaining the dynamic homeostasis of proteins, membrane-bound organelles, and cellular metabolism within neurons. Unfortunately, a comprehensive overview of axonal transport in PD remains absent. In this review, we synthesized the current literature on axonal transport in PD, leveraging neurotoxic and genetic models to explore the causes and consequences of axonal transport alterations in PD. Through this summary, we aim to deepen our understanding of PD pathogenesis and provide potential therapeutic targets for intervention.

Soybean germplasm shows genetically structured variation in isoflavones, soyasaponins, folates and other food relevant traits, and seed specific metabolic engineering and genome editing can further expand this variation beyond naturally occurring profiles. Nevertheless, high seed concentration is an unreliable proxy for functional food efficacy. Compositional modifications often incur trade-offs in plant defence, germination, sensory quality and processing compatibility, while processing, digestion and microbial metabolism substantially reshape the bioactive compounds consumers actually encounter, with outcomes determined by genotype, microorganism, food matrix and production process. Human studies are highly heterogeneous and rarely traceable to the starting germplasm.We therefore establish an evidence-gated framework of four transitions: genotype to composition, composition to product, product to exposure, and exposure to outcome, each with minimum evidential thresholds. Missing upstream links narrow rather than invalidate downstream conclusions, and this framework explicitly defines attribution boundaries and guides necessary experimental work for health-oriented soybean design.

Iron deficiency is a major abiotic constraint that limits soybean growth, nodulation, symbiotic nitrogen fixation, and yield, yet objective criteria for evaluating low-Fe tolerance and the regulatory mechanisms linking root-nodule responses with shoot adaptation remain insufficiently defined. Here, we established an entropy-weight-based evaluation system using 62 soybean accessions and identified Wanhuang506 (Wh506) as a highly tolerant cultivar and Flyer as a highly sensitive cultivar. Physiological validation showed that Wh506 maintained higher Fe accumulation, chlorophyll retention, antioxidant enzyme activities, and nodule development than Flyer under low-Fe stress. To explore the molecular basis of this contrast, integrated transcriptomic and metabolomic profiling was performed in leaves and root-nodule complexes (RNCs). Compared with Flyer, Wh506 exhibited stronger RNC-centered transcriptional and metabolic reprogramming involving Fe-related redox processes, secondary metabolism, and brassinosteroid (BR) biosynthesis. Multi-omics integration prioritized GmCYP90A1, a BR biosynthetic cytochrome P450 gene, as a candidate component associated with low-Fe tolerance, while the MYB transcription factor GmMYB093 was specifically induced in Wh506 RNCs. Yeast one-hybrid and dual-luciferase assays demonstrated that GmMYB093 directly binds to the GmCYP90A1 promoter and activates its transcription. Hairy-root overexpression of GmMYB093 or GmCYP90A1 increased endogenous BR levels, improved Fe accumulation, enhanced antioxidant capacity, reduced lipid peroxidation, and alleviated chlorosis and growth inhibition under low-Fe stress. Exogenous BR application further mitigated Fe-deficiency-induced chlorosis, particularly in sensitive accessions. These findings support a model in which the GmMYB093-GmCYP90A1-BR module contributes to soybean low-Fe adaptation by coordinating Fe homeostasis, redox protection, and root-nodule performance, providing candidate targets for breeding Fe-efficient soybean cultivars.

BACKGROUND:Soybean [Glycine max (L.) Merr.] is the most common leguminous crop and provides protein and oil for human edible, animal feed, industrial and new energy across the world. The abundant oil and protein cause it more difficult to store than cereal seeds. Seed germination viability is a crucial foundation for soybean storage life and seedling establishment, while its regulatory factors remains largely unexplored. RESULTS:In this study, by compared germination percentage (GP) of 107 soybean accessions after natural aging for 18, 30 and 42 months, we identified 13 storage-tolerant and 9 storage-sensitive genotypes. Moreover, 6 high vigor genotypes and 4 low vigor genotypes were screened through 4 days of artificial aging treatment in 124 soybean accessions. The viability curves and physiological indicators indicate that high germination viability genotypes could delay the critical node (CN) of seeds by increasing antioxidant enzyme activity. To elucidate the expression profile differences of soybean accessions with contrasting seed germination viability, we generated the transcriptomes from storage-tolerant genotype Manokin (Ma) and storage-sensitive genotype Xiaoheidou (X) that were naturally aged for 18 and 30 months, as well as the high vigor genotype MN0201 (MN) and low vigor genotype Yiwohou (Y) that were artificially aged for 0, 3, 4, and 5 days. Comparative transcriptome and WGCNA analysis showed that genes clustered in the mediumpurle2 module may be involved in seed germination viability. Tissue-specific expression analysis and transcriptional accumulation during seed germination were further employed to assess hub genes, of which the expression characteristics and natural variation of GmHSP17.7B concerned to seed germination viability. CONCLUSIONS:Through screening of soybean genotypes with contrasting seed germination viability from natural populations and comparative transcriptome analysis, we elucidated the antioxidant capacity and expression profile changes during seed deterioration, which will provide worthy genotype resources and targets for efficient improvement of soybean seed vigor.

The comprehensive annotation of regulatory elements in linear genomes is needed to elucidate the molecular mechanisms underlying chromatin loop formation in plants. Here, we characterized a novel family of conserved noncoding sequences (CNSs) in the rice (Oryza sativa) genome. These sequences, known as AT-rich pincer-like elements (APEs), are composed of 13-bp repeat unit arrays in a reverse-forward configuration. Our findings revealed that there are 611 APE copies across the japonica genome. Deletion of single APEs disrupted the long-range chromatin loops anchoring target-APE regions and moderately remodeled the profile of A/B compartments, topologically associating domains (TADs), and chromatin loops, thereby rewiring the expression of looped gene(s) including those controlling important agronomic traits. Thus, APEs function as hub motifs directly mediating chromatin looping and maintaining 3D genome integrity and stability at the levels of compartments, TADs, and loops. Moreover, neighboring genomic regions harboring numerous paired non-APE (NA) CNSs were more likely to interact with each other. This finding suggests that NA CNS pairs might play a helper role in determining loop frequency in a dose-dependent manner, likely by ensuring the pairing selectivity of anchor sites. Our study highlights the importance of APEs and NA CNSs in maintaining 3D genome structure, thereby providing the framework required to link many noncoding repetitive elements to their molecular functions in plants.

Background & Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by triglyceride (TG) build-up in hepatocytes; however, our understanding of the underlying molecular mechanisms is limited. Here, we investigated the role of hepatic GTPase RAP1A in MASLD and its more progressive form, metabolic dysfunction-associated steatohepatitis (MASH). Methods: RAP1A was silenced or activated by AAV8-TBG-mediated gene expression or treating mice with a small molecule RAP1 activator (n = 4-12 per group). Primary hepatocytes were used to further probe the newly elucidated pathway. Liver samples from patients with MASH and control livers were analyzed for active RAP1A levels (n = 4 per group). Results: Activation of hepatic RAP1A is suppressed in obese mice with MASLD and restoring its activity decreases liver steatosis. RAP1A activation lowers hepatic TG accumulation through decreasing sterol regulatory element-binding protein 1 (SREBP1) cleavage by inhibiting the mechanistic target of rapamycin complex 1 (mTORC1). The mechanism linking RAP1A activation to suppression of mTORC1 involves the lowering of membrane-bound amino acid transporters, which leads to reduced hepatocyte amino acid uptake, decreased intracellular amino acid levels, and inhibition of amino acid-mediated mTORC1 activation. Furthermore, we observed that active-RAP1A levels were decreased in mice fed a MASH-provoking diet (98% lower, p <0.01) and liver extracts from patients with MASH (86% lower, p <0.05). Accordingly, restoration of RAP1A activity in mice liver lowered liver fibrotic gene expression and prevented fibrosis formation, whereas RAP1A silencing promoted the progression of MASH. Conclusions: Activation of hepatic RAP1A lowers MASLD and MASH formation by suppressing amino acid-mediated mTORC1 activation and decreasing cleaved SREBP1. These data provide mechanistic insight into amino acid-mediated mTORC1 regulation and raise the possibility that hepatic RAP1A may serve as a mechanistic node linking obesity with MASLD and MASH. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Drought stress constitutes a major threat to global wheat production. Identification of the genetic components underlying drought tolerance in wheat is highly important. Through a genome-wide association study, we identify a natural allele of the zinc finger-type transcription factor TaDT1-A on chromosome 2 A of the wheat genome that confers drought tolerance without imposing trade-offs between tolerance and yield. This allele, named TaDT1-AhapI, causes an 899-bp deletion in the promoter of the TaDT1-A gene, which results in increased expression of the gene through escape of the repressive MYC transcription factor and, consequently, the promotion of stomatal dynamics and water use efficiency via increased autophagy activity. Our findings provide genetic insights into the natural variation in wheat drought tolerance. The identified loci or genes can serve as direct targets for both genetic engineering and selection for wheat trait improvement.