Search

Melon is an important cash crop grown worldwide, with China leading the world in melon acreage. Melon wilt is becoming more common as the area under melon cultivation gradually increases. Therefore, it is crucial to understand the resistance pattern of Fusarium wilt in melons. In this work, among the four test melon cultivars, YG was highly resistant to Fusarium wilt and 1455 was disease‐resistant. However, 5657 was susceptible, while 5455 was highly susceptible to the disease. We detected 71,852 differentially expressed genes in 16 combinations, including 37,382 with up‐regulation and 3470 with down‐regulation, and we investigated 11 genes associated with resistance to Fusarium wilt through bioinformatics and transcriptome analysis. We identified two differentially expressed cinnamyl alcohol dehydrogenases (CAD) that were down‐regulated in the susceptible melon material in contrast to the resistant variety. Nine chitinase (CHT) genes were differentially expressed, with four showing up‐regulation and four showing down‐regulation in the susceptible compared to the resistant melon material. The MeloCHT genes were located on chromosomes 2, 6, 8, 11 and 12, with four of them on chromosome 8. MeloCAD1 was located on chromosome 1 and MeloCAD2 on chromosome 8. The physicochemical properties of the encoded proteins were predicted by bioinformatics, including theoretical isoelectric point, instability coefficient, relative molecular mass, lipid solubility index and hydrophilicity index. The genes encode proteins with α‐helix, extended strand, random coils and β‐turns. This study provides fundamental knowledge on the transcriptome of melons and establishes a foundation for future research on the detection and application of Fusarium wilt‐resistant genes in melons. [ABSTRACT FROM AUTHOR]

UFMylation involves the covalent modification of substrate proteins with UFM1 (Ubiquitin‐fold modifier 1) and is important for maintaining ER homeostasis. Stalled translation triggers the UFMylation of ER‐bound ribosomes and activates C53‐mediated autophagy to clear toxic polypeptides. C53 contains noncanonical shuffled ATG8‐interacting motifs (sAIMs) that are essential for ATG8 interaction and autophagy initiation. However, the mechanistic basis of sAIM‐mediated ATG8 interaction remains unknown. Here, we show that C53 and sAIMs are conserved across eukaryotes but secondarily lost in fungi and various algal lineages. Biochemical assays showed that the unicellular alga Chlamydomonas reinhardtii has a functional UFMylation pathway, refuting the assumption that UFMylation is linked to multicellularity. Comparative structural analyses revealed that both UFM1 and ATG8 bind sAIMs in C53, but in a distinct way. Conversion of sAIMs into canonical AIMs impaired binding of C53 to UFM1, while strengthening ATG8 binding. Increased ATG8 binding led to the autoactivation of the C53 pathway and sensitization of Arabidopsis thaliana to ER stress. Altogether, our findings reveal an ancestral role of sAIMs in UFMylation‐dependent fine‐tuning of C53‐mediated autophagy activation. Synopsis: Phylogenomic profiling has shown that UFMylation coevolves with the shuffled ATG8‐interacting motifs (sAIMs) in the ER‐phagy receptor C53. Further mechanistic analyses demonstrated that sAIMs regulate C53‐mediated autophagy by competitively binding UFM1 and ATG8.UFMylation and C53 autophagy pathways are conserved in eukaryotes, but secondarily lost in fungi and various parasitic and algal species.UFMylation is crucial for ER stress tolerance in the unicellular alga Chlamydomonas reinhardtii.Both UFM1 and ATG8 bind the shuffled AIM sequences within the intrinsically disordered domain of C53, yet their binding modes are different.Competitive binding of either UFM1 or ATG8 to C53 shuffled AIM sequences regulates C53‐mediated autophagy and ER stress tolerance. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models are valuable research tools for making accurate predictions. However, ML models often unreliably extrapolate outside their training data. The multiparameter delta method quantifies uncertainty for ML models (and generally for other nonlinear models) with parameters trained by least squares regression. The uncertainty measure requires the gradient of the model prediction and the Hessian of the loss function, both with respect to model parameters. Both the gradient and Hessian can be readily obtained from most ML software frameworks by automatic differentiation. We show examples of the uncertainty method in applications of molecular simulations and neural networks. We further show that the uncertainty measure is larger for input space regions that are not part of the training data. Therefore, this method can be used to identify extrapolation and to aid in selecting training data or assessing model reliability. [ABSTRACT FROM AUTHOR]

Hybrid encoding technique is an efficient method to expand the coding capacity in radio-frequency identification chipless tag design. In this study, a novel hybrid coding technique by combining amplitude deviation and frequency position encoding is proposed. The proposed tag is based on three circle ring resonators with loaded resistors. A transmission line model is employed to illustrate the operation principle of the impedance loading technique. A coding capacity of at least 23.7 bits is obtained within a compacted dimension of 3 cm × 3 cm. Corresponding experiments on realised prototypes are provided to validate the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

In Arabidopsis, the phytohormone abscisic acid (ABA) plays a vital role in inhibiting seed germination and in postgermination seedling establishment. In the ABA signaling pathway, ABI5, a basic Leu zipper transcription factor, has important functions in the regulation of seed germination. ABI5 protein localizes in nuclear bodies, alongwith AFP, COP1, and SIZ1, and was degraded through the 26S proteasome pathway. However, the mechanisms of ABI5 nuclear body formation and ABI5 protein degradation remain obscure. In this study, we found that the Arabidopsis CROWDED NUCLEI (CRWN) proteins, predicted nuclear matrix proteins essential for maintenance of nuclear morphology, also participate in ABA-controlled seed germination by regulating the degradation ofABI5 protein. During seed germination, the crwn mutants are hypersensitive to ABA and have higher levels of ABI5 protein compared to wild type. Genetic analysis suggested that CRWNs act upstreamof ABI5. The observation that CRWN3 colocalizes with ABI5 in nuclear bodies indicates that CRWNs might participate in ABI5 protein degradation in nuclear bodies. Moreover, we revealed that the extreme C-terminal of CRWN3 protein is necessary for its function in the response to ABA in germination. Our results suggested important roles of CRWNs in ABI5 nuclear body organization and ABI5 protein degradation during seed germination. [ABSTRACT FROM AUTHOR]

Nitric oxide (NO) is a signal molecule in plants and animals. Arabidopsis GSNO reductase1 (AtGSNOR1) catalyzes metabolism of S‐nitrosoglutathione (GSNO) which is a major biologically active NO species. The GSNOR1 loss‐of‐function mutant gsnor1‐3 overaccumulates GSNO with inherent high S‐nitrosylation level and resistance to the oxidative stress inducer paraquat (1,1′‐dimethyl‐4,4′‐bipyridinium dichloride). Here, we report the characterization of dgl1‐3 as a genetic suppressor of gsnor1‐3. DGL1 encodes a subunit of the oligosaccharyltransferse (OST) complex which catalyzes the formation of N‐glycosidic bonds in N‐glycosylation. The fact that dgl1‐3 repressed the paraquat resistance of gsnor1‐3 meanwhile gsnor1‐3 rescued the embryo‐lethal and post‐embryonic development defect of dgl1‐3 reminded us the possibility that S‐nitrosylation and N‐glycosylation crosstalk with each other through co‐substrates. By enriching glycoproteins in gsnor1‐3 and mass spectrometry analysis, TGG2 (thioglucoside glucohydrolase2) was identified as one of co‐substrates with high degradation rate and elevated N‐glycosylation level in gsnor1‐3 ost3/6. The S‐nitrosylation and N‐glycosylation profiles were also modified in dgl1‐3 and gsnor1‐3. Thereby, we propose a linkage between S‐nitrosylation and N‐glycosylation through co‐substrates. [ABSTRACT FROM AUTHOR]