Multi-Omics Analysis Reveals Mechanisms of Strong Phosphorus Adaptation in Tea Plant Roots

Low phosphorus (P) is a major limiting factor for plant growth in acid soils, which are preferred by tea plants. This study aims to investigate the unique mechanisms of tea plant roots adaptation to low-P conditions. Tea plant roots were harvested for multi-omics analysis after being treated with 0 µmol·L-1 P (0P) and 250 µmol·L-1 P (250P) for 30 days. Under 250P conditions, root elongation was significantly inhibited, and the density of lateral roots was dramatically increased. This suggests that 250P may inhibit the elongation of tea plant roots. Moreover, the P concentration in roots was about 4.58 times higher than that under 0P, indicating that 250P may cause P toxicity in tea plant roots. Contrary to common plants, the expression of CsPT1/2 in tea plant roots was significantly increased by four times at 250P, which indicated that tea plant roots suffering from P toxicity might be due to the excessive expression of phosphate uptake-responsible genes under 250P conditions. Additionally, 94.80% of P-containing metabolites accumulated due to 250P stimulation, most of which were energy-associated metabolites, including lipids, nucleotides, and sugars. Especially the ratio of AMP/ATP and the expression of energy sensor CsSnRKs were inhibited by P application. Therefore, under 250P conditions, P over-accumulation due to the excessive expression of CsPT1/2 may inhibit energy metabolism and thus the growth of tea plant roots.

Bayesian panel smooth transition model with spatial correlation

In this paper, we propose a spatial lag panel smoothing transition regression (SLPSTR) model ty considering spatial correlation of dependent variable in panel smooth transition regression model. This model combines advantages of both smooth transition model and spatial econometric model and can be used to deal with panel data with wide range of heterogeneity and cross-section correlation simultaneously. We also propose a Bayesian estimation approach in which the Metropolis-Hastings algorithm and the method of Gibbs are used for sampling design for SLPSTR model. A simulation study and a real data study are conducted to investigate the performance of the proposed model and the Bayesian estimation approach in practice. The results indicate that our theoretical method is applicable to spatial data with a wide range of spatial structures under finite sample.