Understanding molecular mechanisms underlying plant salinity tolerance provides valuable knowledgebase for effective crop improvement through genetic engineering. Current proteomic technologies, which support reliable and high-throughput analyses, have been broadly used for exploring sophisticated molecular networks in plants.In the current study, we compared phosphoproteomic and proteomic changes in roots of different soybean seedlings of a salt-tolerant cultivar (Wenfeng07) and a salt-sensitive cultivar (Union85140) induced by salt stress. The root samples of Wenfeng07 and Union85140 at threetrifoliate stage were collected at 0 h, 0.5 h, 1 h, 4 h, 12 h, 24 h, and 48 h after been treated with 150 mM NaCl. LC-MS/MS based phosphoproteomic analysis of these samples identified a total of 2692 phosphoproteins and 5509 phosphorylation sites. Of these, 2344 phosphoproteins containing 3744 phosphorylation sites were quantitatively analyzed. Our results showed that 1163 phosphorylation sites were differentially phosphorylated in the two compared cultivars. Among them, 10 MYB/MYB transcription factor like proteins were identified with fluctuating phosphorylation modifications at different time points, indicating that their crucial roles in regulating flavonol accumulation might be mediated by phosphorylated modifications. In addition, the protein expression profiles of these two cultivars were compared using LC MS/MS based shotgun proteomic analysis, and expression pattern of all the 89 differentially expressed proteins were independently confirmed by qRT-PCR. Interestingly, the enzymes involved in chalcone metabolic pathway exhibited positive correlations with salt tolerance. We confirmed the functional relevance of chalcone synthase, chalcone isomerase, and cytochrome P450 monooxygenase genes using soybean composites and Arabidopsis thaliana mutants, and found that their salt tolerance were positively regulated by chalcone synthase, but was negatively regulated by chalcone isomerase and cytochrome P450 monooxygenase. A novel salt tolerance pathway involving chalcone metabolism, mostly mediated by phosphorylated MYB transcription factors, was proposed based on our findings. (The mass spectrometry raw data are available via ProteomeXchange with identifier

Salinity causes osmotic stress to crops and limits their productivity. To understand the mechanism underlying soybean salt tolerance, proteomics approach was used to identify phosphoproteins altered by NaCl treatment. Results revealed that 412 of the 4698 quantitatively analyzed phosphopeptides were significantly up-regulated on salt treatment, including a phosphopeptide covering the serine 59 in the transcription factor GmMYB173. Our data showed that GmMYB173 is one of the three MYB proteins differentially phosphorylated on salt treatment, and a substrate of the casein kinase-II. MYB recognition sites exist in the promoter of flavonoid synthase gene GmCHS5 and one was found to mediate its recognition by GmMYB173, an event facilitated by phosphorylation. Because GmCHS5 catalyzes the synthesis of chalcone, flavonoids derived from chalcone were monitored using metabolomics approach. Results revealed that 24 flavonoids of 6745 metabolites were significantly up-regulated after salt treatment. We further compared the salt tolerance and flavonoid accumulation in soybean transgenic roots expressing the 35S promoter driven cds and RNAi constructs of GmMYB173 and GmCHS5, as well as phospho-mimic (GmMYB173S59D) and phospho-ablative (GmMYB173S59A) mutants of GmMYB173. Overexpression of GmMYB173S59D and GmCHS5 resulted in the highest increase in salt tolerance and accumulation of cyaniding-3-arabinoside chloride, a dihydroxy B-ring flavonoid. The dihydroxy B-ring flavonoids are more effective as anti-oxidative agents when compared with monohydroxy B-ring flavonoids, such as formononetin. Hence the salt-triggered phosphorylation of GmMYB173, subsequent increase in its affinity to GmCHS5 promoter and the elevated transcription of GmCHS5 likely contribute to soybean salt tolerance by enhancing the accumulation of dihydroxy B-ring flavonoids.

No abstract

No abstract

Literary evidence depicts that aggregated β-amyloid (Aβ) leads to the pathogenesis of Alzheimer’s disease (AD). Although many traditional Chinese medicines (TCMs) are effective in treating neurodegenerative diseases, there is no effective way for identifying active compounds from their complicated chemical compositions. Instead of using a traditional herbal separation method with low efficiency, we herein apply UHPLC-DAD-TOF/MS for the accurate identification of the active compounds that inhibit the fibrillation of Aβ (1-42), via an evaluation of the peak area of individual chemical components in chromatogram, after incubation with an Aβ peptide. Using the neuroprotective herbal plant Scutellaria baicalensis (SB) as a study model, the inhibitory effect on Aβ by its individual compounds, were validated using the thioflavin-T (ThT) fluorescence assay, biolayer interferometry analysis, dot immunoblotting and native gel electrophoresis after UHPLC-DAD-TOF/MS analysis. The viability of cells after Aβ (1-42) incubation was further evaluated using both the tetrazolium dye (MTT) assay and flow cytometry analysis. Thirteen major chemical components in SB were identified by UHPLC-DAD-TOF/MS after incubation with Aβ (1–42). The peak areas of two components from SB, baicalein and baicalin, were significantly reduced after incubation with Aβ (1–42), compared to compounds alone, without incubation with Aβ (1–42). Consistently, both compounds inhibited the formation of Aβ (1–42) fibrils and increased the viability of cells after Aβ (1–42) incubation. Based on the hypothesis that active chemical components have to possess binding affinity to Aβ (1–42) to inhibit its fibrillation, a new application using UHPLC-DAD-TOF/MS for accurate identification of inhibitors from herbal plants on Aβ (1–42) fibrillation was developed.

No abstract

The incidence of rheumatoid arthritis (RA) is increasing with age. DNA fragments is known to accumulate in certain autoimmune diseases, but the mechanistic relationship among ageing, DNA fragments and RA pathogenesis remain unexplored. Here we show that the accumulation of DNA fragments, increasing with age and regulated by the exonuclease TREX1, promotes abnormal activation of the immune system in an adjuvant‐induced arthritis (AIA) rat model. Local overexpression of TREX1 suppresses synovial inflammation in rats, while conditional genomic deletion of TREX1 in AIA rats result in higher levels of circulating free (cf) DNA and hence abnormal immune activation, leading to more severe symptoms. The dysregulation of the heterodimeric transcription factor AP-1, formed by c-Jun and c-Fos, appear to regulate both TREX1 expression and SASP induction. Thus, our results confirm that DNA fragments are inflammatory mediators, and TREX1, downstream of AP-1, may serve as regulator of cellular immunity in health and in RA.

Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40°C with 5°C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25°C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15°C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy.