Abiotic stress constrains plant growth and productivity worldwide. To survive adverse environmental conditions, plants deploy sophisticated adaptive strategies involving transcriptional reprogramming and metabolic remodeling. Over the past decade, advancements in high-throughput sequencing and mass spectrometry have propelled transcriptomics and metabolomics as pivotal post-genomic disciplines, offering unprecedented opportunities to dissect molecular mechanisms underlying stress responses. This review synthesizes current progress in applying these omics technologies to investigate plant adaptations to key abiotic stresses (thermal, saline, water deficit/excess, and heavy metal stresses). We systematically evaluate the technical strengths and limitations of transcriptomic and metabolomic platforms, highlight recent breakthroughs in stress-responsive gene identification and metabolic pathway elucidation, and discuss emerging challenges in integrative data analysis. By bridging genotype–phenotype relationships through multi-omics approaches, this study aims to deepen our mechanistic understanding of plant stress resilience and inform the design of stress-resilient crops for sustainable agriculture.

Weighted gene co-expression network analysis (WGCNA) is a research method in systematic biology. It is widely used to identify gene modules related to target traits in multi-sample transcriptome data. In order to further explore the molecular mechanism of maize response to low-temperature stress at the seedling stage, B144 (cold stress tolerant) and Q319 (cold stress sensitive) provided by the Maize Research Institute of Heilongjiang Academy of Agricultural Sciences were used as experimental materials, and both inbred lines were treated with 5 °C for 0 h, 12 h, and 24 h, with the untreated material as a control. Eighteen leaf samples were used for transcriptome sequencing, with three biological replicates. Based on the above transcriptome data, co-expression networks of weighted genes associated with low-temperature-tolerance traits were constructed by WGCNA. Twelve gene modules significantly related to low-temperature tolerance at the seedling stage were obtained, and a number of hub genes involved in low-temperature stress regulation pathways were discovered from the four modules with the highest correlation with target traits. These results provide clues for further study on the molecular genetic mechanisms of low-temperature tolerance in maize at the seedling stage.

Maize (Zea mays L.) is highly sensitive to temperature during its growth and development stage. A 1 °C drop in temperature can delay maturity by 10 days, resulting in a yield reduction of over 10%. Low-temperature tolerance in maize is a complex quantitative trait, and different germplasms exhibit significant differences in their responses to low-temperature stress. To explore the differences in gene expression and metabolites between B144 (tolerant) and Q319 (susceptible) during germination under low-temperature stress and to identify key genes and metabolites that respond to this stress, high-throughput transcriptome sequencing was performed on the leaves of B144 and Q319 subjected to low-temperature stress for 24 h and their respective controls using Illumina HiSeq^TM 4000 high-throughput sequencing technology. Additionally, high-throughput metabolite sequencing was conducted on the samples using widely targeted metabolome sequencing technology. The results indicated that low-temperature stress triggered the accumulation of stress-related metabolites such as amino acids and their derivatives, lipids, phenolic acids, organic acids, flavonoids, lignin, coumarins, and alkaloids, suggesting their significant roles in the response to low temperature. This stress also promoted gene expression and metabolite accumulation involved in the flavonoid biosynthesis pathway. Notably, there were marked differences in gene expression and metabolites related to the glyoxylate and dicarboxylate metabolism pathways between B144 and Q319. This study, through multi-omics integrated analysis, provides valuable insights into the identification of metabolites, elucidation of metabolic pathways, and the biochemical and genetic basis of plant responses to stress, particularly under low-temperature conditions.

Potato (Solanum tuberosum) is the fourth-largest food crop worldwide. China is the world’s largest potato producer with 22% of global potato production occurring in the country (Jansky et al. 2009)...

Low temperature severely restricts maize seedling establishment and yield in northern China, but the proteomic basis of low-temperature tolerance in maize remains unclear. This study used TMT-labeled quantitative proteomics combined with data-independent acquisition (DIA) and liquid chromatography–tandem mass spectrometry (LC-MS/MS) to analyze dynamic proteome changes in two maize inbred lines (low-temperature-tolerant B144 and low-temperature-sensitive Q319) at the three-leaf stage under 5 °C treatment. A total of 4367 non-redundant proteins were identified. For differentially expressed proteins (DEPs, fold change >2.0 or <0.5, ANOVA-adjusted p < 0.05, false discovery rate [FDR] < 0.05), B144 showed exclusive upregulation under stress (6 DEPs at 24 h; 16 DEPs at 48 h), while Q319 exhibited mixed regulation (9 DEPs at 24 h: 6 upregulated, 3 downregulated; 21 DEPs at 48 h: 19 upregulated, 2 downregulated). Functional annotation indicated that ribosomal proteins, oxidoreductases, glycerol-3-phosphate permease, and actin were significantly upregulated in both lines. Pathway enrichment analysis revealed associations with carbohydrate metabolism, amino acid biosynthesis, and secondary metabolite synthesis. Weighted gene co-expression network analysis (WGCNA) identified genotype-specific expression patterns: B144 showed differential expression of proteins related to acetyl-CoA synthetase and fatty acid β-oxidation at 24 h and of proteins related to D-3-phosphoglycerate dehydrogenase at 48 h; Q319 showed differential expression of proteasome-related proteins at 24 h and of proteins related to elongation factor 1α (EF-1α) at 48 h. Venn analysis found no shared DEPs between the two lines at 24 h but four overlapping DEPs at 48 h. These results clarify proteomic differences underlying low-temperature tolerance divergence between maize genotypes and provide candidate targets for molecular breeding of low-temperature-tolerant maize.

Chilling injury during the germination stage (CIGS) of maize significantly hinders production, particularly in middle- and high-latitude regions, leading to slow germination, seed decay, and increased susceptibility to pathogens. This study dissects the genetic architecture of CIGS resistance expressed in terms of the relative germination rate (RGR) in maize through association mapping using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). A natural panel of 287 maize inbred lines was evaluated across multiple environments. The results revealed a broad-sense heritability of 0.68 for chilling tolerance, with 12 significant QTLs identified on chromosomes 1, 3, 5, 6, and 10. A genomic prediction analysis demonstrated that the rr-BLUP model outperformed other models in accuracy, achieving a moderate prediction accuracy of 0.44. This study highlights the potential of genomic selection (GS) to enhance chilling tolerance in maize, emphasizing the importance of training population size, marker density, and significant markers on prediction accuracy. These findings provide valuable insights for breeding programs aimed at improving chilling tolerance in maize.