Integrative Analysis of Metabolome and Transcriptome Provides Insights into the Mechanism of Flower Induction in Pineapple (Ananas comosus (L.) Merr.) by Ethephon.

Exogenous ethylene is commonly utilized to initiate flower induction in pineapple (Ananas comosus (L.) Merr.). However, the molecular mechanisms and metabolic changes involved are not well understood. In this study, we explored the genetic network and metabolic shifts in the 'Comte de Paris' pineapple variety during ethylene-induced flowering. This was achieved through an integrative analysis of metabolome and transcriptome profiles at vegetative shoot apexes (0 d after ethephon treatment named BL_0d), the stage of bract primordia (8 d after ethephon treatment named BL_8d), stage of flower primordia (18 d after ethephon treatment named BL_18d), and the stage of stopped floret differentiation (34 d after ethephon treatment named BL_34d). We isolated and identified 804 metabolites in the pineapple shoot apex and inflorescence, categorized into 24 classes. Notably, 29, 31, and 46 metabolites showed significant changes from BL_0d to BL_8d, BL_8d to BL_18d, and BL_18d to BL_34d, respectively. A marked decrease in indole was observed, suggesting its role as a characteristic metabolite during flower induction. Transcriptomic analysis revealed 956, 1768, and 4483 differentially expressed genes (DEGs) for BL_0d vs. BL_8d, BL_8d vs. BL_18d, and BL_18d vs. BL_34d, respectively. These DEGs were significantly enriched in carbohydrate metabolism and hormone signaling pathways, indicating their potential involvement in flower induction. Integrating metabolomic and transcriptomic data, we identified several candidate genes, such as Agamous-Like9 (AGL9), Ethylene Insensitive 3-like (ETIL3), Apetala2 (AP2), AP2-like ethylene-responsive transcription factor ANT (ANT), and Sucrose synthase 2 (SS2), that play potentially crucial roles in ethylene-induced flower induction in pineapple. We also established a regulatory network for pineapple flower induction, correlating metabolites and DEGs, based on the Arabidopsis thaliana pathway as a reference. Overall, our findings offer a deeper understanding of the metabolomic and molecular mechanisms driving pineapple flowering.

Metabolomic and transcriptomic analyses reveal the mechanism of sweet-acidic taste formation during pineapple fruit development.

Pineapple (Ananas comosus L.) is one of the most valuable subtropical fruit crop in the world. The sweet-acidic taste of the pineapple fruits is a major contributor to the characteristic of fruit quality, but its formation mechanism remains elusive. Here, targeted metabolomic and transcriptomic analyses were performed during the fruit developmental stages in two pineapple cultivars ("Comte de Paris" and "MD-2") to gain a global view of the metabolism and transport pathways involved in sugar and organic acid accumulation. Assessment of the levels of different sugar and acid components during fruit development revealed that the predominant sugar and organic acid in mature fruits of both cultivars was sucrose and citric acid, respectively. Weighted gene coexpression network analysis of metabolic phenotypes and gene expression profiling enabled the identification of 21 genes associated with sucrose accumulation and 19 genes associated with citric acid accumulation. The coordinated interaction of the 21 genes correlated with sucrose irreversible hydrolysis, resynthesis, and transport could be responsible for sucrose accumulation in pineapple fruit. In addition, citric acid accumulation might be controlled by the coordinated interaction of the pyruvate-to-acetyl-CoA-to-citrate pathway, gamma-aminobutyric acid pathway, and tonoplast proton pumps in pineapple. These results provide deep insights into the metabolic regulation of sweetness and acidity in pineapple.

A Spatial-Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction.

Accurate and fine-grained prediction of PM2.5 concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional networks (GCNs), cannot effectively integrate spatial-temporal and meteorological factors and manage dynamic edge relationships among scattered monitoring stations. In this paper, a spatial-temporal causal convolution network framework, ST-CCN-PM2.5, is proposed. Both the spatial effects of multi-source air pollutants and meteorological factors are considered via spatial attention mechanism. Time-dependent features in causal convolution networks are extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-PM2.5 are tuned by Bayesian optimization. Haikou air monitoring station data are employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results include the following points: (1) For a single station, the RMSE, MAE and R2 values of ST-CCN-PM2.5 decreased by 27.05%, 10.38% and 3.56% on average, respectively. (2) For all stations, ST-CCN-PM2.5 achieve the best performance in win-tie-loss experiments. The numbers of winning stations are 68, 63, and 64 out of 95 stations in RMSE (MSE), MAE, and R2, respectively. In addition, the mean MSE, RMSE and MAE of ST-CCN-PM2.5 are 4.94, 2.17 and 1.31, respectively, and the R2 value is 0.92. (3) Shapley analysis shows wind speed is the most influencing factor in fine-grained PM2.5 concentration prediction. The effects of CO and temperature on PM2.5 prediction are moderately significant. Friedman test under different resampling further confirms the advantage of ST-CCN-PM2.5. The ST-CCN-PM2.5 provides a promising direction for fine-grained PM2.5 prediction.

Identification of HCC-Related Genes Based on Differential Partial Correlation Network.

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death, but its pathogenesis is still unclear. As the disease is involved in multiple biological processes, systematic identification of disease genes and module biomarkers can provide a better understanding of disease mechanisms. In this study, we provided a network-based approach to integrate multi-omics data and discover disease-related genes. We applied our method to HCC data from The Cancer Genome Atlas (TCGA) database and obtained a functional module with 15 disease-related genes as network biomarkers. The results of classification and hierarchical clustering demonstrate that the identified functional module can effectively distinguish between the disease and the control group in both supervised and unsupervised methods. In brief, this computational method to identify potential functional disease modules could be useful to disease diagnosis and further mechanism study of complex diseases.

[The value of serum procalcitonin in treatment of community acquired pneumonia in outpatient].

OBJECTIVE: To evaluate the value of serum procalcitonin (PCT) on antibiotic use in treatment of community acquired pneumonia (CAP) in outpatient. METHODS: From November 2006 to February 2008, a total of 127 patients with CAP in outpatient were randomly assigned into two groups: PCT group (n = 63) and control group (n = 64). PCT levels of all patients were measured after study admission. On the base of similarly normal treatment, the control group received antibiotics according to the attending physicians and the PCT group were treated with antibiotics according to serum PCT levels: antibiotic treatment was applied with PCT level > or = 0.25 microg/L and was discouraged with PCT level < 0.25 microg/L. Clinical efficacy, rate of antibiotics use, duration courses and costs of antibiotics were observed. RESULTS: Clinical efficacy of the PCT group was similar with the control group (92.1% vs 87.5%, P > 0.05); rate and costs of antibiotics use was lower, antibiotic duration of the PCT group was shorter than that of the control group (P < 0.05, P < 0.001, P < 0.001). CONCLUSION: PCT could be used in treatment of CAP for antibiotic use in outpatient, which may reduce antibiotic use, shorten antibiotic duration and lower costs of antibiotic.