Mitochondrial dysfunction gene expression, DNA methylation, and inflammatory cytokines interaction activate Alzheimer's disease: a multi-omics Mendelian randomization study.

BACKGROUND: Mitochondrial dysfunction (MD) is increasingly recognized as a key pathophysiological contributor in Alzheimer disease (AD). As differential MD genes expression may serve as either a causative factor or a consequence in AD, and expression of these genes could be influenced by epigenetic modifications or interact with inflammatory cytokines, hence, the precise role of MD in AD remains uncertain. METHODS: Meta-analysis of brain transcriptome datasets was conducted to pinpoint differentially expressed genes (DEGs) associated with MD in AD. We utilized three-step SMR to analyze the AD genome-wide association study summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs from the blood and brain tissues, respectively. Through SMR and colocalization analysis, we further explored the interactions between brain eQTLs and inflammatory cytokines. RESULTS: Five datasets were meta-analyzed to prioritize 825 DEGs in AD from 1339 MD-related genes. Among these, seven genes from blood samples such as NDUFS8 and SPG7 and thirty-two genes from brain tissue including CLU and MAPT were identified as candidate AD-causal MD genes and regulated by methylation level. Furthermore, we revealed 13 MD gene expression-inflammatory pathway pairs involving LDLR, ACE and PTPMT1 along with interleukin-17C, interleukin-18 and hepatocyte growth factor. CONCLUSIONS: This study highlighted that the AD-causal MD genes could be regulated by epigenetic changes and interact with inflammatory cytokines, providing evidence for AD prevention and intervention.

Quantitative Trait Locus Mapping Combined with RNA Sequencing Identified Candidate Genes for Resistance to Powdery Mildew in Bitter Gourd (Momordica charantia L.).

Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by hybridizing the powdery mildew-resistant material MC18 and the powdery mildew-susceptible material MC402. A high-density genetic map of 1222.04 cM was constructed via incorporating 1,996,505 SNPs generated by resequencing data from 180 lines, and quantitative trait locus (QTL) positioning was performed using phenotypic data at different inoculation stages. A total of seven QTLs related to powdery mildew resistance were identified on four chromosomes, among which qPm-3-1 was detected multiple times and at multiple stages after inoculation. By selecting 18 KASP markers that were evenly distributed throughout the region, 250 lines and parents were genotyped, and the interval was narrowed to 207.22 kb, which explained 13.91% of the phenotypic variation. Through RNA-seq analysis of the parents, 11,868 differentially expressed genes (DEGs) were screened. By combining genetic analysis, gene coexpression, and sequence comparison analysis of extreme materials, two candidate genes controlling powdery mildew resistance in bitter gourd were identified (evm.TU.chr3.2934 (C3H) and evm.TU.chr3.2946 (F-box-LRR)). These results represent a step forward in understanding the genetic regulatory network of powdery mildew resistance in bitter gourd and lay a molecular foundation for the genetic improvement in powdery mildew resistance.

High-content phenotypic analysis of a C. elegans recombinant inbred population identifies genetic and molecular regulators of lifespan.

Lifespan is influenced by complex interactions between genetic and environmental factors. Studying those factors in model organisms of a single genetic background limits their translational value for humans. Here, we mapped lifespan determinants in 85 C. elegans recombinant inbred advanced intercross lines (RIAILs). We assessed molecular profiles-transcriptome, proteome, and lipidome-and life-history traits, including lifespan, development, growth dynamics, and reproduction. RIAILs exhibited large variations in lifespan, which correlated positively with developmental time. We validated three longevity modulators, including rict-1, gfm-1, and mltn-1, among the top candidates obtained from multiomics data integration and quantitative trait locus (QTL) mapping. We translated their relevance to humans using UK Biobank data and showed that variants in GFM1 are associated with an elevated risk of age-related heart failure. We organized our dataset as a resource that allows interactive explorations for new longevity targets.

Cellulose synthase-like OsCSLD4: a key regulator of agronomic traits, disease resistance, and metabolic indices in rice.

KEY MESSAGE: Cellulose synthase-like OsCSLD4 plays a pivotal role in regulating diverse agronomic traits, enhancing resistance against bacterial leaf blight, and modulating metabolite indices based on the multi-omics analysis in rice. To delve deeper into this complex network between agronomic traits and metabolites in rice, we have compiled a dataset encompassing genome, phenome, and metabolome, including 524 diverse accessions, 11 agronomic traits, and 841 metabolites, enabling us to pinpoint eight hotspots through GWAS. We later discovered four distinct metabolite categories, encompassing 15 metabolites that are concurrently present on the QTL qC12.1, associated with leaf angle of flag and spikelet length, and finally focused the cellulose synthase-like OsCSLD4, which was pinpointed through a rigorous process encompassing sequence variation, haplotype, ATAC, and differential expression across diverse tissues. Compared to the wild type, csld4 exhibited significant reductions in the plant height, flag leaf length, leaf width, spikelet length, 1000-grain weight, grain width, grain thickness, fertility, yield per plant, and bacterial blight resistance. However, there were significant increase in tiller numbers, degree of leaf rolling, flowering period, growth period, grain length, and empty kernel rate. Furthermore, the content of four polyphenol metabolites, excluding metabolite N-feruloyltyramine (mr1268), notably rose, whereas the levels of the other three polyphenol metabolites, smiglaside C (mr1498), 4-coumaric acid (mr1622), and smiglaside A (mr1925) decreased significantly in mutant csld4. The content of amino acid L-tyramine (mr1446) exhibited a notable increase, whereas the alkaloid trigonelline (mr1188) displayed a substantial decrease among the mutants. This study offered a comprehensive multi-omics perspective to analyze the genetic mechanism of OsCSLD4, and breeders can potentially enhance rice's yield, bacterial leaf blight resistance, and metabolite content, leading to more sustainable and profitable rice production.

SNP-based QTL mapping and identification of panicle structure-related genes in rice.

Rice is a staple crop providing a significant portion of the global food supply. It is then crucial to develop strategies for breeding high-yield cultivars to meet global food security challenges, including the UN's zero-hunger goal. In this study, QTL mapping was employed to pinpoint key genomic regions linked to traits influencing rice yield, with a focus on panicle structure-a critical determinant of grain number. Over two consecutive years, QTLs were identified using 88 JJ625LG/Namchan Recombinant Inbred Lines (JNRILs), revealing several candidate genes. Notably, Gn1a, a known regulator of grain number, was mapped within qNS1 and qNSSr1-1, while the sd1 gene, linked to plant height, was detected across multiple QTLs. Furthermore, a novel gene, OsNSMq3 (Os03g0843800), encoding a methyltransferase, was identified in various QTLs, with haplotype and sequence homology analysis suggesting its role in enhancing yield by influencing panicle structure development. The increase in primary and secondary branches, driven by these genes, leads to a higher number of spikelets per panicle, thereby boosting yield. These findings underscore the potential of candidate genes from stable QTLs as valuable tools in molecular breeding to develop high-yield rice cultivars, addressing global hunger and aiding food supply in refugee crises.

Unveiling the role of the ERD15 gene in wheat's tolerance to combined drought and salinity stress: a meta-analysis of QTL and RNA-Seq data.

The coexistence of drought and salinity stresses in field conditions significantly hinders wheat (Triticum aestivum L.) productivity. Understanding the molecular mechanisms governing response and tolerance to these stresses is crucial for developing resilient wheat varieties. Our research, employing a combination of meta-QTL and meta-RNA-Seq transcriptome analyses, has uncovered the genome functional landscape of wheat in response to drought and salinity. We identified 118 meta-QTLs (MQTLs) distributed across all 21 wheat chromosomes, with ten designated as the most promising. Additionally, we found 690 meta-differentially expressed genes (mDEGs) shared between drought and salinity stress. Notably, our findings highlight the Early Responsive to Dehydration 15 (ERD15) gene, located in one of the most promising MQTLs, as a key gene in the shared gene network of drought and salinity stress. ERD15, differentially expressed between contrasting wheat genotypes under combined stress conditions, significantly regulates water relations, photosynthetic activity, antioxidant activity, and ion homeostasis. These findings not only provide valuable insights into the molecular genetic mechanisms underlying combined stress tolerance in wheat but also hold the potential to contribute significantly to the development of stress-resilient wheat varieties.

A novel strategy to map a locus associated with flowering time in canola (Brassica napus L.).

Flowering time is an important agronomic trait for canola breeders, as it provides growers with options for minimizing exposure to heat stress during flowering and to more effectively utilize soil moisture. Plants have evolved various systems to control seasonal rhythms in reproductive phenology including an internal circadian clock that responds to environmental signals. In this study, we used canola cultivar 'Westar' as a recurrent parent and canola cultivar 'Surpass 400' as the donor parent to generate a chromosome segment substitution line (CSSL) and to map a flowering time locus on chromosome A10 using molecular marker-assisted selection. This CSSL contains an introgressed 4.6 mega-bases (Mb) segment (between 13 and 17.6 Mb) of Surpass 400, which substantially delayed flowering compared with Westar. To map flowering time gene(s) within this locus, eight introgression lines (ILs) were developed carrying a series of different lengths of introgressed chromosome A10 segments using five co-dominant polymorphic markers located at 13.5, 14.0, 14.5, 15.0, 15.5, and 16.0 Mb. Eight ILs were crossed with Westar reciprocally and flowering time of resultant 16 F1 hybrids and parents were evaluated in a greenhouse (2021 and 2022). Four ILs (IL005, IL017, IL035, and IL013) showed delayed flowering compared to Westar (P < 0.0001), and their reciprocal crosses displayed a phenotype intermediate in flowering time of both homozygote parents. These results indicated that flowering time is partial or incomplete dominance, and the flowering time locus mapped within a 1 Mb region between two co-dominant polymorphic markers at 14.5-15.5 Mb on chromosome A10. The flowering time locus was delineated to be between 14.60 and 15.5 Mb based on genotypic data at the crossover site, and candidate genes within this region are associated with flowering time in canola and/or Arabidopsis. The co-dominant markers identified on chromosome A10 should be useful for marker assisted selection in breeding programs but will need to be validated to other breeding populations or germplasm accessions of canola.

Identification and functional analysis of GmPasL regulating pod color in vegetable soybean.

BACKGROUND: Vegetable soybean is rich in nutrients and has a unique flavor. It is highly preferred by people because of its pharmacological activities, including those that regulate the intestines and lower blood pressure. The pod color of vegetable soybeans is an important quality that indicates their freshness and has a significant impact on their commercialization. RESULTS: In this study, pod color was evaluated in 301 vegetable soybean accessions collected from various regions. Genome-wide association analysis was carried out using the Mixed linear model (MLM), a total of 18 quantitative trait loci including 117 SNPs were detected. Two significant QTLs located on chromosomes 6 (qGPCL4 /qGPCa1/qGPCb2) and 18 (qGPCL10/qGPCb3) were consistently detected across different variables. Based on gene functional annotation, 30 candidate genes were identified in these two candidate intervals. Combined with transcriptome analysis, Glyma.18g241700 has been identified as a candidate gene for regulating pod color in vegetable soybeans. Glyma.18g241700 encodes a chlorophyll photosystem I subunit XI. which localizes to the chloroplast named GmPsaL, qRT-PCR analysis showed that GmPsaL was specifically highly expressed in developing pods. Furthermore, overexpression of GmPsaL in transgenetic Arabidopsis plants produced dark green pods. CONCLUSIONS: These findings may be useful for clarifying the genetic basis of the pod color of vegetable soybeans. The identified candidate genes may be useful for the genetic improvement of the appearance quality of vegetable soybeans.

Genetic Variants in METTL16 Affect the Risk of Non-Syndromic Orofacial Clefts.

OBJECTIVE: N6-methyladenosine (m6A) is the most prevalent modification of RNA in eukaryotes which is associated with many cellular processes and diseases. Here, our objective is to explore whether genetic variants in m6A modification genes are associated with the risk of non-syndrome orofacial clefts (NSOCs). METHODS: The transmission disequilibrium test (TDT) was performed to calculate the association between single nucleotide polymorphisms (SNPs) in m6A modification genes and NSOCs risk in 944 case-parent trios. The function of SNP was predicted by HaploReg, RegulomeDB and histone enrichment data. The expression quantitative trait locus (eQTL) analysis was examined using Genotype-Tissue Expression (GTEx) and eQTLGen. The role of gene in the development of NSOCs was assessed with correlation and enrichment analysis based on gene expression data in mice craniofacial tissue and zebrafish embryo. RESULTS: We identified that rs8078195 (A > C) in METTL16 was suggestively associated with the increased risk of NSOCs (OR = 1.32, p = 1.80E - 03). The region surrounding rs8078195 was subjected to deoxyribonuclease hypersensitivity and enriched with multiple histone modifications. In addition, it had a significant eQTL effect with METTL16 in skin tissue and human peripheral blood, which played an important role in NSOCs development. Bioinformatic analysis indicated that METTL16 contributed to the development of NSOCs probably by regulating cell cycle process. CONCLUSIONS: Rs8078195 in METTL16 was associated with the occurrence of NSOCs.

BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies.

Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits. However, most existing multilocus methods require relatively long computational time when analyzing large datasets. To address this issue, in this study, we proposed a fast mrMLM method, namely, best linear unbiased prediction multilocus random-SNP-effect mixed linear model (BLUPmrMLM). First, genome-wide single-marker scanning in mrMLM was replaced by vectorized Wald tests based on the best linear unbiased prediction (BLUP) values of marker effects and their variances in BLUPmrMLM. Then, adaptive best subset selection (ABESS) was used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical Bayes. Finally, shared memory and parallel computing schemes were used to reduce the computational time. In simulation studies, BLUPmrMLM outperformed GEMMA, EMMAX, mrMLM, and FarmCPU as well as the control method (BLUPmrMLM with ABESS removed), in terms of computational time, power, accuracy for estimating quantitative trait nucleotide positions and effects, false positive rate, false discovery rate, false negative rate, and F1 score. In the reanalysis of two large rice datasets, BLUPmrMLM significantly reduced the computational time and identified more previously reported genes, compared with the aforementioned methods. This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets. The software mrMLM v5.1 is available at BioCode (https://ngdc.cncb.ac.cn/biocode/tool/BT007388) or GitHub (https://github.com/YuanmingZhang65/mrMLM).

QTL mapping and candidate gene mining of seed size and seed weight in castor plant (Ricinus communis L.).

BACKGROUND: Castor (Ricinus communis L., 2n = 2x = 20) is an important industrial crop, due to its oil is very important to the global special chemical industry. Seed size and seed weight are fundamentally important in determining castor yield, while little is known about it. In this study, QTL analysis and candidate gene mining of castor seed size and seed weight were conducted with composite interval mapping (CIM), inclusive composite interval mapping (ICIM) and marker enrichment strategy in 4 populations, i.e., populations F2, BC1, S1-1 and S1-2, derived from 2 accessions with significant phenotypic differences. RESULTS: In the QTL primary mapping, 2 novel QTL clusters were detected in marker intervals RCM520-RCM76 and RCM915-RCM950. In order to verify their accuracy and to narrow their intervals, QTL remapping was carried out in populations F2 and BC1. Among them, 44 and 30 QTLs underlying seed size and seed weight were detected in F2 population using methods CIM and ICIM-ADD respectively, including 4-9 and 3-5 ones conferring each trait were identified with a phenotypic variation explained ranged from 37.92 to 115.81% and 32.86-45.98% respectively. The remapping results in BC1 population were consistent with those in F2 population. Importantly, 3 QTL clusters (i.e. QTL-cluster1, QTL-cluster2 and QTL-cluster3) were found in marker intervals RCM74-RCM76 (37.1 kb), RCM930-RCM950 (259.8 kb) and RCM918-RCM920 (172.9 kb) respectively; in addition, all of them were detected again, the former one was found in the S1-2 population, and the latter two were found simultaneously in the populations S1-1 and S1-2. Finally, 6 candidate genes (i.e. LOC8266555, LOC8281168, LOC8281151, LOC8259066, LOC8258591 and LOC8270077) were screened in the above QTL clusters, they were differentially expressed in multiple seed tissues of both parents, signifying the potential role in regulating seed size and seed weight. CONCLUSION: The above results not only provide new insights into the genetic structure of seed size and seed weight in castor, but also lay the foundation for the functional identification of these candidate genes.

Ustilaginoidea virens, an emerging pathogen of rice: the dynamic interplay between the pathogen virulence strategies and host defense.

MAIN CONCLUSION: The Ustilaginoidea virens -rice pathosystem has been used as a model for flower-infecting fungal pathogens. The molecular biology of the interactions between U. virens and rice, with an emphasis on the attempt to get a deeper comprehension of the false smut fungus's genomes, proteome, host range, and pathogen biology, has been investigated. Meta-QTL analysis was performed to identify potential QTL hotspots for use in marker-assisted breeding. The Rice False Smut (RFS) caused by the fungus Ustilaginoidea virens currently threatens rice cultivators across the globe. RFS infects rice panicles, causing a significant reduction in grain yield. U. virens can also parasitize other hosts though they play only a minor role in its life cycle. Furthermore, because it produces mycotoxins in edible rice grains, it puts both humans and animals at risk of health problems. Although fungicides are used to control the disease, some fungicides have enabled the pathogen to develop resistance, making its management challenging. Several QTLs have been reported but stable gene(s) that confer RFS resistance have not been discovered yet. This review offers a comprehensive overview of the pathogen, its virulence mechanisms, the genome and proteome of U. virens, and its molecular interactions with rice. In addition, information has been compiled on reported resistance QTLs, facilitating the development of a consensus genetic map using meta-QTL analysis for identifying potential QTL hotspots. Finally, this review highlights current developments and trends in U. virens-rice pathosystem research while identifying opportunities for future investigations.

QTL mapping for seed vigor-related traits under artificial aging in common wheat in two introgression line (IL) populations.

Background: Seed vigor recognized as a quantitative trait is of particular importance for agricultural production. However, limited knowledge is available for understanding genetic basis of wheat seed vigor. Methods: The aim of this study was to identify quantitative trait loci (QTL) responsible for 10 seed vigor-related traits representing multiple aspects of seed-vigor dynamics during artificial aging with 6 different treatment times (0, 24, 36, 48, 60, and 72 h) under controlled conditions (48 °C, 95% humidity, and dark). The mapping populations were two wheat introgression lines (IL-1 and IL-2) derived from recipient parent (Lumai 14) and donor parent (Shaanhan 8675 or Jing 411). Results: A total of 26 additive QTLs and 72 pairs of epistatic QTLs were detected for wheat seed-vigor traits. Importantly, chromosomes 1B and 7B contained several co-located QTLs, and chromosome 2A had a QTL-rich region near the marker Xwmc667, indicating that these QTLs may affect wheat seed vigor with pleiotropic effects. Furthermore, several possible consistent QTLs (hot-spot regions) were examined by comparison analysis of QTLs detected in this study and reported previously. Finally, a set of candidate genes for wheat seed vigor were predicted to be involved in transcription regulation, carbohydrate and lipid metabolism. Conclusion: The present findings lay new insights into the mechanism underlying wheat seed vigor, providing valuable information for wheat genetic improvement especially marker-assisted breeding to increase seed vigor and consequently achieve high grain yield despite of further investigation required.

Identification of quantitative trait loci and candidate genes for pod shatter resistance in Brassica carinata.

BACKGROUND: Understanding the genetic control of pod shatter resistance and its association with pod length is crucial for breeding improved pod shatter resistance and reducing pre-harvest yield losses due to extensive shattering in cultivars of Brassica species. In this study, we evaluated a doubled haploid (DH) mapping population derived from an F1 cross between two Brassica carinata parental lines Y-BcDH64 and W-BcDH76 (YWDH), originating from Ethiopia and determined genetic bases of variation in pod length and pod shatter resistance, measured as rupture energy. The YWDH population, its parental lines and 11 controls were grown across three years for genetic analysis. RESULTS: By using three quantitative trait loci (QTL) analytic approaches, we identified nine genomic regions on B02, B03, B04, B06, B07 and C01 chromosomes for rupture energy that were repeatedly detected across three growing environments. One of the QTL on chromosome B07, flanked with DArTseq markers 100,046,735 and 100,022,658, accounted for up to 27.6% of genetic variance in rupture energy. We observed no relationship between pod length and rupture energy, suggesting that pod length does not contribute to variation in pod shatter resistance. Comparative mapping identified six candidate genes; SHP1 on B6, FUL and MAN on chromosomes B07, IND and NST2 on B08, and MAN7 on C07 that mapped within 0.2 Mb from the QTL for rupture energy. CONCLUSION: The results suggest that favourable alleles of stable QTL on B06, B07, B08 and C01 for pod shatter resistance can be incorporated into the shatter-prone B. carinata and its related species to improve final seed yield at harvest.

DeepGenomeScan of 15 Worldwide Bovine Populations Detects Spatially Varying Positive Selection Signals.

Identifying genomic regions under selection is essential for understanding the genetic mechanisms driving species evolution and adaptation. Traditional methods often fall short in detecting complex, spatially varying selection signals. Recent advances in deep learning, however, present promising new approaches for uncovering subtle selection signals that traditional methods might miss. In this study, we utilized the deep learning framework DeepGenomeScan to detect spatially varying selection signatures across 15 bovine populations worldwide. Our analysis uncovered novel insights into selective sweep hotspots within the bovine genome, revealing key genes associated with physiological and adaptive traits that were previously undetected. We identified significant quantitative trait loci linked to milk protein and fat percentages. By comparing the selection signatures identified in this study with those reported in the Bovine Genome Variation Database, we discovered 38 novel genes under selection that were not identified through traditional methods. These genes are primarily associated with milk and meat yield and quality. Our findings enhance our understanding of spatially varying selection's impact on bovine genomic diversity, laying a foundation for future research in genetic improvement and conservation. This is the first deep learning-based study of selection signatures in cattle, offering new insights for evolutionary and livestock genomics research.

Trait association and prediction through integrative k-mer analysis.

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

Protocol to search for genetic factors related to severe COVID-19 by analyzing publicly available genome-wide association studies.

COVID-19 casualties vary among different ancestral groups due to a variety of factors. Here, we present a protocol for analyzing publicly available genome-wide association studies (GWASs) to search for ancestry-specific genetic factors related to severe COVID-19. We describe steps for downloading and comparing two COVID-19 GWASs, calculating expression quantitative trait loci, and single-cell gene expression analysis. We demonstrate this approach using GWASs from Host Genetics Initiative; however, it is applicable to other databases such as the UK Biobank. For complete details on the use and execution of this protocol, please refer to Cheng et al.1.

Analysis of Rice Blast Fungus Genetic Diversity and Identification of a Novel Blast Resistance OsDRq12 Gene.

The rice blast fungus Magnaporthe oryzae poses a significant challenge to maintaining rice production. Developing rice varieties with resistance to this disease is crucial for its effective control. To understand the genetic variability of blast isolates collected between 2015 and 2017, the 27 monogenic rice lines that carry specific resistance genes were used to evaluate blast disease reactions. Based on criteria such as viability, virulence, and reactions to resistance genes, 20 blast isolates were selected as representative strains. To identify novel resistance genes, a quantitative trait locus analysis was carried out utilizing a mixture of the 20 representative rice blast isolates and a rice population derived from crossing the blast-resistant cultivar 'Cheongcheong' with the blast-susceptible cultivar 'Nagdong'. This analysis revealed a significant locus, RM1227-RM1261 on chromosome 12, that is associated with rice blast resistance. Within this locus, 12 disease resistance-associated protein genes were identified. Among them, OsDRq12, a member of the nucleotide-binding, leucine-rich repeat disease resistance family, was chosen as the target gene for additional computational investigation. The findings of this study have significant implications for enhancing rice production and ensuring food security by controlling rice blast and developing resistant rice cultivars.

Genetic, molecular and physiological crosstalk during drought tolerance in maize (Zea mays): pathways to resilient agriculture.

MAIN CONCLUSION: This review comprehensively elucidates maize drought tolerance mechanisms, vital for global food security. It highlights genetic networks, key genes, CRISPR-Cas applications, and physiological responses, guiding resilient variety development. Maize, a globally significant crop, confronts the pervasive challenge of drought stress, impacting its growth and yield significantly. Drought, an important abiotic stress, triggers a spectrum of alterations encompassing maize's morphological, biochemical, and physiological dimensions. Unraveling and understanding these mechanisms assumes paramount importance for ensuring global food security. Approaches like developing drought-tolerant varieties and harnessing genomic and molecular applications emerge as effective measures to mitigate the negative effects of drought. The multifaceted nature of drought tolerance in maize has been unfolded through complex genetic networks. Additionally, quantitative trait loci mapping and genome-wide association studies pinpoint key genes associated with drought tolerance, influencing morphophysiological traits and yield. Furthermore, transcription factors like ZmHsf28, ZmNAC20, and ZmNF-YA1 play pivotal roles in drought response through hormone signaling, stomatal regulation, and gene expression. Genes, such as ZmSAG39, ZmRAFS, and ZmBSK1, have been reported to be pivotal in enhancing drought tolerance through diverse mechanisms. Integration of CRISPR-Cas9 technology, targeting genes like gl2 and ZmHDT103, emerges as crucial for precise genetic enhancement, highlighting its role in safeguarding global food security amid pervasive drought challenges. Thus, decoding the genetic and molecular underpinnings of drought tolerance in maize sheds light on its resilience and paves the way for cultivating robust and climate-smart varieties, thus safeguarding global food security amid climate challenges. This comprehensive review covers quantitative trait loci mapping, genome-wide association studies, key genes and functions, CRISPR-Cas applications, transcription factors, physiological responses, signaling pathways, offering a nuanced understanding of intricate mechanisms involved in maize drought tolerance.

A microRNA396b-growth regulating factor module controls castor seed size by mediating auxin synthesis.

Castor (Ricinus communis L.) is an importance crop cultivated for its oil and economic value. Seed size is a crucial factor that determines crop yield. Gaining insight into the molecular regulatory processes of seed development is essential for the genetic enhancement and molecular breeding of castor. Here, we successfully fine-mapped a major QTL related to seed size, qSS3, to a 180 kb interval on chromosome 03 using F2 populations (DL01×WH11). A 17.6-kb structural variation (SV) was detected through genomic comparison between DL01 and WH11. Analysis of haplotypes showed that the existence of the complete 17.6 kb structural variant may lead to the small seed characteristic in castor. In addition, we found that qSS3 contains the microRNA396b (miR396b) sequence, which is situated within the 17.6 kb SV. The results of our experiment offer additional evidence that miR396-Growth Regulating Factor 4 (GRF4) controls seed size by impacting the growth and multiplication of seed coat and endosperm cells. Furthermore, we found that RcGRF4 activates the expression of YUCCA6 (YUC6), facilitating the production of IAA in seeds and thereby impacting the growth of castor seeds. Our research has discovered a crucial functional module that controls seed size, offering a fresh understanding of the mechanism underlying seed size regulation in castor.