Rice (Oryza sativa) is one of the most important crops grown worldwide, and saline–alkali stress seriously affects the yield and quality of rice. It is imperative to elucidate the molecular mechanisms underlying rice response to saline–alkali stress. In this study, we conducted an integrated analysis of the transcriptome and metabolome to elucidate the effects of long-term saline–alkali stress on rice. High saline–alkali stress (pH > 9.5) induced significant changes in gene expression and metabolites, including 9347 differentially expressed genes (DEGs) and 693 differentially accumulated metabolites (DAMs). Among the DAMs, lipids and amino acids accumulation were greatly enhanced. The pathways of the ABC transporter, amino acid biosynthesis and metabolism, glyoxylate and dicarboxylate metabolism, glutathione metabolism, TCA cycle, and linoleic acid metabolism, etc., were significantly enriched with DEGs and DAMs. These results suggest that the metabolites and pathways play important roles in rice’s response to high saline–alkali stress. Our study deepens the understanding of mechanisms response to saline–alkali stress and provides references for molecular design breeding of saline–alkali resistant rice.

The δ¹³C value is regarded as an important indicator for tolerance to drought stress (DS), which is a severe abiotic stress that influences rice productivity. However, exploration of drought-responsive genes (DRGs) related to δ¹³C regulation is less reported. In this study, we investigated the natural variation in δ¹³C values in 102 japonica rice accessions. Among them, two rice accessions with contrasting δ¹³C values, Longdao 10 (LD10, DS-tolerant) and Binxu (BX, DS-sensitive), were used for further analysis. LD10 possesses better drought resistance with 2% lower δ¹³C values, 35% lower stomatal length and density, 33% lower water loss, and 11% lower stomatal conductance in comparison to BX. Transcriptome analysis shows that there are 2325 and 1378 differentially expressed genes (DEGs) induced by DS in LD10 and BX at the tillering stage, respectively, while there are 1076 and 492 DEGs in LD10 and BX at the graining stage, respectively. In total, 21 overlapped DEGs (defined as DRGs) were identified due to DS effects across two rice accessions over two stages. Among them, the expression levels of six genes, including chloride transporter (CLT1) and photosystem II polypeptide (PSBP), were further tested using qRT-PCR. Furthermore, we found that four methyltransferase genes were upregulated in BX compared to LD10 under DS. Consistently, the methylation levels of CLT1 and PSBP were higher along both promoter and CDS regions for CG, CHG, and CHH types. This study highlights the importance of the expression of these DRGs in response to DS and provides deep insights into DNA methylation-driven gene expression conferring different drought responses in rice.

Cold stress (CS) significantly impacts rice (Oryza sativa L.) growth during seedling and heading stages. Based on two-year field observations, this study identified two rice lines, L9 (cold stress-sensitive) and LD18 (cold stress-tolerant), showing contrasting CS responses. L9 exhibited a 38% reduction in photosynthetic efficiency, whereas LD18 remained unchanged, correlating with seed rates. Transcriptome analysis identified differentially expressed genes (DEGs) with LD18 showing enriched pathways (carbon fixation, starch/sucrose metabolism, and glutathione metabolism). LD18 displayed dramatically enhanced expression of MAPK-related genes (LOC4342017, LOC9267741, and LOC4342267) and increased ABA signaling genes (LOC4333690, LOC4345611, and LOC4335640) compared with L9 exposed to CS. Results from qPCR confirmed the enhanced expression of the three MAPK-related genes in LD18 with a dramatic reduction in L9 under CS relative to that under CK. We also observed up to 66% reduction in expression levels of the three genes related to the ABA signaling pathway in L9 relative to LD18 under CS. Consistent with the results of photosynthetic efficiency, metabolic analysis suggests pyruvate metabolism, TCA cycle, and carbon metabolism enrichment in LD18 under CS. The study reveals reprogramming of the carbon assimilation metabolic pathways, emphasizing the critical roles of the key DEGs involved in ABA and MAPK signaling pathways in positive regulation of LD18 response to CS, offering the foundation toward cold tolerance breeding through targeted gene editing.

Planting rice in saline–alkali land can effectively improve saline–alkali soil and increase grain yield, but traditional identification methods for saline–alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the Python machine deep learning method was used to analyze the Raman molecular spectroscopy of rice and assist in feature attribution, in order to study a fast and efficient identification method of saline–alkali-tolerant rice varieties. A total of 156 Raman spectra of four rice varieties (two saline–alkali-tolerant rice varieties and two saline–alkali-sensitive rice varieties) were analyzed, and the wave crests were extracted by an improved signal filtering difference method and the feature information of the wave crest was automatically extracted by scipy.signal.find_peaks. Select K Best (SKB), Recursive Feature Elimination (RFE) and Select F Model (SFM) were used to select useful molecular features. Based on these feature selection methods, a Logistic Regression Model (LRM) and Random Forests Model (RFM) were established for discriminant analysis. The experimental results showed that the RFM identification model based on the RFE method reached a higher recognition rate of 89.36%. According to the identification results of RFM and the identification of feature attribution materials, amylum was the most significant substance in the identification of saline–alkali-tolerant rice varieties. Therefore, an intelligent method for the identification of saline–alkali-tolerant rice varieties based on Raman molecular spectroscopy is proposed.

The purpose of this research is to clarify the genetic groups associated with high-quality germplasms with different genotypes and investigate the responses of their yield and quality traits to varied fertilization regimes, thereby providing references for premium breeding and cultivation practices. A total of 29 samples, including 15 newly developed lines and 14 elite cultivars, were analyzed as to their genetic structure. A split-plot field experiment was conducted to evaluate the responses of yield, quality, and agronomic traits to different fertilization treatments. The 29 samples were classified into three genetic groups. Line 1, developed by our team, formed a unique group, while Lines 3 and 8 constituted another distinct group. In the 2022 trial, the protein content ranged from 6.8% to 9.0%, amylose content from 17.9% to 20.1%, and taste value from 73.8 to 85.7. The top five samples in yield were Line 4, Line 2, Line 5, Line 6, and Line 1, with significant differences among them (p < 0.05) and markedly higher yields compared to other samples. For taste value, Longdao 17029, Line 2, Line 4, Line 5, and Line 6 ranked highest, with Longdao 17029 exhibiting significantly superior taste scores (p < 0.05). Fertilizer treatments ranked by yield were as follows: Treatment 1 (N₁₂₀P₁₀₀K₁₀₀) > Treatment 4 (Longfutianxia slow-release fertilizer) > Treatment 2 (N₉₆P₁₀₀K₁₀₀ + bio-organic fertilizer) > Treatment 3 (N₀P₀K₀), with significant differences (p < 0.05). Treatment 2 achieved a significantly higher taste value than the others (p < 0.05). In the 2023 trial, Longdao 17029 demonstrated higher leaf color index and net photosynthetic rate during the grain-filling stage, compared to Longdao 18 and Longdao 21. Lines 1, 3, and 8 are valuable for broadening the genetic diversity of rice in cold regions. Line 4 and Longdao 17029 show potential as novel germplasms for improving yield and taste quality. Moderate nitrogen reduction, combined with bio-organic fertilizer application, enhances rice taste value.

Background: Cold stress is a major abiotic factor limiting rice growth and yield. Elucidating the molecular mechanisms underlying cold tolerance is therefore of great significance for variety improvement. This study focused on the cold-tolerant variety QJ10 and the cold-sensitive variety DHX2, systematically comparing their physiological and transcriptomic differences under cold stress and identifying genes and modules associated with the cold stress response. Methods: After 0, 3, 5, and 7 days of treatment at 4 °C, we measured leaf MDA and Pro contents, as well as SOD and POD activities. We performed multi-time-point transcriptome sequencing using RNA-seq, and conducted data mining and validation through differential expression analysis, Mfuzz trend clustering, WGCNA co-expression network analysis, GENIE3 regulatory network prediction, and qRT-PCR. Results: Compared with DHX2, QJ10 exhibited lower MDA levels and higher levels of Pro, SOD, and POD under cold stress. Transcriptome analysis identified a total of 13,599 differentially expressed genes. Trend clustering revealed that QJ10 primarily maintained genes associated with growth, development, and basal metabolism, whereas DHX2 tended to activate defense responses. WGCNA identified the MEturquoise module, which showed significant positive correlations with both cold treatment duration and the activities of SOD and POD. Genes in this module were significantly enriched in pathways such as carbon metabolism, photosynthesis, and ion transport. Twelve key transcription factors were identified, nine of which were highly expressed at the late stage of cold stress in QJ10. GENIE3 further predicted seven key regulatory factors centered on OsNAC2, OsLBD, and OsARF19; the expression patterns of these factors were validated by qRT-PCR and were consistent with the transcriptomic results. Conclusions: This study revealed that the cold tolerance of QJ10 is associated with enhanced antioxidant capacity, upregulation of genes related to carbon metabolism, and the induced expression of specific transcription factors. The key transcription factors identified here provide candidate genes for studying the molecular mechanisms of cold tolerance in rice. However, their regulatory functions require further experimental validation.