Search Results (91)

The invasive tomato leafminer, Tuta absoluta, poses a severe global threat to solanaceous crops, necessitating sustainable biocontrol solutions. Through systematic bioprospecting across several Moroccan soils, we constructed a novel library of indigenous fungal isolates using complementary Tenebrio molitor baiting and selective media methods. High-throughput phenotyping identified 49 highly pathogenic isolates, which were characterized for conidial production, thermotolerance, and virulence against T. absoluta. We discovered two lead isolates, Beauveria bassiana UCA-350 and Metarhizium robertsii UCA-329, that demonstrated superior virulence, reducing median survival time and achieving lower LC₅₀ values than most commercial reference strains. Notably, virulence was positively correlated with in vitro conidial yield, revealing a key trait linkage for strain selection. Furthermore, genus-level divergence in thermotolerance was observed, with Beauveria isolates exhibiting significantly higher heat resilience. Our integrated multi-trait screening pipeline not only delivers two potent, regionally sourced biocontrol candidates but also establishes a phenotypic selection framework that prioritizes both efficacy and production scalability, advancing the rational development of next-generation mycoinsecticides. Full article

The improvement of sucrose yield in sugarcane is impeded by the crop’s complex polyploid genome and slow progress in breeding. To clarify how arginine (Arg) regulates sugar metabolism and identify key genes associated with sucrose transport and accumulation in sugarcane, a screening experiment was performed by spraying L-arginine hydrochloride on the leaves and leaf sheaths of three sugarcane varieties (YZ05-51, YZ08-1609, and YT93-159), which differ in growth vigor, leaf morphology and other phenotypic traits. YZ05-51 exhibited the most prominent sugar-increasing effect, and subsequent optimization experiments on its leaf sheaths revealed that 20 g/mu L-arginine hydrochloride at pH 7.0 was optimal, significantly enhancing stem sucrose content. Transcriptomic analysis revealed the upregulation of genes related to sucrose synthesis and transport, with candidate genes enriched in pathways such as starch-sucrose metabolism, glycolysis/gluconeogenesis, and ATP-binding cassette (ABC) transporters. Metabolomic analysis detected 32 sugar metabolites across three categories, of which 24 were differentially abundant (e.g., glucose, galactose, fructose, and mannose). Integrated multi-omics analysis identified key regulatory genes, including SBEs and TPS1 (sucrose synthesis and carbon flux regulation), RBSK, α-amylases, GH28 (starch breakdown, glycolysis, and sugar mobilization), ABC transporters, GTs, and TIM10/TIM12 (sucrose transporter). Collectively, these analyses demonstrate enhanced activity of genes and metabolites involved in sucrose synthesis/transport in leaf sheaths, accompanied by reduced synthesis of other monosaccharides and oligosaccharides. Vigorously metabolizing leaf sheaths is more conducive to sucrose transport. This study provides valuable insights into the molecular mechanisms underlying Arg-mediated sucrose accumulation specifically in the sugarcane YZ05-51 sugarcane, highlighting its critical regulatory roles. Full article

GS in beef cattle faces challenges in cross-population prediction. Blind expansion of reference populations can reduce accuracy because of genetic noise, and traditional models such as GBLUP ignore functional heterogeneity of SNPs. In this study, we used three simulated beef cattle populations with different genetic relationships and a single trait corresponding to birth weight (heritability 0.42) to evaluate the effect of functional annotations on cross-population GS. We compared GBLUP, ssGBLUP and wGBLUP using either all SNPs or SNP sets annotated by GO and KEGG. Mixed reference populations were constructed with multidimensional scaling (MDS)- and fixation index (FST)-based screening. Functionally annotated SNPs increased cross-population prediction accuracy and stability compared with generic SNPs. In the population with close genetic relatedness (PopB), GO-wGBLUP achieved a prediction accuracy of 0.55–0.60 at a 10% reference proportion, higher than GBLUP (about 0.50) and ssGBLUP (about 0.52). In the population with high genetic differentiation (PopA), KEGG-wGBLUP showed a smaller loss of accuracy when the reference proportion increased from 10% to 20% (6.7% decline, from 0.45 to 0.42) than GO-wGBLUP (10% decline, from 0.50 to 0.45) and GBLUP (20% decline, from 0.35 to 0.28). Across scenarios, functional SNP sets reduced the loss of accuracy due to reference population expansion from 20.0% in GBLUP to 12.5% in GO-wGBLUP. These results indicate that wGBLUP combined with GO or KEGG annotations can improve the accuracy and robustness of cross-population GS for beef cattle birth weight traits. GO-based models are more suitable for closely related populations, whereas KEGG-based models are more suitable for highly differentiated populations. The proposed framework provides a practical reference for multi-population GS design in beef cattle breeding. Full article

In this study, 43 celery germplasm resources were comprehensively evaluated based on phenotypic and quality-related traits. The results revealed substantial variation among the measured traits, with physiological traits exhibiting greater variability than morphological traits. Correlation analysis showed that growth-related traits were generally significantly positively correlated. Principal component analysis (PCA) effectively extracted the major sources of variation, with the first two principal components explaining 77.2% and 80.7% of the total variation in growth-related and physiological traits, respectively. A comprehensive evaluation model was subsequently established using the membership function method, and the D values ranged from 0.107 to 0.763. Comprehensive ranking identified accessions 1, 10, 12, and 17 as superior germplasm resources that may serve as valuable materials for celery breeding. Overall, the multi-trait comprehensive evaluation approach employed in this study effectively identified elite germplasm resources and provides important theoretical support for celery breeding and the efficient utilization of germplasm resources. Full article

To explore efficient and sustainable biocontrol resources against anthracnose in Begonia benariensis, endophytic fungi were isolated from healthy host tissues and screened for antagonistic activity against Colletotrichum aotearoa SWBG5. Among 31 isolates, four showed strong inhibition, and the most potent strain, QYN6, exhibited an in vitro mycelial inhibition rate of 63.67%. Based on morphology and multi-gene phylogeny (ITS, TUB2, TEF-1α), QYN6 was identified as Nigrospora sphaerica. Mechanistic assays revealed that QYN6 secretes multiple cell wall-degrading enzymes (chitinase, β-1,3-glucanase, cellulase, protease) and displays hyperparasitism against the pathogen hyphae (entwining, deformation, swelling), acting synergistically to inhibit fungal growth. In greenhouse pot trials, QYN6 achieved a biocontrol efficacy of 48.91% against Begonia anthracnose. Additionally, QYN6 significantly activated host defense responses, increasing the activities of antioxidant enzymes (SOD, POD, PPO, CAT) and the contents of soluble protein and soluble sugar. Furthermore, QYN6 exhibited multiple plant growth-promoting traits, including IAA production, siderophore synthesis, and potassium solubilization. Inoculation with QYN6 markedly improved plant height, leaf number, root length, and biomass of B. benariensis. Overall, N. sphaerica QYN6 possesses dual biocontrol and growth-promoting potential, providing a promising microbial resource and theoretical basis for green management of Begonia anthracnose. Full article

Ovarian cancer (OC) comprises multiple histotypes with distinct mechanisms, molecular features, and clinical behavior. We used Mendelian randomization (MR) to map histotype-stratified metabolic pathways and connect them to drug targets, establishing a translatable target–metabolic node–histotype risk chain. We built a multi-stage MR framework using Integrative Epidemiology Unit (IEU) OpenGWAS summary statistics. After screening 1400 plasma metabolites against overall ovarian cancer in UK Biobank and Ovarian Cancer Association Consortium (OCAC) with KEGG enrichment, we traced a prespecified amino acid/energy–nitrogen axis using histotype-stratified univariable MR and pathway-restricted multivariable MR. We then performed cis drug-target MR for PPARG, DPP4, ABCC8/KCNJ11, and SLC5A2, integrated triangulation, colocalization, and mediation analyses, and experimentally interrogated the prioritized PPARG/ABCC8-KCNJ11–lactate–invasive mucinous ovarian cancer (IMOC) triangle. Screening nominated 55 and 72 metabolites in UK Biobank and OCAC, respectively (IVW p < 0.05), highlighting amino-acid nitrogen and central-carbon metabolism. Univariable Mendelian randomization (UVMR) showed marked heterogeneity: alanine increased low-grade serous ovarian cancer (LGSOC) risk, glutamate was protective for endometrioid OC, and lactate-related traits most consistently implicated the low-grade/borderline serous lineage. In multivariable Mendelian randomization (MVMR), tryptophan and lactate levels emerged as independent risk nodes for serous low-grade plus low malignant potential (LG + LMP). Drug-target MR prioritized PPARG as protective (OR = 0.18) and ABCC8/KCNJ11 as risk-increasing (OR = 7.50) for IMOC, with opposite target → lactate effects supporting a directionally symmetric target–lactate–IMOC triangle. Experimental perturbation in mucinous ovarian cancer models produced concordant reciprocal changes in lactate and malignant phenotypes, extending this triangle biologically. This integrative MR framework delineates histotype-specific metabolic drivers and links them to actionable targets, providing a roadmap from genetic prioritization to mechanistic and translational validation. Full article

Wild maize (teosinte) has been reported to be less susceptible to biotic and abiotic stresses than its modern relative, corn. The composition of the teosinte root microbiome may be linked to traits such as drought tolerance and pest resistance. Trichoderma spp. are ubiquitous saprotrophic fungi found in the plant rhizosphere, enhancing host plant growth and crop productivity while alleviating biotic and abiotic stresses. The present study identified ten Trichoderma fungal isolates associated with the rhizosphere microbiome of teosinte (Zea mays spp. mexicana) and performed in vitro screening to assess both their multi-trait plant growth-promoting activities and their biological control potential against the phytopathogens Aspergillus flavus and Fusarium verticillioides. Additionally, interaction tests were conducted to evaluate the phytostimulant effect of Trichoderma spp. on maize (Zea mays) seed germination. Taxonomic and phylogenetic analysis identified five different Trichoderma species: T. rifaii (TA and TH); T. azevedoi (TB and TI); T. afroharzianum (TE); T. hamatum (TF and TG); and Trichoderma sp. (aff. bannaense) (TC, TD, and TJ). Partial least squares discriminant analysis revealed the isolates TF, TG, and TJ to have the highest potential for use as biocontrol and biostimulant agents. The present study is the first to examine Trichoderma species associated with the teosinte microbiome, and the results suggest that Trichoderma isolates are a potential sustainable alternative for improving maize cultivation. Full article

The Northwest soybean production region (covering Shanxi, Shaanxi, Gansu, Ningxia, Xinjiang, central and western Inner Mongolia and northern parts of Hebei) possesses vast cultivated land resources and advantageous light–temperature conditions, endowing soybean with substantial yield potential. In this study, two natural soybean populations originating from this region were used to systematically investigate the phenotypic variation in two important agronomic traits, plant height (PH) and main stem node number (NN). The results showed abundant genetic variation for both traits. Through genome-wide association analysis (GWAS) and employing a joint detection across multi-environments (control false positives), 5 SNPs significantly associated with PH and 18 SNPs significantly associated with NN were identified, among which four SNPs were detected associated with both traits. Candidate genes were further screened within the ±100 kb intervals flanking lead SNPs at association peaks. By integrating gene expression levels of different soybean tissues and their correlations with the phenotypes, two candidate genes associated with both PH and NN were determined. These findings provide a theoretical basis for the identification and utilization of soybean germplasm resources in Northwest China, and lay a solid foundation for breeding high-yield and high-quality soybean varieties through molecular breeding. Full article

Genomic prediction is now routine in crop improvement, but its main bottleneck has shifted from marker density to environmental complexity. Breeders rarely need predictions for one fixed environment; they need to rank genotypes across target populations of environments that differ in weather, soils, management, and stress timing. This makes genotype-by-environment interaction a primary breeding problem rather than a secondary statistical nuisance. This review examines how genomic, environmental, and phenomic information can be integrated to improve multi-environment prediction in crop breeding pipelines. The review is narrative rather than PRISMA-style, but the literature search and selection logic were structured and explicitly defined. Peer-reviewed English-language studies were identified through structured searches of Web of Science Core Collection and Scopus, supplemented by backward citation screening, with emphasis on literature published from January 2023 to March 2026. Four conclusions emerge. First, environmental information is most useful when it is developmentally aligned, biologically interpretable, and matched to the target population of environments. Second, strong structured statistical baselines remain highly competitive, especially in moderate-sized or highly unbalanced datasets, whereas gains from more flexible machine-learning and deep-learning approaches are most evident in large, sparse, heterogeneous, and multimodal settings. Third, phenomic markers often improve prediction for complex traits, especially yield, because they capture realized crop responses not fully represented by markers alone. Fourth, practical value depends less on isolated gains in predictive accuracy than on evaluation under realistic deployment scenarios, including untested genotype and untested environment settings. Progress therefore requires transparent reporting, benchmark design, stage-aware envirotyping, multimodal integration, uncertainty reporting, and cost-aware deployment. Full article

Objectives: To facilitate the innovation and efficient utilization of specialty maize germplasm, this study aimed to systematically evaluate a panel of 222 inbred lines. The objective was to comprehensively characterize phenotypic variation, genetic diversity, and genotype–phenotype associations to screen for representative germplasm resources. Methods: We integrated Best Linear Unbiased Prediction (BLUP) values derived from multi-environment field trials with high-density whole-genome single-nucleotide polymorphism (SNP) data. Population structure and genetic diversity were analyzed, Mantel tests were conducted to assess genotype–phenotype correspondence, and a genome-wide association study (GWAS) was performed to identify significant loci. Results: The population exhibited substantial phenotypic variation, particularly in plant height and tassel traits, with distinct morphological differentiations among specialty types. Genetic diversity analyses revealed varying diversity levels among subpopulations. While Mantel tests indicated a weak overall genotype–phenotype correspondence, specific traits showed significant associations with genetic distance. GWAS successfully identified significant loci associated with plant height and tassel traits. Furthermore, population structure analysis revealed distinct genetic stratification corresponding to specialty types, albeit with a certain degree of admixture. Conclusions: By integrating multi-dimensional phenotypic and genomic profiles, a panel of highly diverse and representative candidate germplasm was identified. These findings provide a crucial theoretical basis for specialty maize breeding and the optimized utilization of germplasm resources. Full article

Rhizosphere microbiome engineering is a promising approach that can enhance crop resilience and input use efficiency by redirecting plant–microbe–soil interactions toward predictable functions. Here, we review the mechanistic bases underlying rhizosphere assembly and stability, including root exudate-mediated selection, priority effects, keystone taxa, and metabolite-driven signaling, and connect these principles to proposed design rules for microbial inoculants. We present a generalizable Design–Build–Test–Learn (DBTL) framework for engineering synthetic microbial consortia, covering trait-to-module mapping (nutrient acquisition, phytohormone modulation, ACC deaminase activity, stress-protective metabolites, and biocontrol), compatibility screening, minimal yet robust community architectures, and iterative optimization driven by multi-omics and high-throughput phenotyping. Translation to field settings is framed as an engineering challenge defined by formulation and administration limitations, including carrier type, seed coating and encapsulation methods, shelf life, strain invasiveness, and permanence of colonization amid environmental diversity. We also summarize how integrative measurement pipelines (amplicon and shotgun sequencing, transcriptomics, metabolomics, and network or causal analyses) can advance microbiome studies from correlation to actionability. We describe how precision agriculture (sensors, remote sensing, and variable-rate inputs) and AI/ML (split-sample comparisons, transfer learning, and active learning) approaches can accelerate strain discovery, mixture optimization, and adaptive experimentation, driven by the need for stringent controls, metadata-rich reporting, and cross-site comparability. Use cases focus on stress conditions (drought, salinity, thermal extremes, and biotic stress) to demonstrate how microbial functions translate to agronomic outcomes and to highlight critical bottlenecks for reproducible, scalable microbiome products. Full article

To address the current mismatch between processing pepper cultivars and the requirements of mechanized production, this study aims to construct a comprehensive evaluation model for the suitability of mechanized transplanting and harvesting, thereby screening highly adaptable varieties. An evaluation system comprising eight indicators for the transplanting stage and thirteen indicators for the harvesting stage was established using 105 processing pepper varieties (including 56 erect-fruit and 49 pendent-fruit peppers). Variation analysis, hierarchical clustering, principal component analysis (PCA), and Pearson correlation analysis were integrated to reveal the clustering effects of the cultivars and the synergistic and antagonistic relationships among the indicators. Furthermore, a combined CRITIC–VIKOR model was applied to conduct a multi-criteria comprehensive ranking of mechanization suitability. The results indicated that the biomechanical properties of processing peppers exhibited a significantly higher degree of variation than conventional morphological indicators (e.g., the coefficient of variation for lodging resistance reached 93.60%). Significant differences were observed in the mechanization adaptation mechanisms between the two pepper types: erect-fruit peppers were primarily limited by fruiting branch toughness (weight: 5.907%), whereas pendent-fruit peppers were mainly constrained by fruit morphological uniformity. Compared with the traditional PCA model, the CRITIC–VIKOR model effectively identified varieties with critical biomechanical defects by constraining the “individual regret value”, which highly aligns with Liebig’s Law of the Minimum in mechanized operations. Based on this model, varieties with superior comprehensive mechanization adaptability were successfully identified, notably C21, C55, and C23 (erect-fruit peppers), and D20, D11, and D19 (pendent-fruit peppers). This study provides a theoretical foundation and mathematical modeling support for the directional breeding of mechanization-suitable cultivars, the integration of agronomy and agricultural machinery, and the quantitative evaluation of multi-trait pyramiding in processing peppers. Full article

The global demand for Salvia miltiorrhiza Bunge in the botanical medicine market is steadily increasing. However, its production has long relied on asexual root propagation, making it highly susceptible to germplasm degradation. Transitioning to seed reproduction offers the advantage of genetic renewal, yet it is constrained by unstable seed vigor and slow seedling growth. In the present study, comprehensive physiological and microbiome analyses of S. miltiorrhiza seeds from 14 regions across 7 provinces in China were conducted to elucidate the association between the seed microbiome and vigor, and to identify plant growth-promoting (PGP) strains. The results demonstrated: (1) Seed physical traits and germination characteristics varied significantly across geographic origins. Seed vigor, exhibiting the highest coefficient of variation, served as a key parameter reflecting germination quality. (2) High-vigor seeds harbored distinct microbial communities characterized by higher diversity indices, greater network complexity, and the significant enrichment of potentially beneficial bacteria (e.g., Pseudomonas). (3) Through correlation-directed screening of isolated pure cultures, Pseudomonas mendocina P-6 and Enterobacter ludwigii BM-12 were identified as exhibiting robust, multi-trait PGP capacity. In planta validation showed that these two strains significantly promoted the growth of 1-month-old S. miltiorrhiza seedlings, increasing total fresh weight by 33.9–71.3%. This study reveals the microecological drivers of seed vigor and provides candidate strains for inoculant development, thereby supporting the sustainable, seed-based propagation of S. miltiorrhiza. Full article

Aphanomyces root rot is a major threat to legume production worldwide, mainly in pea and lentil, crops on which extensive research programs are targeting the management of the disease. However, other legumes such as common vetch, although known to be severely affected by the disease, remain largely unexplored. This study aimed to identify sources of resistance within V. sativa subsp. sativa accessions. A total of 211 genetically diverse accessions were screened under controlled conditions following inoculation with isolate RB84. Disease progression was monitored through periodic foliar assessments and final root symptom evaluation. To assess resistance stability, a subset of 13 accessions representing contrasting response levels was further inoculated with three additional isolates (Aph-1, AE11, and AE12). In this multi-isolate assay, disease severity was quantified, shoot biomass was recorded, and root system architecture traits were determined using WinRHIZO image analysis. A high correlation between foliar and root symptoms at 20 days indicated that foliar symptom assessment provides a reliable, non-destructive indicator of root health. Considerable variation in disease response was detected, with several genotypes maintaining consistently low symptom levels and three exhibiting near-complete resistance across all isolates. Root architectural traits further corroborated visual disease assessments, showing patterns consistent with resistance and susceptibility responses. Overall, this study demonstrates the presence of genetic variability in the response of V. sativa to A. euteiches, with a subset of accessions showing resistance to the four isolates tested. This resistance potential can be directly used in breeding programs focused on improving tolerance to root rot. Full article

Background: Sjögren’s disease (SjD) is a chronic systemic autoimmune disorder characterized by persistent exocrine gland inflammation, possible multi-organ involvement and a marked predominance of mid-life women. Beyond dryness and fatigue, patients report mood disturbances and cognitive complaints such as “brain fog”, which affect daily functioning and quality of life. Objective: To summarize and critically synthesize the literature on depression, anxiety, cognitive function, personality traits and quality of life assessment in adults with SjD and to highlight clinically relevant gaps. Methods: We performed a narrative review (PubMed, Cochrane, Embase through June 2025) of studies on psychological outcomes, cognitive function and quality of life in adults with SjD. Results: Depression and anxiety were frequently observed: depressive symptoms were present in roughly one-third to nearly half of patients, while anxiety symptoms were reported by about one-third. Cognitive impairment (affecting memory, attention and executive function) was also frequently described, often alongside severe fatigue and sleep disturbance. Overall, quality of life was reduced in SjD, driven mainly by fatigue and emotional distress rather than by classic disease activity. Neuroimmune mechanisms (e.g., chronic systemic inflammation and cytokine signalling such as IL-6 and TNF-α) may contribute to affective and cognitive symptoms. Overall, the evidence base remains largely cross-sectional and heterogeneous. Conclusions: Psychiatric symptoms and cognitive complaints represent a substantial and clinically relevant burden in SjD. Routine screening and multidisciplinary management that includes psychological assessment and support may improve well-being, adherence and quality of life. Full article