The DEP1 Mutation Improves Stem Lodging Resistance and Biomass Saccharification by Affecting Cell Wall Biosynthesis in Rice.

BACKGROUND: Plant cell walls have evolved precise plasticity in response to environmental stimuli. The plant heterotrimeric G protein complexes could sense and transmit extracellular signals to intracellular signaling systems, and activate a series of downstream responses. dep1 (Dense and Erect Panicles 1), the gain-of-function mutation of DEP1 encoding a G protein γ subunit, confers rice multiple improved agronomic traits. However, the effects of DEP1 on cell wall biosynthesis and wall-related agronomic traits remain largely unknown. RESULTS: In this study, we showed that the DEP1 mutation affects cell wall biosynthesis, leading to improved lodging resistance and biomass saccharification. The DEP1 is ubiquitously expressed with a relatively higher expression level in tissues rich in cell walls. The CRISPR/Cas9 editing mutants of DEP1 (dep1-cs) displayed a significant enhancement in stem mechanical properties relative to the wild-type, leading to a substantial improvement in lodging resistance. Cell wall analyses showed that the DEP1 mutation increased the contents of cellulose, hemicelluloses, and pectin, and reduced lignin content and cellulose crystallinity (CrI). Additionally, the dep1-cs seedlings exhibited higher sensitivity to cellulose biosynthesis inhibitors, 2,6-Dichlorobenzonitrile (DCB) and isoxaben, compared with the wild-type, confirming the role of DEP1 in cellulose deposition. Moreover, the DEP1 mutation-mediated alterations of cell walls lead to increased enzymatic saccharification of biomass after the alkali pretreatment. Furthermore, the comparative transcriptome analysis revealed that the DEP1 mutation substantially altered expression of genes involved in carbohydrate metabolism, and cell wall biosynthesis. CONCLUSIONS: Our findings revealed the roles of DEP1 in cell wall biosynthesis, lodging resistance, and biomass saccharification in rice and suggested genetic modification of DEP1 as a potential strategy to develop energy rice varieties with high lodging resistance.

Assessment of nomogram model for the prediction of esophageal variceal hemorrhage in hepatitis B-induced hepatic cirrhosis.

BACKGROUND: Esophageal variceal (EV) hemorrhage is a life-threatening consequence of portal hypertension in hepatitis B virus (HBV) -induced cirrhotic patients. Screening upper endoscopy and endoscopic variceal ligation to find EVs for treatment have complications, contraindications, and high costs. We sought to identify the nomogram models (NMs) as alternative predictions for the risk of EV hemorrhage. METHODS: In this case-control study, we retrospectively analyzed 241 HBV-induced liver cirrhotic patients treated for EVs at the Second People's Hospital of Fuyang City, China from January 2021 to April 2023. We applied univariate analysis and multivariate logistic regression to assess the accuracy of various NMs in EV hemorrhage. The area under the curve (AUC) and calibration curves of the receiver's operating characteristics were used to evaluate the predictive accuracy of the nomogram. Decision curve analysis (DCA) was used to determine the clinically relevant of nomograms. RESULTS: In the prediction group, multivariate logistic regression analysis identified platelet distribution and spleen length as independent risk factors for EVs. We applied NMs as the independent risk factors to predict EVs risk. The NMs fit well with the calibration curve and have good discrimination ability. The AUC and DCA demonstrated that NMs with a good net benefit. The above results were validated in the validation cohort. CONCLUSION: Our non-invasive NMs based on the platelet distribution width and spleen length may be used to predict EV hemorrhage in HBV-induced cirrhotic patients. NMs can help clinicians to increase diagnostic performance leading to improved treatment measures.

Machine learning-assisted high-content imaging analysis of 3D MCF7 microtissues for estrogenic effect prediction.

Endocrine-disrupting chemicals (EDCs) pose a significant threat to human well-being and the ecosystem. However, in managing the many thousands of uncharacterized chemical entities, the high-throughput screening of EDCs using relevant biological endpoints remains challenging. Three-dimensional (3D) culture technology enables the development of more physiologically relevant systems in more realistic biochemical microenvironments. The high-content and quantitative imaging techniques enable quantifying endpoints associated with cell morphology, cell-cell interaction, and microtissue organization. In the present study, 3D microtissues formed by MCF-7 breast cancer cells were exposed to the model EDCs estradiol (E2) and propyl pyrazole triol (PPT). A 3D imaging and image analysis pipeline was established to extract quantitative image features from estrogen-exposed microtissues. Moreover, a machine-learning classification model was built using estrogenic-associated differential imaging features. Based on 140 common differential image features found between the E2 and PPT group, the classification model predicted E2 and PPT exposure with AUC-ROC at 0.9528 and 0.9513, respectively. Deep learning-assisted analysis software was developed to characterize microtissue gland lumen formation. The fully automated tool can accurately characterize the number of identified lumens and the total luminal volume of each microtissue. Overall, the current study established an integrated approach by combining non-supervised image feature profiling and supervised luminal volume characterization, which reflected the complexity of functional ER signaling and highlighted a promising conceptual framework for estrogenic EDC risk assessment.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter.

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

INTEDE 2.0: the metabolic roadmap of drugs.

The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.

Decoding Drug Response with Structurized Gridding Map-based Cell Representation.

A thorough understanding of cell-line drug response mechanisms is crucial for drug development, repurposing, and resistance reversal. While targeted anticancer therapies have shown promise, not all cancers have well-established biomarkers to stratify drug response. Single-gene associations only explain a small fraction of the observed drug sensitivity, so a more comprehensive method is needed. However, while deep learning models have shown promise in predicting drug response in cell lines, they still face significant challenges when it comes to their application in clinical applications. Therefore, this study proposed a new strategy called DD-Response for cell-line drug response prediction. First, a limitation of narrow modeling horizons was overcome to expand the model training domain by integrating multiple datasets through source-specific label binarization. Second, a modified representation based on a two-dimensional structurized gridding map (SGM) was developed for cell lines & drugs, avoiding feature correlation neglect and potential information loss. Third, a dual-branch, multi-channel convolutional neural network-based model for pairwise response prediction was constructed, enabling accurate outcomes and improved exploration of underlying mechanisms. As a result, the DD-Response demonstrated superior performance, captured cell-line characteristic variations, and provided insights into key factors impacting cell-line drug response. In addition, DD-Response exhibited scalability in predicting clinical patient responses to drug therapy. Overall, because of DD-response's excellent ability to predict drug response and capture key molecules behind them, DD-response is expected to greatly facilitate drug discovery, repurposing, resistance reversal, and therapeutic optimization.

The Resistance of Maize to Ustilago maydis Infection Is Correlated with the Degree of Methyl Esterification of Pectin in the Cell Wall.

Common smut caused by Ustilago maydis is one of the dominant fungal diseases in plants. The resistance mechanism to U. maydis infection involving alterations in the cell wall is poorly studied. In this study, the resistant single segment substitution line (SSSL) R445 and its susceptible recurrent parent line Ye478 of maize were infected with U. maydis, and the changes in cell wall components and structure were studied at 0, 2, 4, 8, and 12 days postinfection. In R445 and Ye478, the contents of cellulose, hemicellulose, pectin, and lignin increased by varying degrees, and pectin methylesterase (PME) activity increased. The changes in hemicellulose and pectin in the cell wall after U. maydis infection were analyzed via immunolabeling using monoclonal antibodies against hemicellulsic xylans and high/low-methylated pectin. U. maydis infection altered methyl esterification of pectin, and the degree of methyl esterification was correlated with the resistance of maize to U. maydis. Furthermore, the relationship between methyl esterification of pectin and host resistance was validated using 15 maize inbred lines with different resistance levels. The results revealed that cell wall components, particularly pectin, were important factors affecting the colonization and propagation of U. maydis in maize, and methyl esterification of pectin played a role in the resistance of maize to U. maydis infection.

Machine Learning-Assisted High-Content Imaging Analysis of 3D MCF7 Microtissues for Estrogenic Effect Prediction.

Endocrine-disrupting chemicals (EDCs) pose a significant threat to human well-being and the ecosystem. However, in managing the many thousands of uncharacterized chemical entities, the high-throughput screening of EDCs using relevant biological endpoints remains challenging. Three-dimensional (3D) culture technology enables the development of more physiologically relevant systems in more realistic biochemical microenvironments. The high-content and quantitative imaging techniques enable quantifying endpoints associated with cell morphology, cell-cell interaction, and microtissue organization. In the present study, 3D microtissues formed by MCF-7 breast cancer cells were exposed to the model EDCs estradiol (E2) and propyl pyrazole triol (PPT). A 3D imaging and image analysis pipeline was established to extract quantitative image features from estrogen-exposed microtissues. Moreover, a machine-learning classification model was built using estrogenic-associated differential imaging features. Based on 140 common differential image features found between the E2 and PPT group, the classification model predicted E2 and PPT exposure with AUC-ROC at 0.9528 and 0.9513, respectively. Deep learning-assisted analysis software was developed to characterize microtissue gland lumen formation. The fully automated tool can accurately characterize the number of identified lumens and the total luminal volume of each microtissue. Overall, the current study established an integrated approach by combining non-supervised image feature profiling and supervised luminal volume characterization, which reflected the complexity of functional ER signaling and highlighted a promising conceptual framework for estrogenic EDC risk assessment.

A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites.

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.

The Fragile culm19 (FC19) mutation largely improves plant lodging resistance, biomass saccharification, and cadmium resistance by remodeling cell walls in rice.

Cell wall is essential for plant upright growth, biomass saccharification, and stress resistance. Although cell wall modification is suggested as an effective means to increase biomass saccharification, it is a challenge to maintain normal plant growth with improved mechanical strength and stress resistance. Here, we reported two independent fragile culm mutants, fc19-1 and fc19-2, resulting from novel mutations of OsIRX10, produced by the CRISPR/Cas9 system. Compared to wild-type, the two mutants exhibited reduced contents of xylose, hemicellulose, and cellulose, and increased arabinose and lignin without significant alteration in levels of pectin and uronic acids. Despite brittleness, the mutants displayed increased breaking force, leading to improved lodging resistance. Furthermore, the altered cell wall and increased biomass porosity in fc19 largely increased biomass saccharification. Notably, the mutants showed enhanced cadmium (Cd) resistance with lower Cd accumulation in roots and shoots. The FC19 mutation impacts transcriptional levels of key genes contributing to Cd uptake, sequestration, and translocation. Moreover, transcriptome analysis revealed that the FC19 mutation resulted in alterations of genes mainly involved in carbohydrate and phenylpropanoid metabolism. Therefore, a hypothetic model was proposed to elucidate that the FC19 mutation-mediated cell wall remodeling leads to improvements in lodging resistance, biomass saccharification, and Cd resistance.

Natural variation in OsSEC13 HOMOLOG 1 modulates redox homeostasis to confer cold tolerance in rice.

Rice (Oryza sativa L.) is a cold-sensitive species that often faces cold stress, which adversely affects yield productivity and quality. However, the genetic basis for low-temperature adaptation in rice remains unclear. Here, we demonstrate that 2 functional polymorphisms in O. sativa SEC13 Homolog 1 (OsSEH1), encoding a WD40-repeat nucleoporin, between the 2 subspecies O. sativa japonica and O. sativa indica rice, may have facilitated cold adaptation in japonica rice. We show that OsSEH1 of the japonica variety expressed in OsSEH1MSD plants (transgenic line overexpressing the OsSEH1 allele from Mangshuidao [MSD], cold-tolerant landrace) has a higher affinity for O. sativa metallothionein 2b (OsMT2b) than that of OsSEH1 of indica. This high affinity of OsSEH1MSD for OsMT2b results in inhibition of OsMT2b degradation, with decreased accumulation of reactive oxygen species and increased cold tolerance. Transcriptome analysis indicates that OsSEH1 positively regulates the expression of the genes encoding dehydration-responsive element-binding transcription factors, i.e. OsDREB1 genes, and induces the expression of multiple cold-regulated genes to enhance cold tolerance. Our findings highlight a breeding resource for improving cold tolerance in rice.

SISPRO: Signature Identification for Spatial Proteomics.

Spatial proteomics aims for a global description of organelle-specific protein distribution and dynamics, which is essential for understanding the molecular functions and cellular processes in health and disease. However, the application of this technique is seriously restricted by the neglect of robustness among proteomic signatures identified using standard statistical frameworks. Moreover, it is still a major bottleneck to automatically interpretate the identified proteomic signatures due to lack of integration of subcellular information. Herein, an online-tool SISPRO was constructed to (a) identify proteomic signatures with good robustness and accuracy via collectively evaluating relative weighted consistency (CWrel) & area under the curve (AUC) and (b) interpretate the identified signature based on comprehensive subcellular information from 9 organelles and 22 subcellular structures. All in all, SISPRO provides the endeavor to realize the simultaneous improvement of robustness and accuracy in signature identification and the unique capacity in biological annotation lies in its wide coverage of proteins and comprehensive spatial information. SISPRO is expected to be critical in spatial proteomic studies, which can be freely accessed without any login requirement at https://idrblab.org/sispro/.

State-of-the-art Application of Artificial Intelligence to Transporter-centered Functional and Pharmaceutical Research.

Protein transporters not only have essential functions in regulating the transport of endogenous substrates and remote communication between organs and organisms, but they also play a vital role in drug absorption, distribution, and excretion and are recognized as major determinants of drug safety and efficacy. Understanding transporter function is important for drug development and clarifying disease mechanisms. However, the experimental-based functional research on transporters has been challenged and hinged by the expensive cost of time and resources. With the increasing volume of relevant omics datasets and the rapid evolution of artificial intelligence (AI) techniques, next-generation AI is becoming increasingly prevalent in the functional and pharmaceutical research of transporters. Thus, a comprehensive discussion on the state-of-the-art application of AI in three cutting-edge directions was provided in this review, which included (a) transporter classification and function annotation, (b) structure discovery of membrane transporters, and (c) drug-transporter interaction prediction. This study provides a panoramic view of AI algorithms and tools applied to the field of transporters. It is expected to guide a better understanding and utilization of AI techniques for in-depth studies of transporter-centered functional and pharmaceutical research.

Single-molecular insights into the breakpoint of cellulose nanofibers assembly during saccharification.

Plant cellulose microfibrils are increasingly employed to produce functional nanofibers and nanocrystals for biomaterials, but their catalytic formation and conversion mechanisms remain elusive. Here, we characterize length-reduced cellulose nanofibers assembly in situ accounting for the high density of amorphous cellulose regions in the natural rice fragile culm 16 (Osfc16) mutant defective in cellulose biosynthesis using both classic and advanced atomic force microscopy (AFM) techniques equipped with a single-molecular recognition system. By employing individual types of cellulases, we observe efficient enzymatic catalysis modes in the mutant, due to amorphous and inner-broken cellulose chains elevated as breakpoints for initiating and completing cellulose hydrolyses into higher-yield fermentable sugars. Furthermore, effective chemical catalysis mode is examined in vitro for cellulose nanofibers conversion into nanocrystals with reduced dimensions. Our study addresses how plant cellulose substrates are digestible and convertible, revealing a strategy for precise engineering of cellulose substrates toward cost-effective biofuels and high-quality bioproducts.

DRESIS: the first comprehensive landscape of drug resistance information.

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.

DrugMAP: molecular atlas and pharma-information of all drugs.

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

Time-ordering japonica/geng genomes analysis indicates the importance of large structural variants in rice breeding.

Temperate japonica/geng (GJ) rice yield has significantly improved due to intensive breeding efforts, dramatically enhancing global food security. However, little is known about the underlying genomic structural variations (SVs) responsible for this improvement. We compared 58 long-read assemblies comprising cultivated and wild rice species in the present study, revealing 156 319 SVs. The phylogenomic analysis based on the SV dataset detected the putatively selected region of GJ sub-populations. A significant portion of the detected SVs overlapped with genic regions were found to influence the expression of involved genes inside GJ assemblies. Integrating the SVs and causal genetic variants underlying agronomic traits into the analysis enables the precise identification of breeding signatures resulting from complex breeding histories aimed at stress tolerance, yield potential and quality improvement. Further, the results demonstrated genomic and genetic evidence that the SV in the promoter of LTG1 is accounting for chilling sensitivity, and the increased copy numbers of GNP1 were associated with positive effects on grain number. In summary, the current study provides genomic resources for retracing the properties of SVs-shaped agronomic traits during previous breeding procedures, which will assist future genetic, genomic and breeding research on rice.

CovInter: interaction data between coronavirus RNAs and host proteins.

Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.

Biochar application enhanced rice biomass production and lodging resistance via promoting co-deposition of silica with hemicellulose and lignin.

Biochar, an environmentally friendly soil amendment, is created via a series of thermochemical processes from carbon-rich organic matter. The biochar addition enhances soil characteristics dramatically and increases crop growth and yields. However, the mechanism by which biochar improves plant lodging resistance, which is heavily influenced by cell walls, remains unknown. Three rice cultivars were grown in an experimental field provided with four concentrations of biochar (10, 20, 30, 40 t ha-1). The biochar application enhanced biomass production and lodging resistance in all three cultivars by up to 29 % and 22 %, respectively, with the largest improvement at a biochar application rate of 30 t ha-1. Biochar application significantly enhanced stem cell wall-related characteristics, with an increase in stem breaking force, wall thickness, and plumpness of 52 %, 32 %, and 21 %, respectively, which are suggested to be major contributors to enhanced lodging resistance and biomass yield. Notably, cell wall composition and silica content analysis indicated a significant increase in hemicellulose, lignin, and silica content in biochar-treated samples up to 36 %, 13 %, and 58 %, respectively, when compared to plants not treated with biochar. Integrative analysis suggested that silica, hemicellulose, and lignin were co-deposited in cell walls, which influenced biomass production and lodging resistance. Furthermore, the transcriptome profile revealed that biochar application increased the expression of genes involved in biomass production, cell wall formation, and silica deposition. This study suggests that biochar application might improve both biomass production and lodging resistance by promoting the co-deposition of silicon with hemicellulose and lignin in cell walls.

Introgression of a Complex Genomic Structural Variation Causes Hybrid Male Sterility in GJ Rice (Oryza sativa L.) Subspecies.

Hybrids between different subspecies of rice Oryza sativa L. commonly show hybrid sterility. Here we show that a widely planted commercial japonica/GJ variety, DHX2, exhibited hybrid sterility when crossing with other GJ varieties. Using the high-quality genome assembly, we identified three copies of the Sc gene in DHX2, whereas Nipponbare (Nip) had only one copy of Sc. Knocking out the extra copies of Sc in DHX2 significantly improved the pollen fertility of the F1 plant of DHX2/Nip cross. The population structure analysis revealed that a slight introgression from Basmati1 might occur in the genome of DHX2. We demonstrated that both DHX2 and Basmati1 harbored three copies of Sc. Moreover, the introgression of GS3 and BADH2/fgr from Basmati1 confers the slender and fragrance grain of DHX2. These results add to our understanding of the hybrid sterility of inter-subspecies and intra-subspecies and may provide a novel strategy for hybrid breeding.