Genetic diversity is crucial to genetic research and crop breeding, and core collections are important resources for capturing this diversity.  Recently, the core germplasm of tea plants was constructed mainly based on phenotypic data or molecular markers; however, the effective construction of core germplasm resources for plant breeding programs requires consideration of multiple aspects.  In this study, we collected 320 tea germplasm resources and analyzed their single-nucleotide polymorphisms (SNPs) and metabolite data.  Abundant genetic diversity in tea plants was inferred from the mean values of observed heterozygosity (Ho=0.340), expected heterozygosity (He=0.327), minor allele frequency (MAF=0.229), and polymorphic information content (PIC=0.268), based on the data from 2,118,060 high-quality SNP markers.  A mean genetic diversity index (H´) value of 1.902 suggested significant metabolic variation.  The 320 tea samples were categorized into six groups based on phylogenetic analysis, reflecting the influence of geographical origins on genetic diversity.  Based on the genetic and metabolic data, a preliminary core collection of 106 accessions was developed to effectively represent most of the original panel’s molecular, metabolic, population, and regional diversity.  Genome-wide association studies of the core panel successfully replicated the marker-trait associations found in the original panel.  This study contributes to the conservation and management of tea plant germplasm.

The ideal plant architecture is a critical factor in achieving high yields in tomato (Solanum lycopersicum) cultivation.  The length and number of internodes directly influence plant height.  Therefore, investigating the regulatory mechanisms of internode morphology is essential for the genetic enhancement of tomatoes.  We identified a naturally occurring field mutant, tomato elongated internode (tei), characterized by longer internodes and darker leaf color.  Physiological hormone and microscopic studies revealed that, compared to wild-type (WT) plants, the tei mutant exhibited increased endogenous GA3 levels, enhanced photosynthetic capacity, and elongation of stem internode cells.  RNA-seq analysis results of tei and WT indicated enrichment in the gibberellin pathway.  We employed BSA-seq for mapping analysis on tei, WT, and F₂ populations, leading to the fine mapping of the candidate gene designated as TEI (Tomato Elongated Internode).  This gene encoded a gibberellin 20 oxidase (GA20ox) protein and was identified as Solyc09g042210.  Additionally, we discovered numerous SNPs and InDel mutations in the TEI promoter region, with expression levels of TEI in tei stems significantly higher than those in WT.  Furthermore, knocking out the TEI gene eliminated its role in elongating internodes.  We proposed that TEI serves as the primary effector gene regulating the internode elongation phenotype associated with tei.  This discovery offered researchers a novel target for enhancing crop plant varieties by modulating gibberellin homeostasis, ultimately contributing to the breeding of superior tomato varieties.

The eutrophication of rivers and lakes is becoming increasingly common, primarily because of pollution from agricultural non-point sources.  We investigated the effects of optimized water and fertilizer treatments on agricultural non-point source pollution in the Nansi Lake basin.  The water heat carbon nitrogen simulator model (WHCNS model) was used to analyze water and nitrogen transport in wheat fields in Nansi Lake basin.  Four water and fertilizer treatments were set up: conventional fertilization and irrigation (CK), reduced controlled-release fertilizer and conventional irrigation (F2W1), an equal amount of controlled-release fertilizer and reduced irrigation (F1W2), and reduced controlled-release fertilizer and reduced irrigation (F2W2).  The results indicated that the replacement of conventional fertilizers with controlled-release fertilizers, combined with reduced irrigation, led to reduced nitrogen loss.  Compared with those of the CK, the cumulative nitrogen leaching and ammonia volatilization of F2W1 were reduced by 8.90 and 41.67%, respectively; under F1W2, the same parameters were reduced by 12.50 and 15.99%, respectively.  Compared with the other treatments, F2W2 significantly reduced nitrogen loss while producing a stable yield.  Compared with those of the CK, ammonia volatilization and nitrogen loss due to leaching were reduced by 29.17 and 27.13%, respectively, water and nitrogen use efficiencies increased by 11.38 and 17.80%, respectively.  F2W2 showed the best performance among the treatments, considering water and fertilizer management.  Our findings highlight the effectiveness of optimizing water and fertilizer application in improving the water and nitrogen use efficiency of wheat, which is of great significance for mitigating nitrogen loss from farmland in the Nansi Lake basin.

Genomic selection (GS) has been widely used in livestock, which greatly accelerated the genetic progress of complex traits.  The population size was one of the significant factors affecting the prediction accuracy, while it was limited by the purebred population.  Compared to directly combining two uncorrelated purebred populations to extend the reference population size, it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.  In this study, we simulated purebred offspring (P_AS and P_BS) and crossbred offspring (C_AB) base on real genotype data of two base purebred populations (P_A and P_B), to evaluate the performance of genomic selection on purebred while incorporating crossbred information.  The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship (REL) was better than the other methods (individuals closet or farthest to the purebred population, CP/FP) in term of the prediction accuracy.  Furthermore, the prediction accuracy of reference populations combining P_A and C_AB was significantly better only based on P_A, which was similar to combine P_A and P_AS.  Moreover, the rank correlation between the multiple of the increased relationship (MIR) and reliability improvement was 0.60–0.70.  But for individuals with low correlation (Cor(P_i, P_{A or B}), the reliability improvement was significantly lower than other individuals.  Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.  The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

PLATZ is a novel zinc finger DNA-binding protein that plays an important role in regulating plant growth and development and resisting abiotic stress.  However, there has been very little research on the function of this family gene in tomatoes, which limits its application in germplasm resource improvement.  Therefore, the PLATZ gene family was identified and analyzed in tomato, and its roles were predicted and verified to provide a basis for in-depth research on SlPLATZ gene function.  In this study, the PLATZ family members of tomato were identified in the whole genome, and 19 SlPLATZ genes were obtained.  Functional prediction was conducted based on gene and promoter structure analysis and RNA-seq-based expression pattern analysis.  SlPLATZ genes that responded significantly under different abiotic stresses or were significantly differentially expressed among multiple tissues were screened as functional gene resources.  SlPLATZ17 was selected for functional verification by experiment-based analysis.  The results showed that the downregulation of SlPLATZ17 gene expression reduced the drought and salt tolerance of tomato plants.  Tomato plants overexpressing SlPLATZ17 had larger flower sizes and long, thin petals, adjacent petals were not connected at the base, and the stamen circumference was smaller.  This study contributes to understanding the functions of the SlPLATZ family in tomato and provides a reference for functional gene screening.

Genotype imputation is essential for increasing marker density and maximizing the utility of existing SNP array data in animal breeding. Although a wide range of software is available for genotype imputation, a comprehensive benchmark in pigs is still lacking. In this study, we benchmarked 24 combinations of genotype imputation software for SNP arrays in pigs, comprising six independent pre-phasing software (fastPHASE, MaCH, BIMBAM, Eagle, SHAPEIT, Beagle) and four distinct imputation software (pbwt, Minimac, IMPUTE, Beagle), using 1,602 whole-genome sequencing (WGS) pigs from a multibreed pig genomics reference panel (PGRP) in PigGTEx. Our results indicated that the combination of Beagle for pre-phasing and Minimac for imputation achieves the highest imputation accuracy with a concordance of 0.983, especially for low-frequency SNPs (MAF<0.05). Finally, we proposed three recommended strategies: i) the combination of Beagle and Minimac is optimal for achieving the highest accuracy; ii) the combination of Beagle and Beagle is recognized for its convenience and relatively high accuracy despite it being memory-intensive; iii) the combination of Eagle and pbwt is feasible for its minimal computational cost with relatively high accuracy. This study provides valuable insights for implementing genotype imputation for pig SNP arrays toward sequence data and offers a basis for applications in livestock and poultry breeding.

Although genome-wide interaction effects are critical for unraveling the underlying genetic architectures of complex traits, the rich landscape of biological interactions is often disregarded in statistical models for genomic dissecting and predicting complex traits/diseases. To bridge this gap, we introduce biBLUP (biological interaction Best Linear Unbiased Prediction), a novel epistatic model that integrates prior biological knowledge by focusing on interactions among genes within KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Simulation experiments demonstrate that biBLUP effectively captures interaction effects across diverse genetic architectures, achieving up to a 62% increase in predictive accuracy compared to models ignoring such information. We validated the performance of biBLUP using real data across species. In a specific application using data from 6,642 yeast lines, biBLUP yielded a 40.36% improvement in prediction accuracy for growth rate by modeling genetic interaction effects within the KEGG pathway associated with allantoin utilization. Furthermore, incorporating KEGG into biBLUP successfully captures validated epistatic effects associated with rice flowering time. This integration results in an improvement of 16.29% in prediction accuracy for flowering time of rice. Our findings demonstrate that integrating KEGG pathway information into genomic prediction models enables the capture of biologically relevant interaction effects, thereby enhancing both predictive ability and our understanding of the genetic basis of complex traits.

Citrus Huanglongbing (HLB) has caused extensive damage to the global citrus industry. 'Candidatus Liberibacter asiaticus' (CLas), the primary causal agent of HLB, utilizes effectors to modulate host defense responses, though the mechanisms of these effectors remain unclear. This study demonstrates that the Citrus ARM repeated protein CsARM26 interacted with CLIBASIA_00185 (CLas0185) in vivo and in vitro. CLas0185 enhanced the abundance of CsARM26, while CsARM26 destabilized the effector. Additionally, the transient co-expression of CLas0185 and CsARM26 facilitated infection by Xanthomonas citri subsp. citri. Moreover, transgenic CsARM26 citrus plants suppressed the accumulation of free salicylic acid (SA) and the expression of SA-associated genes. This study reveals that an ARM repeated protein plays a role in the immune response to the CLas–citrus interaction, establishing a foundation for further investigation of the molecular mechanisms of CLas infection.

The evolutionary development of adventitious roots (ARs) in plants enhances their capacity to adapt to various stress conditions. A thorough analysis of the influencing factors in its morphological construction holds significant theoretical value and practical guidance for overcoming rooting obstacles in cuttings, as well as for cultivating superior varieties characterized by broad adaptability and stress resistance. In this study, we investigated the molecular mechanisms underlying the development of ARs in tomato (Solanum lycopersicum).by performing transcriptome sequencing (RNA-seq). We analyzed the transcription profiles of relevant genes in the "Y962" strain, which exhibits spontaneous AR formation, and the "W961" strain, which does not form ARs. Our findings indicate that the AR induction stage represents an active phase of development, during which we identified 1,676 overlapping genes across the three comparison groups, highlighting the most differentially expressed genes. Functional enrichment analysis showed that they were most closely related to response to auxin, and were also dependent on the crosstalk between other hormones and carbohydrates. Furthermore, through the measurement of endogenous auxin levels and the induction tests with exogenous auxin, it was established that the formation of ARs is closely linked to the accumulation and transport of auxin. Notably, the auxin efflux SlPIN3, which was enriched in the auxin response pathway, exhibited significantly high expression during the induction phase of ARs. The slpin3 mutant, generated using the CRISPR/Cas9 editing system, exhibited a significant reduction in the number of ARs, highlighting the close relationship between polar transport regulated by SlPIN3 and auxin-induced AR formation. In summary, this study not only enriches the developmental network of AR formation in tomatoes with a wealth of data but also elucidates the potential mechanisms for promoting AR development by targeting SlPIN3. 

The tea plant (Camellia sinensis) is an economically important leaf crop in which the flowering process consumes substantial nutrients, thereby negatively impacting tea yield and quality. Therefore, deciphering the molecular basis of floral transition is essential for enhancing tea cultivars and optimizing plantation management. The natural mutant ‘Ziyang 1’ (ZY1H, which had not flowered for years) and its wild-type cultivar ‘Ziyang’ (ZYQT, normal flowering) were used to study the molecular mechanisms underlying the non-flowering phenotype in ZY1H. Phenotypically, ZY1H exhibited shortened internodes, prolonged vegetative growth, and failure to develop floral meristems. Chromosomal analysis confirmed that ZY1H maintains a normal diploid chromosome number (2n=30), excluding triploidy as a cause of its sterility. Transcriptome analysis revealed defective vegetative-to-reproductive transition in ZY1H, manifesting as insufficient expression of SPL genes and sustained high expression of flower-inhibiting AP2-like genes. Additionally, elevated expression of the flowering repressor SVP and reduced expression of the flowering integrator FT further disrupted the integration of floral induction signals. Notably, brassinosteroid (BR) levels and CsBZR2 expression were elevated in ZY1H. Functional assays showed that CsBZR2 directly interacts with the CsFLC promoter and suppresses its expression, thereby blocking the flowering process. These findings suggest that the floral transition defect in ZY1H is driven by dysregulated BR signaling and excessive vegetative growth, to provide novel insights into the molecular mechanisms of flowering regulation in tea plants and valuable theoretical support for cultivar improvement.

Overapplication of nitrogen (N) is an important limiting factor in sustainable agricultural development. Breeding N-efficient genotypes is an effective approach to reduce crop N input, increase N-efficiency, and improve crop productive. However, the molecular mechanisms underlying low-N adaptations in peanut (Arachis hypogaea L.) roots are unknown. Herein, we compared root adaptation mechanisms to low-N stress between the N-efficient genotype JH15 (JH) and the N-inefficient genotype HY20 (HY), focusing on N metabolism and antioxidant capacity. Under N deficiency, JH exhibited a more developed root architecture, higher antioxidant activity, and higher N-metabolic enzyme levels under N deficiency. The expression of both high- and low-affinity nitrate transporter proteins (NRT2.5, NRT1.6), and the chloride channel protein CLC was upregulated in JH, with higher expression of genes encoding glutamine synthetase and asparagine synthase. However, only the low-affinity N transporters (NPF5.2, NPF7.3) were upregulated in HY. Flavonoid and isoflavonoid biosynthesis were the main metabolic pathways underlying the differences between the two genotypes under low-N treatment. The results of weighted gene co-expression network analysis and correlation network analysis revealed that differential expression of the key genes encoding caffeoyl-CoA O-methyltransferase, chalcone synthase, 2'-hydroxyisoflavone reductase, and shikimate hydroxycinnamoyl-CoA transferase affected key metabolites levels (epicatechin, kaempferol, calycosin, and biochanin A). We also found that WRKY40 and MYB30, MYB4, and bHLH35 may regulate flavonoids accumulation as positive and negative regulators, respectively. In summary, enhanced N uptake and assimilation and flavonoid accumulation in JH enhanced N metabolism and antioxidant capacity, improving N-efficiency.

Peanut (Arachis hypogaea L.) is a globally recognized crop with a pivotal role in the agricultural and economic sectors worldwide. Low temperature during the flowering stage is a major constraint for peanut yield in high-altitude and high-latitude regions. Despite extensive progress in understanding vegetative low temperature responses, the molecular mechanisms governing reproductive-stage low temperature sensitivity remain largely unexplored. To clarify how low temperature disrupts anther function and pollen fertility, a comparative study was conducted between a low temperature-tolerant genotype (NH5) and a low temperature-sensitive genotype (NH9) under controlled low temperature conditions. Low temperature markedly impaired peanut reproductive development by reducing peg and pod formation, altering floral organ morphology, and decreasing pollen viability, while tolerant genotype NH5 exhibited milder yield and pollen damage than sensitive NH9, indicating that low temperature primarily affects peanut yield through pollen quality deterioration. Furthermore, multi-omics profiling identified 20,928 differentially expressed genes and 1,613 metabolites, showing significant enrichment in carbohydrate metabolism, glycolysis, and tricarboxylic acid (TCA) cycle pathways, thereby highlighting severe metabolic reprogramming in low temperature-sensitive anthers. Importantly, low temperature stress triggered a pronounced accumulation of abscisic acid (ABA) in sensitive genotypes, which in turn suppressed key sugar-metabolizing enzymes activities (BAM, INV, HXK, PK, and CS) and restricted hexose availability to developing pollen. Consistently, exogenous ABA application exacerbated pollen sterility, whereas chemical inhibition of ABA biosynthesis restored fertility and reactivated carbon metabolism under low temperature conditions. Together, these findings demonstrate that ABA acts as a pivotal negative regulator of reproductive low temperature tolerance by constraining sugar utilization in peanut anthers. This study thus provides new mechanistic insight into ABA–sugar metabolism coordination during floral low temperature adaptation and offer a theoretical basis for breeding peanut varieties with enhanced low temperature resilience.

Genomic selection (GS) is one of the most effective approaches for accelerating genetic improvement in animals and plants, but its efficiency largely depends on the size of the training population. However, establishing a large training population is often time-consuming and costly. An alternative strategy is to combine multiple populations distributed across different breeding farms or companies for joint GS, but this is greatly constrained by data-security concerns and the lack of a public platform for secure collaborative analysis. In this study, we developed HEGS (Homomorphic Encryption Genomic Selection), an open-source platform for privacy-preserving joint GS across institutions, which is in principle applicable to diploid species. HEGS uses homomorphic encryption to perform genomic analyses directly on encrypted data without revealing raw information, and extends the encrypted analysis framework from the initial genomic best linear unbiased prediction (GBLUP) model to include both conventional best linear unbiased prediction (BLUP) and single-step GBLUP (ssGBLUP), thereby broadening its applicability in breeding evaluation. To demonstrate the utility of the platform, we constructed a large encrypted pig dataset comprising four breeds (Duroc, Yorkshire, Landrace, and Pietrain), 36 economically important traits, 180 pre-encrypted datasets, and more than 580,000 phenotypic records, enabling immediate joint analyses without exposing raw data. Using both simulated and real datasets, we demonstrated the feasibility and effectiveness of GS under homomorphic encryption. After model fitting, HEGS outputs genomic estimated breeding values (GEBVs) for genotyped candidates without phenotypic records, facilitating selection without additional phenotyping. Overall, HEGS provides a deployable and scalable open-source solution for privacy-preserving cross-institutional collaboration in animal breeding.