The use of differentiating human induced pluripotent stem cells (hiPSCs) in mini-tissue organoids provides an invaluable resource for regenerative medicine applications, particularly in the field of disease modeling. However, most studies using a kidney organoid model, focused solely on the transcriptomics and did not explore mechanisms of regulating kidney organoids related to metabolic effects and maturational phenotype. Here, we applied metabolomics coupled with transcriptomics to investigate the metabolic dynamics and function during kidney organoid differentiation. Not only did we validate the dominant metabolic alteration from glycolysis to oxidative phosphorylation in the iPSC differentiation process but we also showed that glycine, serine, and threonine metabolism had a regulatory role during kidney organoid formation and lineage maturation. Notably, serine had a role in regulating S-adenosylmethionine (SAM) to facilitate kidney organoid formation by altering DNA methylation. Our data revealed that analysis of metabolic characterization broadens our ability to understand phenotype regulation. The utilization of this comparative omics approach, in studying kidney organoid formation, can aid in deciphering unique knowledge about the biological and physiological processes involved in organoid-based disease modeling or drug screening.Polygenic risk score (PRS) has been shown to be predictive of disease risk such as type 2 diabetes (T2D). However, the existing studies on genetic prediction for T2D only had limited predictive power. To further improve the predictive capability of the PRS model in identifying individuals at high T2D risk, we proposed a new three-step filtering procedure, which aimed to include truly predictive single-nucleotide polymorphisms (SNPs) and avoid unpredictive ones into PRS model. First, we filtered SNPs according to the marginal association p-values (p≤ 5× 10-2) from large-scale genome-wide association studies. Second, we set linkage disequilibrium (LD) pruning thresholds (r2) as 0.2, 0.4, 0.6, and 0.8. Third, we set p-value thresholds as 5× 10-2, 5× 10-4, 5× 10-6, and 5× 10-8. Then, we constructed and tested multiple candidate PRS models obtained by the PRSice-2 software among 182,422 individuals in the UK Biobank (UKB) testing dataset. We validated the predictive capability of the optimal PRS model that was chosen from the testing process in identifying individuals at high T2D risk based on the UKB validation dataset (n = 274,029). The prediction accuracy of the PRS model evaluated by the adjusted area under the receiver operating characteristics curve (AUC) showed that our PRS model had good prediction performance [AUC = 0.795, 95% confidence interval (CI) (0.790, 0.800)]. Specifically, our PRS model identified 30, 12, and 7% of the population at greater than five-, six-, and seven-fold risk for T2D, respectively. After adjusting for sex, age, physical measurements, and clinical factors, the AUC increased to 0.901 [95% CI (0.897, 0.904)]. Therefore, our PRS model could be useful for population-level preventive T2D screening.RNA-binding proteins (RBPs) play significant roles in various cancer types. However, the functions of RBPs have not been clarified in renal papillary cell carcinoma (pRCC). In this study, we identified 31 downregulated and 89 upregulated differentially expressed RBPs on the basis of the cancer genome atlas (TCGA) database and performed functional enrichment analyses. Subsequently, through univariate Cox, random survival forest, and multivariate Cox regression analysis, six RBPs of SNRPN, RRS1, INTS8, RBPMS2, IGF2BP3, and PIH1D2 were screened out, and the prognostic model was then established. Further analyses revealed that the high-risk group had poor overall survival. The area under the curve values were 0.87 and 0.75 at 3 years and 0.78 and 0.69 at 5 years in the training set and test set, respectively. We then plotted a nomogram on the basis of the six RBPs and tumor stage with the substantiation in the TCGA cohort. Moreover, we selected two intersectant RBPs and evaluate their biological effects by GSEA and predicted three drugs, including STOCK1N-28457, pyrimethamine, and trapidil by using the Connectivity Map. Our research provided a novel insight into pRCC and improved the determination of prognosis and individualized therapeutic strategies.Chrysanthemum rhombifolium (Ling et C. Shih), an endemic plant that is extremely well-adapted to harsh environments. However, little is known about its molecular biology of the plant's resistant traits against stress, or even its molecular biology of overall plant. To investigate the molecular biology of C. rhombifolium and mechanism of stress adaptation, we performed transcriptome sequencing of its leaves using an Illumina platform. A total of 130,891 unigenes were obtained, and 97,496 (~74.5%) unigenes were annotated in the public protein database. The similarity search indicated that 40,878 and 74,084 unigenes showed significant similarities to known proteins from NCBI non-redundant and Swissprot protein databases, respectively.  https://www.selleckchem.com/EGFR(HER).html  Of these, 56,213 and 42,005 unigenes were assigned to the Gene Ontology (GO) database and Cluster of Orthologous Groups (COG), respectively, and 38,918 unigenes were mapped into five main categories, including 18 KEGG pathways. Metabolism was the largest category (23,128, 59.4%) among the main KEGG categories, suggesting active metabolic processes in C. rhombifolium. About 2,459 unigenes were annotated to have a role in defense mechanism or stress tolerance. Transcriptome analysis of C. rhombifolium revealed the presence of 12,925 microsatellites in 10,524 unigenes and mono, trip, and dinucleotides having higher polymorphism rates. The phylogenetic analysis based on GME gene among related species confirmed the reliability of the transcriptomic data. This work is the first genetic study of C. rhombifolium as a new plant resource of stress-tolerant genes. This large number of transcriptome sequences enabled us to comprehensively understand the basic genetics of C. rhombifolium and discover novel genes that will be helpful in the molecular improvement of chrysanthemums.