Comparative transcriptomic analysis reveals the molecular mechanism underlying seedling heterosis and its relationship with hybrid contemporary seeds DNA methylation in soybean.

Heterosis is widely used in crop production, but phenotypic dominance and its underlying causes in soybeans, a significant grain and oil crop, remain a crucial yet unexplored issue. Here, the phenotypes and transcriptome profiles of three inbred lines and their resulting F1 seedlings were analyzed. The results suggest that F1 seedlings with superior heterosis in leaf size and biomass exhibited a more extensive recompilation in their transcriptional network and activated a greater number of genes compared to the parental lines. Furthermore, the transcriptional reprogramming observed in the four hybrid combinations was primarily non-additive, with dominant effects being more prevalent. Enrichment analysis of sets of differentially expressed genes, coupled with a weighted gene co-expression network analysis, has shown that the emergence of heterosis in seedlings can be attributed to genes related to circadian rhythms, photosynthesis, and starch synthesis. In addition, we combined DNA methylation data from previous immature seeds and observed similar recompilation patterns between DNA methylation and gene expression. We also found significant correlations between methylation levels of gene region and gene expression levels, as well as the discovery of 12 hub genes that shared or conflicted with their remodeling patterns. This suggests that DNA methylation in contemporary hybrid seeds have an impact on both the F1 seedling phenotype and gene expression to some extent. In conclusion, our study provides valuable insights into the molecular mechanisms of heterosis in soybean seedlings and its practical implications for selecting superior soybean varieties.

Smartphone-based visualized microarray detection for multiplexed harmful substances in milk.

In this paper, we report a sensitive, simple and inexpensive analytical method for the immunoassay microarray based on a smartphone in which various harmful substances in milk could be assayed. Tetracyclines (TCs) and Quinolones (QNs) were selected as the model targets in this study. TCs and QNs antigens were immobilized in the microarray and then samples containing free of antibiotics and corresponding antibodies as well as AgNPs labeled secondary antibodies were added to the microarray. The signal of this competitive format was further amplified by silver enhancement technique based on the development reagents and achieved a visual dots in the array. The resulting microarray could be detected by the smartphone placed in the minicartridge. The limit of detection (LOD) of this novel detection platform was 1.51ngmL-1 (TCs) and 1.74ngmL-1 (QNs). To achieve one-well quantitative analysis, a series of gradient concentration mouse IgG was immobilized in the same well. As a result, an internal standard curve was plotted by the signal of different concentrations of mouse IgG. The results showed that a quantitative detection of TCs and QNs established were consistent with external standard curve. Compared to other methods, this method was superior in terms of detection limit, time saving, and one-well quantitative detected with smartphone which were simple sample-preparation.