Background: Calcium (Ca) deficiency can cause apple bitter pit, reduce the quality and shelf life. WRKY transcription factors play essential role in plant response to multiple disorders. However, the underlying mechanisms causing bitter pit in apple fruit due to Ca deficiency during storage is extremely limited. Results: In the present study, the nutritional metabolites and reactive oxygen species (ROS) were compared in Ca-deficient and healthy apple fruit (CK) during storage. Results showed that Ca-deficient apples sustained significantly higher production of ROS, PPO activity, flavonoids, total phenol, total soluble solids (TSS), and sucrose contents, but the contents of Ca, H₂O₂, titratable acids (TA), glucose and fructose were significantly lower than those of CK during storage. Principal component analysis (PCA) showed that TSS, •O₂⁻, PPO, malondialdehyde (MDA) and Ca were the main factors, and TSS had a positive correlation with sucrose. Furthermore, transcriptome analysis revealed that WRKYs were co-expressed with sucrose metabolism-related enzymes (SWEETs, SS, SPS). qRT-PCR and correlation analysis indicated that MdWRKY75 was correlated positively with MdSWEET1. Moreover, transient overexpression of MdWRKY75 could significantly increase the sucrose content and promote the expression of MdSWEET1 in apple fruit. Conclusions: Calcium deficiency could decrease antioxidant capacity, accelerate nutritional metabolism and up-regulate the expression of WRKYs in apple with bitter pit. Overexpression of MdWRKY75 significantly increased sucrose accumulation and the expression of MdSWEET1. These findings further strengthened knowledge of the basic molecular mechanisms in calcium deficiency apple flesh and contributed to improving the nutritional quality of apple fruit.

As a typical climacteric fruit, tomato (Solanum lycopersicum) is widely used for studying the ripening process. The negative regulation of tomato fruits by transcription factor SlNAC1 has been reported, but its regulatory network was unclear. In the present study, we screened a transcription factor, SlERF109-like, and found it had a stronger relationship with SlNAC1 at the early stage of tomato fruit development through the use of transcriptome data, RT-qPCR, and correlation analysis. We inferred that SlERF109-like could interact with SlNAC1 to become a regulatory complex that co-regulates the tomato fruit ripening process. Results of transient silencing (VIGS) and transient overexpression showed that SlERF109-like and SlNAC1 could regulate chlorophyll degradation-related genes (NYC1, PAO, PPH, SGR1), carotenoids accumulation-related genes (PSY1, PDS, ZDS), ETH-related genes (ACO1, E4, E8), and cell wall metabolism-related genes expression levels (CEL2, EXP, PG, TBG4, XTH5) to inhibit tomato fruit ripening. A dual-luciferase reporter and yeast one-hybrid (Y1H) showed that SlNAC1 could bind to the SlACO1 promoter, but SlERF109-like could not. Furthermore, SlERF109-like could interact with SlNAC1 to increase the transcription for ACO1 by a yeast two-hybrid (Y2H) assay, a luciferase complementation assay, and a dual-luciferase reporter. A correlation analysis showed that SlERF109-like and SlNAC1 were positively correlated with chlorophyll contents, and negatively correlated with carotenoid content and ripening-related genes. Thus, we provide a model in which SlERF109-like could interact with SlNAC1 to become a regulatory complex that negatively regulates the tomato ripening process by inhibiting SlACO1 expression. Our study provided a new regulatory network of tomato fruit ripening and effectively reduced the waste of resources.

Polymerization products are indispensable for our daily life, and the relevant modeling process plays a vital role in improving product quality. However, the model identification of the related process is a difficult point in industry due multivariate, nonlinear and time-varying characteristics. As for the conventional offline subspace identification methods, the identification accuracy may be not satisfying. To handle such a problem, an enhanced on-line recursive subspace identification method is presented on the basis of principal component analysis and sliding window (RSIMPCA-SW) in this paper to obtain the state space model for polymerization. In the proposed on-line subspace identification approach, the initial L-factor is acquired by the LQ decomposition of the sampled historical data, firstly, and then it is updated recursively through the bona fide method after the new data have been handled by the sliding window rule. Subsequently, principal component analysis (PCA) is introduced to calculate the extended observation matrix, and finally the on-line model parameters are extracted. Compared with the traditional subspace schemes, smaller computation complexity and higher identification precision are anticipated in the proposed method. A case study on the modeling of the ethylene polymerization verifies the effectiveness of the developed approach, in which the related statistical indexes of the obtained identification model are better.