Unraveling crop enzymatic browning through integrated omics.

Enzymatic browning reactions, triggered by oxidative stress, significantly compromise the quality of harvested crops during postharvest handling. This has profound implications for the agricultural industry. Recent advances have employed a systematic, multi-omics approach to developing anti-browning treatments, thereby enhancing our understanding of the resistance mechanisms in harvested crops. This review illuminates the current multi-omics strategies, including transcriptomic, proteomic, and metabolomic methods, to elucidate the molecular mechanisms underlying browning. These strategies are pivotal for identifying potential metabolic markers or pathways that could mitigate browning in postharvest systems.

Widely targeted metabolomics analysis reveals the mechanism of quality improvement of flue-cured tobacco.

Flue-curing of top leaves with stems is a widely applied curing technology. Owing to the presence of stems, the quality of flue-cured leaves was significantly improved. However, the contribution of stems to flue-cured leaves is still unknown. In this study, the differences in physicochemical properties and metabolomics data between separated leaves (stem(-)) and leaves with stems (stem(+)) were investigated. The metabolic profiling of stem(+) was significantly different from that of stem(-), with phytohormone indole-3-acetic acid (IAA) being one of the most differential metabolites. The presence of stems reduced the rate of water loss in leaves, which led to less ROS accumulation, higher antioxidant enzyme activities and a lower level of membrane lipid peroxidation in stem(+) than in stem(-). The presence of stems also helped maintain the cellular membrane integrity of leaf cells by preventing the accumulation of IAA in leaf cells. Better cellular membrane integrity during flue-curing means a lower risk of leaf browning. In addition, stem(+) had a lower starch content than stem(-) because of a higher level of amylase activity. In summary, these results indicated that the presence of stems caused metabolism changes in leaves, prevented flue-cured leaves from browning and enhanced starch degradation in leaves during flue-curing.

Transcriptome dynamics uncovers long non-coding RNAs response to salinity stress in Chenopodium quinoa.

Chenopodium quinoa is a crop with outstanding tolerance to saline soil, but long non-coding RNAs (LncRNAs) expression profile driven by salt stress in quinoa has rarely been observed yet. Based on the high-quality quinoa reference genome and high-throughput RNA sequencing (RNA-seq), genome-wide identification of LncRNAs was performed, and their dynamic response under salt stress was then investigated. In total, 153,751 high-confidence LncRNAs were discovered and dispersed intensively in chromosomes. Expression profile analysis demonstrated significant differences between LncRNAs and coding RNAs. Under salt stress conditions, 4,460 differentially expressed LncRNAs were discovered, of which only 54 were differentially expressed at all the stress time points. Besides, strongly significantly correlation was observed between salt-responsive LncRNAs and their closest neighboring genes (r = 0.346, p-value < 2.2e-16). Furthermore, a weighted co-expression network was then constructed to infer the potential biological functions of LncRNAs. Seven modules were significantly correlated with salt treatments, resulting in 210 hub genes, including 22 transcription factors and 70 LncRNAs. These results indicated that LncRNAs might interact with transcription factors to respond to salinity stress. Gene ontology enrichment of the coding genes of these modules showed that they were highly related to regulating metabolic processes, biological regulation and response to stress. This study is the genome-wide analysis of the LncRNAs responding to salt stress in quinoa. The findings will provide a solid framework for further functional research of salt responsive LncRNAs, contributing to quinoa genetic improvement.

High-density genetic mapping identified QTLs for anaerobic germination tolerance in rice.

The tolerance of rice anaerobic germination (AG) is the main limiting factor for direct seeding application, yet the genetics mechanism is still in its infancy. In the study, recombinant inbred lines population of TD70 Japonica cultivar and Kasalath Indica cultivar, was employed to construct a high-density genetic map by whole genome re-sequencing. As a result, a genetic map containing 12,328 bin-markers was constructed and a total of 50 QTLs were then detected for CL(coleoptile length), CD (coleoptile diameter), CSA (coleoptile surface area) and CV (coleoptile volume) related traits in the two stages of anaerobic treatment using complete interval mapping method (inclusive composite interval mapping, ICIM). Among the four traits associated with coleoptile, coleoptile volume had the largest number of QTLs (17), followed by coleoptile diameter (16), and coleoptile length had 5 QTLs. These QTLs could explain phenotypic contribution rates ranging from 0.34% to 11.17% and LOD values ranging from 2.52 to 11.57. Combined with transcriptome analysis, 31 candidate genes were identified. Furthermore, 12 stable QTLs were used to detect the aggregation effect analysis. Besides, It was found that individuals with more aggregation synergistic alleles had higher phenotypic values in different environments. Totally, high-density genetic map, QTL mapping and aggregation effect analysis of different loci related to the anaerobic germination of rice seeds were conducted to lay a foundation for the fine mapping of related genes in subsequent assisted breeding.