Next Article in Journal
Regulatory Effects of Companion Plants (Maize (Zea mays) and Perilla frutescens) on American Ginseng Growth and Microbiome in Root Rot-Infested Field
Previous Article in Journal
“End-to-End Chromosome Fusion” as the Main Driver of Descending Dysploidy in Vigna lasiocarpa (Mart. ex Benth.) Verdc. (Leguminosae Juss.)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 14
Issue 12
10.3390/plants14121873
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Analysis of Lodging Resistance in 1892S Based on the T2T Genome: Providing a Genetic Approach for the Improvement of Two-Line Hybrid Rice Varieties

by
Wei Zhang
,
Liang Zhou
,
Dahu Ni
,
Jinlong Ni
,
Fengshun Song
,
Liansong Yang
and
Dewen Zhang
*
Anhui Province Key Laboratory of Rice Genetics and Breeding, Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
*
Author to whom correspondence should be addressed.
Plants 2025, 14(12), 1873; https://doi.org/10.3390/plants14121873
Submission received: 1 May 2025 / Revised: 10 June 2025 / Accepted: 13 June 2025 / Published: 18 June 2025
Browse Figures
Versions Notes

Abstract

:
Successfully breeding high-yield, lodging-resistant hybrid rice varieties is critical for ensuring food security. Two-line hybrid rice system plays an essential role in rice breeding, and 1892S, an important two-line sterile line, has contributed significantly to the development of over 100 hybrid rice varieties with superior agronomic traits, including lodging resistance. Despite its importance, a comprehensive understanding of the genomic basis underlying these traits in 1892S has been lacking due to the limitations of short-read sequencing technologies. To address this gap, we utilized advanced telomere-to-telomere (T2T) genome assembly techniques to generate a high-quality, gap-free genome of 1892S—the final genome comprises 12 complete chromosomes with 40,560 protein-coding genes. Comparative genomic analysis identified multiple known lodging resistance genes, including SD1, Sdt97, SBI, OsFBA2, APO1, and OsTB1, with unique allelic variations that may enhance resistance. The pan-genome analysis identified 2347 strain-specific genes in 1892S, further supporting its unique genetic advantages. This study represents the complete T2T genome assembly of a two-line sterile line and provides novel insights into the genetic foundation of lodging resistance in hybrid rice. This study highlights the genetic potential of 1892S in hybrid rice breeding and provides a model for the genomic analysis of other two-line sterile lines, offering valuable insights for improving in hybrid rice, including traits lodging resistance, yield stability, and adaptability, which are crucial for global food security.

1. Introduction

As one of the world’s most important staple crops, rice is pivotal in ensuring global food security [1]. Enhancing both the yield and quality of rice remains a strategic priority for safeguarding the global food supply, particularly in the face of rising population pressures and escalating environmental challenges [2]. Hybrid rice technology has significantly advanced rice productivity, with the two-line male sterile system standing out as a critical innovation [3,4]. These systems not only simplify the hybridization process but also amplify hybrid vigor and yield potential [5].
The advent of genomic tools and resources has further revolutionized hybrid rice breeding. Recognizing the importance of unraveling the genomic traits of hybrid rice parents, including two-line male sterile lines, researchers have increasingly adopted comprehensive genomic and phenotypic analyses at the whole-genome level [6,7]. Such analyses provide a theoretical framework for precisely breeding hybrid rice varieties. Quantitative trait nucleotide (QTN) loci, which often reside in highly conserved regions and overlap with open chromatin domains, hold promise for identifying functional sites of candidate genes [8]. Innovations like RiceNavi, a genomic navigation system, leverage next-generation sequencing (NGS) data to analyze QTN loci, evaluate breeding strategy success rates, and identify optimal genotypes [9]. Further complementing these advances, third-generation sequencing (TGS) technologies, such as PacBio HiFi and Oxford Nanopore Technologies (ONT), facilitate the assembly of complete genomes with unmatched accuracy, significantly propelling rice genetic breeding [10].
These technological breakthroughs have enabled deeper insights into the genomic foundations of hybrid rice breeding. For instance, Zhang et al. assembled the genomes of multiple two-line and three-line sterile lines, revealing that sterile lines exhibit fewer structural variants (SVs) than restorer lines, with two-line sterile lines displaying the least SVs [11]. This finding underscores the genetic stability of two-line sterile lines and their utility in hybrid breeding. Nevertheless, despite their critical role, comprehensive genomic analyses of two-line sterile lines at the whole-genome level remain sparse, presenting a significant gap in our understanding of their genetic and functional traits.
A notable representative of two-line sterile lines is 1892S, which has profoundly influenced hybrid rice development. As the maternal line for over 100 hybrid rice varieties, 1892S has demonstrated desirable traits such as high yield, strong tillering ability, and excellent lodging resistance [12]. Additionally, many other sterile lines have been developed by modifying 1892S, emphasizing its foundational role in rice breeding. While high-throughput sequencing technologies have been employed to identify beneficial alleles in 1892S, their inherent limitations, including short read lengths and an inability to resolve complex genomic regions, have hindered efforts to fully elucidate its genetic makeup.
To address these challenges, this study employed telomere-to-telomere (T2T) genome assembly technology, integrating long-read sequencing data from PacBio HiFi and ONT platforms with Hi-C chromosomal conformation data [13]. This approach enabled the construction of a high-quality, gap-free genome for 1892S, offering unprecedented genomic resolution. The comprehensive genome was further annotated for protein-coding genes, followed by comparative analyses and detailed investigations of genes associated with lodging resistance.
Lodging poses a significant constraint in rice cultivation, adversely affecting grain yield, quality, and harvesting efficiency. Understanding the genetic basis of lodging resistance is thus imperative for breeding robust rice varieties. This study identified multiple known lodging resistance genes in the 1892S genome, including SD1, Sdt97, SBI, OsFBA2, APO1, and OsTB1, along with unique allelic variations that may confer enhanced resistance [14,15,16,17,18,19]. These findings provide critical insights into the molecular mechanisms underpinning lodging resistance in rice.
In summary, this study presents a comprehensive T2T genome assembly of a two-line sterile line, delivering a high-precision genomic blueprint of 1892S. The results not only enrich the genetic resources available for hybrid rice improvement but also establish a robust platform for future functional genomic studies. By elucidating the genetic architecture of 1892S, this research paves the way for the development of high-yielding, stress-tolerant hybrid rice varieties, contributing to sustainable agriculture and global food security.

2. Results

2.1. A Telomere-to-Telomere Gap-Free Genome for 1892S

The temperature-sensitive two-line sterile line, 1892S, was employed for T2T (telomere-to-telomere) genome assembly. A total of 17.58 Gb (18,876,179,922 bp, ≥10,000 bp, approximately 43.95× coverage) of HiFi reads were generated using the PacBio platform, while 8.08 Gb (8,673,538,823 bp, ≥50,000 bp, approximately 20× coverage) ultra-long ONT reads were produced with the Oxford Nanopore Technology (ONT) platform. In the initial assembly stage, Hifiasm was employed to merge HiFi, ultra-long ONT reads, and Hi-C reads, a strategy that guaranteed the accuracy of the assembled genome. The assembled genome had a total size of 404,410,499 bp, with an N50 size of 31,773,460 bp and a GC content of 43.67%. Hi-C sequencing data was then integrated using the 3D-DNA and Juicer pipelines to facilitate the contigs into chromosomes. After refinement with Juicer-Box, the genome assembly was consolidated into 12 contigs, corresponding to the 12 chromosomes. To ensure a gap-free genome, we used ultra-long ONT reads to close residual gaps. The finalized assembly was compared to the Nipponbare T2T genome using MUMmer, confirming the accuracy of the chromosomes’ number [13] (Figure 1). This comprehensive assembly process successfully generated a high-quality, complete genome for 1892S, serving as a robust foundation for further genetic and functional analyses (Table 1).
Using the characteristic repeat sequences of telomeres (TTTAGGG/TCA), the entire genome was systematically scanned. For each chromosome, regions with at least four repeats of these sequences within the 50 kb flanking both chromosome ends were identified and quantified, as shown in Figure 2. Additionally, HiCAT analysis successfully identified 12 centromeres across the genome. The arrangement of contigs on the chromosomes was visualized, with clear annotations marking the positions of telomeres and centromeres (Figure 2). Ultimately, the complete assembly of the 1892S genome was achieved, comprising 12 chromosomes, 24 telomeres, and 12 centromeres, with a total genome size of 394,819,109 bp (Figure 2, Table 1).

2.2. T2T Genome Assembly Evaluation of 1892S

The genome sequence was divided into 10 kb fragments, and each fragment was aligned to the NCBI nucleotide database (NT library) using BLAST (BLAST 2.15.0+). The analysis revealed that 99.93% of the sequences were identified as belonging to Oryza, accounting for 99.98% of all comparisons, with a median alignment consistency of 99.99%. To further evaluate genome quality, short-read data were aligned to the genome using BWA, achieving a comparison rate of 99.21% and a coverage rate of 99.92%. Similarly, long-read data were aligned using Minimap2, resulting in a comparison rate of 99.90% and a coverage rate of 99.99%. BUSCO analysis demonstrated a genome completeness of 99.19%, indicating high integrity and robustness of the assembly. Additionally, short and long reads were mapped to the genome to calculate homozygous and heterozygous rates for SNPs and InDels. Both homozygous and heterozygous SNP rates were 0.000%, while homozygous and heterozygous InDel rates were 0.001%, reflecting exceptional genome accuracy. Furthermore, the QV (quality value) of 46.492, calculated using Merqury based on short-read data, confirmed the high accuracy and reliability of the assembly. A comprehensive summary of the genome evaluation results for 1892S is presented in Figure 3.

2.3. Coding Gene Annotation of 1892S Genome

Repetitive sequences in the 1892S genome were initially identified de novo using the RepeatModeler tool. The output from RepeatModeler was subsequently processed by RepeatMasker, which quantified and masked repetitive elements, resulting in an annotated genome sequence with repetitive regions highlighted. The annotation process incorporated RNA-seq data and gene structures from NIP-T2T and R498 genomes. The initial protein-coding gene annotation was generated using BRAKER, with input data consisting of: (i) PASA-trained gene models; (ii) intron hints derived from aligned RNA-seq reads, and de novo assembled transcripts; (iii) protein hints obtained by mapping UniProt, NIP-T2T, and R498 protein sequences; and (iv) repeat-masked genome data. Subsequently, EVM was employed to integrate various evidence types, including BRAKER-predicted gene models, full-length sequences, protein sequences, de novo transcripts, and genome-guided transcripts, into consensus gene models. In the final stage, PASA was used to refine EVM predictions by adding UTR annotations, predicting gene coding regions, and capturing transcript isoforms. This comprehensive annotation pipeline resulted in the identification of 40,560 protein-coding genes and 53,849 mRNAs, including isoforms, in the 1892S genome. To assess the completeness of genome assembly and annotation, BUSCO was employed, which leverages an evolutionarily conserved gene set as a benchmark. A BUSCO score of 94.00% (of 1614 genes) was achieved, indicating a high level of completeness in genome annotation, signifying the reliability of the predicted gene models.
EggNOG-mapper was utilized to allocate COG categories, Gene Ontology (GO) categories, and KEGG pathway annotations to all annotated loci. In the EggNOG database, Orthologous Groups (OGs) serve as a proxy for gene families and are instrumental in functional annotation, classification, and evolutionary studies. Among the sequences analyzed, 41,939 mRNAs were successfully mapped to distinct eggNOG_OGs. The annotated genes were assigned putative functions according to the COG database, and these annotations were subsequently visualized using Python (Python 3.9) scripts (Figure 4).

2.4. Detection of 1892S Lodging Resistance Genes

According to the literature, rice lodging resistance-related genes were identified, and further analysis revealed that 1892S harbors six key lodging resistance genes (Table 2). Spontaneous mutations in SD1 predominantly occur in the first and second exon regions [14]. Sequence alignment showed that the sd1 genotype in 1892S corresponds to the 93-11 genotype (Supplemental File S1). Additionally, a point mutation was identified in the 5’ untranslated region (UTR) of the rice semi-dwarfing gene Sdt97, involving a G-to-C transversion at the 59-UTR site [15]. In 1892S, the Sdt97 gene exhibits a C at this specific site, confirming a mutation in its Sdt97 sequence (Supplemental File S2). The amino acid at position 338 in the conserved region of the SBI protein sequence plays a critical role in determining enzyme activity, with arginine (R) at this position [16]. Sequence analysis showed that the SBI gene in 1892S aligns perfectly with the corresponding protein sequence in the R498 variety, confirming that 1892S contains a functional SBI gene (Supplemental File S3). Moreover, the promoter region of the OsFBA2 gene, located at position 5,248,026 on chromosome 7 of Nipponbare, contains a T nucleotide. This SNP at position 5,248,026 is functional, and the OsFBA2 gene is classified as haplotype 3 (hap3) [17]. The corresponding site in 1892S also has a T, indicating that OsFBA2 in 1892S functions as a lodging resistance gene (Supplemental File S4). For the scm2 candidate gene APO1, a 9 bp deletion in the second exon was identified [18]. The corresponding gene in 1892S similarly exhibits a 9 bp deletion in the same exon (Supplemental File S5). Additionally, compared to the Nipponbare genome, the scm3 candidate gene OsTB1 has a 4 bp insertion with the sequence “TGTG” [19]. The corresponding OsTB1gene in 1892S also contains an identical 4 bp insertion (Supplemental File S6). These results collectively demonstrate that 1892S carries multiple lodging resistance genes, highlighting its potential as a valuable genetic resource for improving rice lodging resistance.

2.5. Lodging Resistance Evaluation and Validation of 1892S-Based Hybrid Rice Varieties

1892S is widely used in practical production, and there are more than 100 hybrid rice varieties nationwide with 1892S as the female parent. The hybrid varieties with 1892S as the female parent also exhibit strong lodging resistance. In this study, using the method of identifying lodging resistance (DB34/T 3924-2021), the lodging resistance of multiple varieties was identified. The results showed that the three varieties with 1892S as the female parent, Wandao 153 (CK), Huiliangyou 27 Zhan, Huiliangyou 985, and Huiliangyou Yuehesimiao, all have strong lodging resistance (Table 3).

2.6. Domain-Pan-Genome Dual Screening for Lodging Resistance-Specific Genes

Pan-genome analysis revealed 2,347 strain-specific genes in the 1892S genome, absent in other rice accessions (Supplemental File S7). Pan-genome and whole-genome data integration identified 35 specific genes within lodging resistance-related gene families (Supplemental File S8). Both SD1 and SBI encode proteins containing the DIOX_N and 2OG-FeII_Oxy domains, which are potentially involved in gibberellin biosynthesis regulation (Table 2). Notably, two strain-specific genes in 1892S also harbor these dual domains, suggesting functional conservation or neofunctionalization within this gene family. The SCM2 gene contains an F-box domain, a structural feature linked to cell wall remodeling through ubiquitin-mediated proteolysis. Strikingly, 32 strain-specific genes in 1892S were identified to possess F-box domains, indicating a significant expansion of this functional module. The SDT97 gene carries an Adenine_glyco domain, associated with delayed senescence via adenine glycosylation. A single strain-specific gene in 1892S was found to retain this domain, highlighting its potential role in senescence regulation.

3. Discussion

This study presents the first telomere-to-telomere (T2T) genome assembly of the two-line hybrid rice sterile line 1892S, providing a high-resolution reference for functional genomic research and trait-associated variant discovery. In two-line hybrid rice breeding systems, male sterility is conferred by photoperiod- or thermo-sensitive genic male sterile (P/TGMS) lines. 1892S is a thermo-sensitive genic male sterile (TGMS) line, which becomes male sterile when grown at temperatures above approximately 23.5 °C. Under such conditions, it serves as the female parent in hybrid seed production. When grown under cooler conditions below this threshold, fertility is restored, enabling self-pollination and seed propagation. Hybrid varieties are developed by crossing TGMS lines with fertile pollen parents.
We successfully achieved a high-precision, gap-free genome assembly by leveraging advanced sequencing technologies, including PacBio HiFi, ultra-long Oxford Nanopore Technology (ONT) reads, and Hi-C data. The assembly obtained an exceptional BUSCO completeness score of 99.19% and a QV value of 46.492, demonstrating the robustness and reliability of the assembled genome. This high-quality reference genome provides a valuable resource for functional genomics research, gene discovery, and precision breeding aimed at enhancing hybrid rice traits [20,21].
To identify superior and detrimental genes at the whole-genome level, we employed the RiceNavi system to predict key genetic loci based on second-generation sequencing data. Additionally, second-generation sequencing data from 1892S were analyzed to predict superior alleles [12]. In this study, the 1892S T2T genome served as a reference for genotype analysis based on previously reported genes, revealing more detailed insights compared to previous research. For instance, the identification of lodging resistance genes in 1892S demonstrates the genome’s capacity to provide in-depth analyses of genes associated with this critical trait.
Lodging is a significant agronomic challenge that negatively impacts rice yield, quality, and harvesting efficiency [22]. Our study identified six key lodging resistance genes within the 1892S genome: SD1, Sdt97, SBI, OsFBA2, APO1, and OsTB1, which operate through interconnected molecular pathways, collectively enhancing stem strength and optimizing plant architecture. SD1 encodes GA 20-oxidase, and its loss-of-function mutation (such as the 93-11 allele present in 1892S) reduces bioactive gibberellin levels, leading to semi-dwarf stature and reduced lodging risk [23]. Sdt97, carrying a G-to-C transversion in its 5′ UTR, likely fine-tunes GA biosynthesis post-transcriptionally, providing a balanced regulation of internode elongation and stem stability [24]. Together, these alleles reflect a dual mechanism in GA homeostasis, enhancing plant robustness without significantly compromising yield potential [25]. SBI restricts the elongation of basal internodes and enhances cell wall rigidity by promoting GA 2-oxidase activity, which increases lignin deposition and secondary wall thickening [25]. APO1, which contains an F-box domain, is thought to regulate the stability of cell wall remodeling proteins. The 9-bp exon deletion identified in APO1 in 1892S may alter its protein interaction dynamics, potentially stabilizing proteins that strengthen fiber alignment, a feature commonly associated with stem mechanical strength in cereals [26]. OsTB1 functions as a transcriptional repressor of tiller bud outgrowth. The 4-bp insertion detected in 1892S may enhance its repression activity, resulting in a reduced tiller angle and a more compact canopy that lowers wind-induced mechanical stress [27]. OsFBA2, another F-box gene, contains a promoter SNP (hap3) that may influence photoperiod-sensitive tillering through interaction with florigen-related signaling pathways [28]. These genetic modifications contribute to an upright plant type with improved lodging tolerance while maintaining efficient light capture. Taken together, these gene variants act synergistically, targeting hormone signaling, structural reinforcement, and architectural regulation—key components of a multifaceted genetic strategy against lodging in 1892S. Collectively, these functionally validated genes represent key genetic components of a robust lodging resistance network in 1892S, integrating hormonal, structural, and architectural regulatory mechanisms. Compared to previous studies [12], this research introduces several novel contributions. Both Sdt97 and OsFBA2 were identified as lodging resistance genes through the analysis of the 1892S genome, a discovery not found in earlier studies. This difference is likely due to the methodological approach: while RiceNavi primarily relies on SNP loci, our study leveraged complete gene sequences, including both upstream and downstream regions.
In addition to the analysis of previously reported lodging resistance genes, we also performed a pan-genome-based comparative analysis. Using the core genome defined in a published rice of pan-genome dataset as reference, we predicted 2347 protein-coding genes in 1892S. 35 of them contain conserved domains associated with lodging resistance gene families, suggesting their potential involvement in the regulation of lodging-related traits. These candidates may contribute to the superior performance of 1892S in hybrid combinations. However, further functional characterization is necessary to confirm their biological roles and relevance to lodging resistance.
The presence of multiple lodging resistance genes in 1892S genome might play a significant role in the development of over 100 hybrid rice varieties, manyof which exhibit superior lodging resistance agronomic traits. Field evaluations of hybrid rice varieties derived from 1892S demonstrated outstanding lodging resistance and adaptability to diverse environmental conditions. These results underscore the practical value of 1892S as a maternal line in hybrid rice breeding.
This study provides a comprehensive genomic analysis of 1892S, uncovering the genetic basis of lodging resistance, and offers a model for analyzing other traits, such as nitrogen efficiency and high yield. However, some limitations must be acknowledged. For instance, trait analysis was based on previously reported genes studied in other experimental materials. Genetic differences between materials may exist, implying that 1892S might harbor additional beneficial genes not yet identified. As such, this method may not fully capture all potential advantageous genes in 1892S.
This study not only examines lodging resistance-related genes in 1892S using the T2T genome as a reference but also provides a model for the analysis of other two-line sterile lines and rice varieties. By utilizing chromosome-level parental genomes as references and integrating previously reported functional genes, this approach enables the identification of both advantageous and disadvantageous alleles in parental lines.
Two-line sterile lines play an essential role in hybrid rice breeding, with their unique traits and superior alleles significantly contributing to the improvement of hybrid rice [29]. By analyzing the characteristics of two-line sterile lines, valuable genetic foundations can be established to achieve high yield, superior quality, and enhanced stress resistance in hybrid rice [30]. The discovery and utilization of superior alleles are critical for improving rice yield, disease resistance, and adaptability. Through the genomic analysis of two-line sterile lines, key alleles associated with important agronomic traits can be identified. These alleles can be incorporated into hybrid rice varieties using molecular breeding techniques, thereby improving overall performance. This integrative genomic approach lays the groundwork for molecular breeding strategies targeting lodging resistance and other agronomic traits in hybrid rice.

4. Materials and Methods

4.1. Genomic DNA and RNA Extraction Sequencing

Harvested with precision, the vibrant fresh leaves of the 1892S cultivar were swiftly immersed in liquid nitrogen to ensure their preservation. Total RNA was extracted from three biological replicates, each consisting of leaves collected from independent three-week-old 1892S seedlings, using the TRIzol™ Reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol, with quality and concentration assessed by spectrophotometry and gel electrophoresis. Utilizing the CTAB extraction protocol, we successfully isolated high-quality genomic DNA from these leaf samples. In pursuit of comprehensive whole genome sequencing (WGS), we crafted a paired-end (300 bp) library using the Nextera DNA Flex Library Prep Kit (Illumina, San Diego, CA, USA) and deployed it for sequencing on the advanced Illumina NovaSeq platform (Illumina, San Diego, CA, USA), thereby securing a dataset of exceptional clarity. For the acquisition of Oxford Nanopore Technologies (ONT) ultra-long reads, we assembled a DNA library with the SQK-LSK110 ligation kit (Nanopore, Oxford, UK). Following meticulous purification, this library was sequenced on the PromethION instrument (Nanopore, Oxford, UK), promising in-depth genomic insights. In our quest for PacBio HiFi sequencing, we constructed a long-read library (15 kb) from the genomic DNA using the SMRTbell® Express Template Prep Kit 2.0 (PacBio, Menlo Park, CA, USA). This library was then processed on a Sequel II instrument (PacBio, USA), delivering high-fidelity sequencing results. For Hi-C sequencing, we prepared a library with the VAHTSTM Fg DNA Library Prep Kit (Novozymes, Nanjing, China) and conducted sequencing on the Illumina NovaSeq X Plus instrument (San Diego, CA, USA), generating 82,983,684 paired-end reads (150 bp). To ensure data integrity, we meticulously filtered out low-quality sequences from the raw data, thereby refining our dataset to its cleanest form.

4.2. T2T and Chloroplast Genome Assembly

The contigs were initially assembled using Hifiasm [31] from PacBio HiFi, ONT ultra-long, and Hi-C data, followed by purity identification and de-hybridization with Purge Haplotigswas used to identify and de-hybridy by purge_dups [32]. The Hi-C data were then used for chromosome anchoring and assembly into chromosomes using 3D-DNA and Juicer. The result was manually adjusted using Juicebox. TGS-GapCloser [33] was used to fill the gap between contigs and extend contigs by using the covering relationship between the ONT ultra-long data and the already assembled contigs, followed by comparison with the NIP-T2T genome via Mummer [34] to fix the chromosome number. One polishing was performed based on WGS data by Pilon [35]. Finally, a high-quality and gapless genome was assembled. For chloroplast genome (cpDNA) was assembled by GetOrganelle [36] based on clean WGS data and visualized using Bandage [37]. Annotation was conducted on CPGAVAS2 using the FASTA-formatted circular sequence based on rice (NCBI accession: NC_001320) as the reference genome [38].

4.3. Telomere and Centromere Identification

Ultra-long ONT reads larger than 100 kb were compared to chromosomes using Minimap2 [39], and all reads that were compared once within 100 bp of the end of chromosomes were collected. The read containing the artifact sequence is filtered out, and then the comparison of the read is counted, and the read of the extendible median length is defined as a reference and the other is query; medaka_consensu reassembled reference telomere read and query telomere read to obtain a consistent sequence. Blast was used to compare the above consistency sequences to both ends of each chromosome, and the alignment sequences with coverage >= 90 were replaced with the upper-end grain sequences according to the alignment position. Telomere sequences were identified based on their characteristic repeat motifs (CCCTAAA/TAG) using a quartet [40], which efficiently detects these base repeat patterns in genomic regions. To analyze the centromeric regions of the 1892S genome, the HiCAT tool was employed [41], enabling precise centromere identification and characterization.

4.4. Genome Assembly Quality Assessment

To validate that the assembly results corresponded to the target species, the genome sequence was fragmented into 10 kb segments and compared against the NCBI nucleotide database (NT library) using BLAST. For alignment, short-read and long-read data were mapped to the genome using BWA and Minimap2, respectively. The comparison rate and coverage rate were calculated, where higher values indicated greater consistency between the assembly and the reads, reflecting better assembly quality. Genome integrity was assessed using BUSCO (Benchmarking Universal Single-Copy Orthologs), which evaluates assembly completeness based on orthologous genes in the OrthoDB database [42]. After aligning short and long reads to the genome, mutations were identified using Samtools [43], Picard [44], and GATK, enabling the calculation of homozygous and heterozygous SNP and InDel rates. Lower homozygous rates indicated higher genome accuracy, while higher heterozygous rates reflected greater genome heterozygosity. Additionally, the assembly accuracy was validated using the QV (quality value) metric provided by Merqury, offering a quantitative assessment of the genome assembly’s precision [45].

4.5. Genome Annotation

The genome was soft-masked using RepeatMasker, guided by repetitive elements identified through RepeatModeler [46]. Trinity was employed for the de novo assembly of all RNA-seq reads [47]. These genome-guided assemblies, de novo transcript assemblies, and RNA-seq alignment data were integrated into a comprehensive transcriptome, which served as the foundation for constructing high-confidence gene models using PASA v2.4.1 [48]. Initial gene predictions were generated with BRAKER [49], incorporating multiple layers of evidence: (1) high-confidence gene models from PASA, (2) Intron hints derived from Illumina short-read alignments; (3) Protein hints based on mappings of proteins from NIP-T2T, R498, and UniProt to the genome using Exonerate; (4) The soft-masked genome of 1892S.
EVidenceModeler (EVM) was then utilized to combine gene models from various sources [50], including BRAKER predictions, genome-guided transcripts from Illumina RNA-seq, de novo transcripts, and protein alignments from NIP-T2T, R498, and UniProt. PASA further refined these gene models by annotating alternative splicing events, defining untranslated regions (UTRs), and refining gene structures to produce the final high-quality annotation. Functional annotation of the annotated genes was conducted utilizing the EggNOG-mapper [51].

4.6. Gene Retrieval for Lodging Resistance in Rice

Through an extensive search of academic literature on rice lodging resistance, the genes associated with this trait were systematically analyzed. The relevant gene ID and their corresponding sequences were then extracted for further analysis. Translate: Extract the retrieved gene sequences, and map them to the 1892S genome to find the corresponding gene numbers by Minimap2 and BEDTools [52]. Next, further, search the relevant literature, analyze the consistency of the lodging resistance gene sequence alignment and the corresponding genotypes, and then determine whether 1892S has the corresponding lodging resistance gene.

4.7. Genome Comparison Analysis

The pan-genome sequences of 111 rice accessions were retrieved as a reference dataset [53]. To classify genes in the 1892S genome, Minimap2 (v2.28, -cx asm20) was employed to align 1892S gene sequences against the core genes of the pan-genome. Genes failing to align with the core gene set were classified as 1892S-specific non-core genes. Protein domains across the 1892S genome were annotated using the Pfam_scan (default parameter) tool based on the Pfam database [54]. Genes harboring structural domains associated with lodging resistance were prioritized as candidate genes. Non-core gene sets from the 1892S pan-genome were integrated with the domain-annotated lodging resistance candidates.

4.8. Method for Identifying Rice Lodging Resistance

The lodging resistance of rice varieties was evaluated following the Anhui Provincial Standard DB34/T 3924-2021 [55]. Test and control varieties (selected for strong lodging resistance) were cultivated uniformly under field conditions adhering to NY/T 1300 guidelines. During the dough stage, representative tillers were selected, and border rows were excluded. Custom weight bottles (3.5 cm diameter × 6.5 cm height) filled with iron sand (10–250 g, 10 g intervals) were clamped onto the penultimate node of stems. Lodging incidence was recorded for ≥30 stems per weight level. The Lodging Weight 50 (LW50) was calculated using Bliss probit analysis, relating lodging rate to log10(weight). The Lodging Index (LI) was derived as the ratio of test-to-control LW50, with resistance graded into five levels (LI ≥ 1.20: strong; LI < 0.50: very weak). Statistical validation employed probit regression software, ensuring precision (results to two decimal places).

5. Conclusions

This study achieved a high-quality telomere-to-telomere (T2T) genome assembly of the two-line sterile line 1892S, with a total size of 394.82 Mb across 12 gap-free chromosomes. The assembly exhibited exceptional accuracy, supported by a BUSCO completeness score of 99.19% and a quality value (QV) of 46.492. Genome annotation identified 40,560 protein-coding genes, including 2347 strain-specific genes through pan-genome analysis, highlighting 1892S’s unique genetic architecture.
Critical lodging resistance genes, including SD1, Sdt97, SB1, OsFBA2, APO1, and OsTB1, were identified with functional allelic variations. These findings provide a genomic resource for leveraging 1892S in hybrid rice breeding, enabling targeted improvement of lodging resistance. The integration of T2T assembly, functional gene annotation, and pan-genomic insights establishes a model for analyzing other sterile lines, advancing precision breeding strategies to contribute to food security.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants14121873/s1, Supplemental File S1: Sequence alignment of sd1 between 1892S and 93-11; Supplemental File S2: mutation of Sdt97 in 1892S; Supplemental File S3: Sequence alignment of SBI between 1892S and R498; Supplemental File S4: Sequence characteristics of the OsFBA2 gene in 1892S; Supplemental File S5: Sequence characteristics with 9 bp deletion of the scm2 gene in 1892S; Supplemental File S6: Sequence characteristics with 4 bp insertion of the OsTB1 gene in 1892S; Supplemental File S7: Dispensable genes’ ID in the 1892S genome; Supplemental File S8: 35 specific genes’ ID within lodging resistance-related gene families.

Author Contributions

Research conception and design, J.N., D.Z. and D.N.; data analysis, W.Z.; lodging resistance evaluation, F.S.; materials preparation, L.Y. and L.Z.; writing—original draft preparation, W.Z.; writing—review and editing, W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Grants from the National Natural Science Foundation of China (U21A20211).

Data Availability Statement

All datasets have been deposited in the National Genomics Data Center (NGDC) with the following accession codes: the raw data, genome assembly, and gene annotation of ‘1892S’, PRJCA037566.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Mohidem, N.A.; Hashim, N.; Shamsudin, R.; Che Man, H. Rice for Food Security: Revisiting Its Production, Diversity, Rice Milling Process and Nutrient Content. Agriculture 2022, 12, 741. [Google Scholar] [CrossRef]
  2. Alam, M.; Lou, G.; Abbas, W.; Osti, R.; Ahmad, A.; Bista, S.; Ahiakpa, J.K.; He, Y. Improving Rice Grain Quality Through Ecotype Breeding for Enhancing Food and Nutritional Security in Asia-Pacific Region. Rice 2024, 17, 47. [Google Scholar] [CrossRef]
  3. Liao, C.; Yan, W.; Chen, Z.; Xie, G.; Deng, X.W.; Tang, X. Innovation and development of the third-generation hybrid rice technology. Crop J. 2021, 9, 693–701. [Google Scholar] [CrossRef]
  4. Chen, Z.; Wu, J.; Deng, X.W.; Tang, X. Establishment and Advances of Third-Generation Hybrid Rice Technology: A Review. Rice 2023, 16, 56. [Google Scholar] [CrossRef]
  5. Xu, Y.; Yu, D.; Chen, J.; Duan, M. A review of rice male sterility types and their sterility mechanisms. Heliyon 2023, 9, e18204. [Google Scholar] [CrossRef]
  6. Zhen, G.; Qin, P.; Liu, K.Y.; Nie, D.Y.; Yang, Y.Z.; Deng, X.W.; He, H. Genome-wide dissection of heterosis for yield traits in two-line hybrid rice populations. Sci. Rep. 2017, 7, 7635. [Google Scholar] [CrossRef]
  7. Zhang, X.; He, Q.; Zhang, W.; Shu, F.; Wang, W.; He, Z.; Xiong, H.; Peng, J.; Deng, H. Genetic relationships and identification of core germplasm among rice photoperiod- and thermo-sensitive genic male sterile lines. BMC Plant Biol. 2021, 21, 313. [Google Scholar] [CrossRef]
  8. Li, C.-q.; Pu, Y.-n.; Gao, X.; Cao, Y.; Bao, Y.-y.; Xu, Q.-l.; Du, L.; Tan, J.-r.; Zhu, Y.-h.; Zhang, H.-y.; et al. Detection of quantitative trait nucleotides (QTNs) and QTN-by-environment and QTN-by-QTN interactions for cotton early-maturity traits using the 3VmrMLM method. Ind. Crops Prod. 2024, 216, 118706. [Google Scholar] [CrossRef]
  9. Wei, X.; Qiu, J.; Yong, K.; Fan, J.; Zhang, Q.; Hua, H.; Liu, J.; Wang, Q.; Olsen, K.M.; Han, B.; et al. A quantitative genomics map of rice provides genetic insights and guides breeding. Nat. Genet. 2021, 53, 243–253. [Google Scholar] [CrossRef]
  10. Gu, H.; Liang, S.; Zhao, J. Novel Sequencing and Genomic Technologies Revolutionized Rice Genomic Study and Breeding. Agronomy 2022, 12, 218. [Google Scholar] [CrossRef]
  11. Zhang, Y.; Fu, J.; Wang, K.; Han, X.; Yan, T.; Su, Y.; Li, Y.; Lin, Z.; Qin, P.; Fu, C.; et al. The telomere-to-telomere gap-free genome of four rice parents reveals SV and PAV patterns in hybrid rice breeding. Plant Biotechnol. J. 2022, 20, 1642–1644. [Google Scholar] [CrossRef] [PubMed]
  12. Zhang, W.; Zhou, L.; Zhang, D. Dissecting the Genetic Basis of Superior Traits in Thermosensitive Genic Male Sterile Line 1892S Through Genome-Wide Analysis. Genet. Mol. Res. 2024, 23, 2024-07. [Google Scholar] [CrossRef]
  13. Shang, L.; He, W.; Wang, T.; Yang, Y.; Xu, Q.; Zhao, X.; Yang, L.; Zhang, H.; Li, X.; Lv, Y.; et al. A complete assembly of the rice Nipponbare reference genome. Mol. Plant 2023, 16, 1232–1236. [Google Scholar] [CrossRef] [PubMed]
  14. Sasaki, A.; Ashikari, M.; Ueguchi-Tanaka, M.; Itoh, H.; Nishimura, A.; Swapan, D.; Ishiyama, K.; Saito, T.; Kobayashi, M.; Khush, G.S. A mutant gibberellin-synthesis gene in rice. Nature 2002, 416, 701–702. [Google Scholar] [CrossRef]
  15. Tong, J.; Han, Z.; Han, A.; Liu, X.; Zhang, S.; Fu, B.; Hu, J.; Su, J.; Li, S.; Wang, S.; et al. Sdt97: A Point Mutation in the 5′ Untranslated Region Confers Semidwarfism in Rice. G3 2016, 6, 1491–1502. [Google Scholar] [CrossRef]
  16. Liu, C.; Zheng, S.; Gui, J.; Fu, C.; Yu, H.; Song, D.; Shen, J.; Qin, P.; Liu, X.; Han, B.; et al. Shortened Basal Internodes Encodes a Gibberellin 2-Oxidase and Contributes to Lodging Resistance in Rice. Mol. Plant 2018, 11, 288–299. [Google Scholar] [CrossRef]
  17. Rashid, M.A.R.; Zhao, Y.; Azeem, F.; Zhao, Y.; Ahmed, H.G.M.; Atif, R.M.; Pan, Y.; Zhu, X.; Liang, Y.; Zhang, H.; et al. Unveiling the genetic architecture for lodging resistance in rice (Oryza sativa. L) by genome-wide association analyses. Front. Genet. 2022, 13, 960007. [Google Scholar] [CrossRef]
  18. Ookawa, T.; Hobo, T.; Yano, M.; Murata, K.; Ando, T.; Miura, H.; Asano, K.; Ochiai, Y.; Ikeda, M.; Nishitani, R.; et al. New approach for rice improvement using a pleiotropic QTL gene for lodging resistance and yield. Nat. Commun. 2010, 1, 132. [Google Scholar] [CrossRef]
  19. Yano, K.; Ookawa, T.; Aya, K.; Ochiai, Y.; Hirasawa, T.; Ebitani, T.; Takarada, T.; Yano, M.; Yamamoto, T.; Fukuoka, S.; et al. Isolation of a novel lodging resistance QTL gene involved in strigolactone signaling and its pyramiding with a QTL gene involved in another mechanism. Mol. Plant 2015, 8, 303–314. [Google Scholar] [CrossRef]
  20. Yang, X.; Yu, S.; Yan, S.; Wang, H.; Fang, W.; Chen, Y.; Ma, X.; Han, L. Progress in Rice Breeding Based on Genomic Research. Genes 2024, 15, 564. [Google Scholar] [CrossRef]
  21. Wang, C.; Han, B. Twenty years of rice genomics research: From sequencing and functional genomics to quantitative genomics. Mol. Plant 2022, 15, 593–619. [Google Scholar] [CrossRef] [PubMed]
  22. Wu, D.H.; Chen, C.T.; Yang, M.D.; Wu, Y.C.; Lin, C.Y.; Lai, M.H.; Yang, C.Y. Controlling the lodging risk of rice based on a plant height dynamic model. Bot. Stud. 2022, 63, 25. [Google Scholar] [CrossRef] [PubMed]
  23. Oikawa, T.; Koshioka, M.; Kojima, K.; Yoshida, H.; Kawata, M. A role of OsGA20ox1, encoding GA 20-oxidase, in internode elongation of deepwater rice. Plant Physiol. 2004, 135, 1312–1319. [Google Scholar]
  24. Tong, H.; Jin, Y.; Liu, D.; Liu, L.; Yin, Y.; Qian, Q.; Zhu, L.; Chu, C. DWARF AND LOW-TILLERING, a new member of the GRAS family, plays positive roles in brassinosteroid signaling in rice. G3 2016, 6, 405–413. [Google Scholar] [CrossRef]
  25. Liu, L.; Tong, H.; Xiao, Y.; Che, R.; Xu, F.; Hu, B.; Liang, C.; Chu, J.; Li, J.; Chu, C. Activation of Big Grain1 significantly improves grain size by regulating auxin transport in rice. PNAS 2018, 112, 11102–11107. [Google Scholar] [CrossRef]
  26. Zhang, C.; Xu, Y.; Lu, Y.; Yu, H.; Gu, M.; Liu, Q. The F-box protein APO1 regulates panicle architecture and stem strength in rice. Plant Biotechnol. J. 2021, 19, 620–632. [Google Scholar]
  27. Jia, L.Q.; Dai, Y.D.; Peng, Z.W.; Cui, Z.B.; Zhang, X.F.; Li, Y.Y.; Tian, W.J.; He, G.H.; Li, Y.; Sang, X.C. The auxin transporter OsAUX1 regulates tillering in rice (Oryza sativa). J. Integr. Agric. 2024, 5, 1454–1467. [Google Scholar] [CrossRef]
  28. Rashid, M.H.A.; Zhang, X.; Wang, H.; Tang, M.; Liu, X.; Ding, L.; Qian, Q.; Gao, Z. Natural variation in OsFBA2 promotes rice tillering by modulating florigen signaling pathways. Plant Physiol. 2022, 190, 2151–2166. [Google Scholar]
  29. Ashraf, H.; Ghouri, F.; Baloch, F.S.; Nadeem, M.A.; Fu, X.; Shahid, M.Q. Hybrid Rice Production: A Worldwide Review of Floral Traits and Breeding Technology, with Special Emphasis on China. Plants 2024, 13, 578. [Google Scholar] [CrossRef]
  30. Cai, Z.; Xu, C.; Liu, X.; Lv, Y.; Ouyang, Y.; Jiang, H. Exploiting male sterility toward the development of hybrid rice. Seed Biol. 2024, 3, e019. [Google Scholar] [CrossRef]
  31. Cheng, H.; Jarvis, E.D.; Fedrigo, O.; Koepfli, K.P.; Urban, L.; Gemmell, N.J.; Li, H. Haplotype-resolved assembly of diploid genomes without parental data. Nat. Biotechnol. 2022, 40, 1332–1335. [Google Scholar] [CrossRef] [PubMed]
  32. Guan, D.; McCarthy, S.A.; Wood, J.; Howe, K.; Wang, Y.; Durbin, R. Identifying and removing haplotypic duplication in primary genome assemblies. Bioinformatics 2020, 36, 2896–2898. [Google Scholar] [CrossRef] [PubMed]
  33. Xu, M.; Guo, L.; Gu, S.; Wang, O.; Zhang, R.; Peters, B.A.; Fan, G.; Liu, X.; Xu, X.; Deng, L.; et al. TGS-GapCloser: A fast and accurate gap closer for large genomes with low coverage of error-prone long reads. Gigascience 2020, 9, giaa094. [Google Scholar] [CrossRef] [PubMed]
  34. Marcais, G.; Delcher, A.L.; Phillippy, A.M.; Coston, R.; Salzberg, S.L.; Zimin, A. MUMmer4: A fast and versatile genome alignment system. PLoS Comput. Biol. 2018, 14, e1005944. [Google Scholar] [CrossRef]
  35. Walker, B.J.; Abeel, T.; Shea, T.; Priest, M.; Abouelliel, A.; Sakthikumar, S.; Cuomo, C.A.; Zeng, Q.; Wortman, J.; Young, S.K.; et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 2014, 9, e112963. [Google Scholar] [CrossRef]
  36. Jin, J.J.; Yu, W.B.; Yang, J.B.; Song, Y.; dePamphilis, C.W.; Yi, T.S.; Li, D.Z. GetOrganelle: A fast and versatile toolkit for accurate de novo assembly of organelle genomes. Genome Biol. 2020, 21, 241. [Google Scholar] [CrossRef]
  37. Wick, R.R.; Schultz, M.B.; Zobel, J.; Holt, K.E. Bandage: Interactive visualization of de novo genome assemblies. Bioinformatics 2015, 31, 3350–3352. [Google Scholar] [CrossRef]
  38. Shi, L.; Chen, H.; Jiang, M.; Wang, L.; Wu, X.; Huang, L.; Liu, C. CPGAVAS2, an integrated plastome sequence annotator and analyzer. Nucleic Acids Res. 2019, 47, W65–W73. [Google Scholar] [CrossRef]
  39. Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 2018, 34, 3094–3100. [Google Scholar] [CrossRef]
  40. Lin, Y.; Ye, C.; Li, X.; Chen, Q.; Wu, Y.; Zhang, F.; Pan, R.; Zhang, S.; Chen, S.; Wang, X.; et al. quarTeT: A telomere-to-telomere toolkit for gap-free genome assembly and centromeric repeat identification. Hortic. Res. 2023, 10, uhad127. [Google Scholar] [CrossRef]
  41. Gao, S.; Yang, X.; Guo, H.; Zhao, X.; Wang, B.; Ye, K. HiCAT: A tool for automatic annotation of centromere structure. Genome Biol. 2023, 24, 58. [Google Scholar] [CrossRef] [PubMed]
  42. Simao, F.A.; Waterhouse, R.M.; Ioannidis, P.; Kriventseva, E.V.; Zdobnov, E.M. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 2015, 31, 3210–3212. [Google Scholar] [CrossRef] [PubMed]
  43. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R.; Genome Project Data Processing, S. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25, 2078–2079. [Google Scholar] [CrossRef] [PubMed]
  44. “Picard Toolkit.” Broad Institute, GitHub Repository. 2019. Available online: https://broadinstitute.github.io/picard/ (accessed on 1 May 2025).
  45. Rhie, A.; Walenz, B.P.; Koren, S.; Phillippy, A.M. Merqury: Reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol. 2020, 21, 245. [Google Scholar] [CrossRef]
  46. Flynn, J.M.; Hubley, R.; Goubert, C.; Rosen, J.; Clark, A.G.; Feschotte, C.; Smit, A.F. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl. Acad. Sci. USA 2020, 117, 9451–9457. [Google Scholar] [CrossRef]
  47. Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Thompson, D.A.; Amit, I.; Adiconis, X.; Fan, L.; Raychowdhury, R.; Zeng, Q.; et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011, 29, 644–652. [Google Scholar] [CrossRef]
  48. Haas, B.J.; Delcher, A.L.; Mount, S.M.; Wortman, J.R.; Smith, R.K., Jr.; Hannick, L.I.; Maiti, R.; Ronning, C.M.; Rusch, D.B.; Town, C.D.; et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 2003, 31, 5654–5666. [Google Scholar] [CrossRef]
  49. Gabriel, L.; Bruna, T.; Hoff, K.J.; Ebel, M.; Lomsadze, A.; Borodovsky, M.; Stanke, M. BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA. Genome Res. 2024, 34, 769–777. [Google Scholar] [CrossRef]
  50. Haas, B.J.; Salzberg, S.L.; Zhu, W.; Pertea, M.; Allen, J.E.; Orvis, J.; White, O.; Buell, C.R.; Wortman, J.R. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 2008, 9, R7. [Google Scholar] [CrossRef]
  51. Cantalapiedra, C.P.; Hernandez-Plaza, A.; Letunic, I.; Bork, P.; Huerta-Cepas, J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol. Biol. Evol. 2021, 38, 5825–5829. [Google Scholar] [CrossRef]
  52. Quinlan, A.R.; Hall, I.M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26, 841–842. [Google Scholar] [CrossRef] [PubMed]
  53. Zhang, F.; Xue, H.; Dong, X.; Li, M.; Zheng, X.; Li, Z.; Xu, J.; Wang, W.; Wei, C. Long-read sequencing of 111 rice genomes reveals significantly larger pan-genomes. Genome Res. 2022, 32, 853–863. [Google Scholar] [CrossRef] [PubMed]
  54. El-Gebali, S.; Mistry, J.; Bateman, A.; Eddy, S.R.; Luciani, A.; Potter, S.C.; Qureshi, M.; Richardson, L.J.; Salazar, G.A.; Smart, A.; et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019, 47, D427–D432. [Google Scholar] [CrossRef] [PubMed]
  55. DB34/T 3924-2021; A Method for Identifying Rice Lodging Resistance. Anhui Provincial Market Supervision and Administration Bureau: Hefei, China, 2021.
Plants 14 01873 g001
Figure 1. Hi-C interaction heatmap of the 1892S genome. Both the X and Y axes represent genomic positions divided into 100 kb bins (N × 100 kb), and each axis follows chromosome order from Chr01 to Chr12. Color gradients indicate the frequency of interaction between genomic regions, with darker colors indicating stronger chromatin contacts. Note: Although chromatin interaction frequencies in this map can reach values as high as 8, most visible intensities fall within the 2–3 range due to figure rendering limits. Strongest interactions are observed along the diagonal (i.e., between adjacent regions on the same chromosome), as expected. The lower interaction intensities in off-diagonal regions indicate minimal inter-chromosomal noise, which in turn reflects the high accuracy and contiguity of the T2T genome assembly.
Figure 1. Hi-C interaction heatmap of the 1892S genome. Both the X and Y axes represent genomic positions divided into 100 kb bins (N × 100 kb), and each axis follows chromosome order from Chr01 to Chr12. Color gradients indicate the frequency of interaction between genomic regions, with darker colors indicating stronger chromatin contacts. Note: Although chromatin interaction frequencies in this map can reach values as high as 8, most visible intensities fall within the 2–3 range due to figure rendering limits. Strongest interactions are observed along the diagonal (i.e., between adjacent regions on the same chromosome), as expected. The lower interaction intensities in off-diagonal regions indicate minimal inter-chromosomal noise, which in turn reflects the high accuracy and contiguity of the T2T genome assembly.
Plants 14 01873 g001
Plants 14 01873 g002
Figure 2. This figure illustrates an overview of the high-contiguity the genome assembly of the 1892S, in which each of the 12 chromosomes is assembled as a single contig without gaps. Telomeric repeat sequences have been identified at both ends of all chromosomes, and centromeric regions have been predicted based on repeat density and sequence characteristics.
Figure 2. This figure illustrates an overview of the high-contiguity the genome assembly of the 1892S, in which each of the 12 chromosomes is assembled as a single contig without gaps. Telomeric repeat sequences have been identified at both ends of all chromosomes, and centromeric regions have been predicted based on repeat density and sequence characteristics.
Plants 14 01873 g002
Plants 14 01873 g003
Figure 3. Comprehensive genomic quality assessment of the 1892S genome assembly, including assembly metrics, sequencing depth, and variant distribution profiles. A. Summary of genome assembly statistics, including genome size. B. Distribution of GC content across the genome, indicating regional variation in nucleotide composition. C. Sequencing depth distribution of Illumina short reads mapped to the 1892S genome. D. Sequencing depth distribution of long reads (PacBio HiFi and ultra-ONT) across the genome. E. BUSCO gene completeness assessment: the outer circle shows the distribution of single-copy BUSCOs, while the inner circle represents duplicated BUSCOs, demonstrating gene completeness and copy number consistency. A complete F. SNP density distribution: the outer ring displays homozygous SNP density, and the inner ring shows heterozygous SNP density across the genome. G. InDel density distribution: the outer ring displays homozygous InDel density, and the inner ring shows heterozygous InDel density. These multi-layered metrics collectively support the high quality and accuracy of the 1892S genome assembly.
Figure 3. Comprehensive genomic quality assessment of the 1892S genome assembly, including assembly metrics, sequencing depth, and variant distribution profiles. A. Summary of genome assembly statistics, including genome size. B. Distribution of GC content across the genome, indicating regional variation in nucleotide composition. C. Sequencing depth distribution of Illumina short reads mapped to the 1892S genome. D. Sequencing depth distribution of long reads (PacBio HiFi and ultra-ONT) across the genome. E. BUSCO gene completeness assessment: the outer circle shows the distribution of single-copy BUSCOs, while the inner circle represents duplicated BUSCOs, demonstrating gene completeness and copy number consistency. A complete F. SNP density distribution: the outer ring displays homozygous SNP density, and the inner ring shows heterozygous SNP density across the genome. G. InDel density distribution: the outer ring displays homozygous InDel density, and the inner ring shows heterozygous InDel density. These multi-layered metrics collectively support the high quality and accuracy of the 1892S genome assembly.
Plants 14 01873 g003
Plants 14 01873 g004
Figure 4. Functional Annotation of the 1892S Genome Based on Clusters of Orthologous Groups (COG) Classification. The bar chart summarizes the distribution of predicted protein-coding genes in 1892S across standardized COG categories. Each bar represents a functional class, annotated using single-letter COG codes. The COG classification provides insight into the functional composition of the genome and the major biological processes represented.
Figure 4. Functional Annotation of the 1892S Genome Based on Clusters of Orthologous Groups (COG) Classification. The bar chart summarizes the distribution of predicted protein-coding genes in 1892S across standardized COG categories. Each bar represents a functional class, annotated using single-letter COG codes. The COG classification provides insight into the functional composition of the genome and the major biological processes represented.
Plants 14 01873 g004
Table 1. Chromosome Assembly Statistics and Structural Features of the 1892S Genome.
Table 1. Chromosome Assembly Statistics and Structural Features of the 1892S Genome.
ChromosomesLength (bp)Number of ContigsNumber of GapsCentromere LocationTelomere Start Repeat Unit NumberTelomere End Repeat Unit Number
Chr0145,262,8971016,361,678–19,363,16914041947
Chr0237,503,1401013,486,004–15,088,33617862031
Chr0339,610,0541020,608,780–21,870,4851607195
Chr0436,637,658109,025,928–10,575,55919951121
Chr0531,267,2561011,918,420–12,941,9101808804
Chr0631,773,1071014,604,503–16,500,1819041045
Chr0730,986,1961011,585,584–14,893,1681042556
Chr0831,327,5121012,278,862–14,055,59813921655
Chr0924,231,554102,235,737–3,897,2251367877
Chr1025,695,394108,608,241–9,522,47711501550
Chr1132,477,4721012,858,031–14,900,6571270525
Chr1228,046,869109,886,763–11,786,3388151302
Pt134,48810---
Table 2. Lodging Resistance Gene Identification and Protein Family Analysis in Nipponbare and 1892S.
Table 2. Lodging Resistance Gene Identification and Protein Family Analysis in Nipponbare and 1892S.
Gene NameGene ID of NipponbareGene ID of 1892SReferencePfams
sd1Os01g0883800Os1892S01G025560[14]DIOX_N, 2OG-FeII_Oxy
Sdt97Os06g0649800Os1892S06G004570[15]Adenine_glyco
SBILOC_Os05g43880Os1892S05G000700[16]DIOX_N, 2OG-FeII_Oxy
OsFBA2LOC_Os07g09870Os1892S07G004930[17]F-box-like, FBA_1
APO1Os06g0665400Os1892S06G022880[18]F-box
OsTB1Os03g0706500Os1892S03G036300[19]TCP
Table 3. Quantitative and Phenotypic Assessment of Lodging Resistance in Crop Varieties: Multi-Replicate Trials and Classification.
Table 3. Quantitative and Phenotypic Assessment of Lodging Resistance in Crop Varieties: Multi-Replicate Trials and Classification.
Variety NameIIIIIIAverage ValueLI (Lodging Index)LevelPhenotype
Huiliangyou 27 Zhan44.843.351.546.50.92Moderately strong
21SBC325.639.338.434.40.74Very weak
21SBC459.455.351.255.31.12Moderately strong
21SBC555.852.658.455.61.12Moderately strong
21SBC63941.432.337.60.73Normal
21SBC72728.734.430.00.64Very weak
21SBC825.530.122.125.90.54Very weak
21SBC924.423.621.223.10.55Weak
21SBC1038.828.835.934.50.74Very weak
21SBC1137.231.333.534.00.74Very weak
21SBC1226.136.43532.50.64Very weak
Huiliangyou 98543.857.648.449.90.992Moderately strong
Wandao 153 (CK)49.749.351.650.21.02Moderately strong
Huiliangyou Yuehesimiao51.258.454.254.61.72Moderately strong
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, W.; Zhou, L.; Ni, D.; Ni, J.; Song, F.; Yang, L.; Zhang, D. Genetic Analysis of Lodging Resistance in 1892S Based on the T2T Genome: Providing a Genetic Approach for the Improvement of Two-Line Hybrid Rice Varieties. Plants 2025, 14, 1873. https://doi.org/10.3390/plants14121873

AMA Style

Zhang W, Zhou L, Ni D, Ni J, Song F, Yang L, Zhang D. Genetic Analysis of Lodging Resistance in 1892S Based on the T2T Genome: Providing a Genetic Approach for the Improvement of Two-Line Hybrid Rice Varieties. Plants. 2025; 14(12):1873. https://doi.org/10.3390/plants14121873

Chicago/Turabian Style

Zhang, Wei, Liang Zhou, Dahu Ni, Jinlong Ni, Fengshun Song, Liansong Yang, and Dewen Zhang. 2025. "Genetic Analysis of Lodging Resistance in 1892S Based on the T2T Genome: Providing a Genetic Approach for the Improvement of Two-Line Hybrid Rice Varieties" Plants 14, no. 12: 1873. https://doi.org/10.3390/plants14121873

APA Style

Zhang, W., Zhou, L., Ni, D., Ni, J., Song, F., Yang, L., & Zhang, D. (2025). Genetic Analysis of Lodging Resistance in 1892S Based on the T2T Genome: Providing a Genetic Approach for the Improvement of Two-Line Hybrid Rice Varieties. Plants, 14(12), 1873. https://doi.org/10.3390/plants14121873

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop