PKgraph: an R package for graphically diagnosing population pharmacokinetic models

Resequencing of 410 Sesame Accessions Identifies SINST1 as the Major Underlying Gene for Lignans Variation

Sesame is a promising oilseed crop that produces specific lignans of clinical importance. Hence, a molecular description of the regulatory mechanisms of lignan biosynthesis is essential for crop improvement. Here, we resequence 410 sesame accessions and identify 5.38 and 1.16 million SNPs (single nucleotide polymorphisms) and InDels, respectively. Population genomic analyses reveal that sesame has evolved a geographic pattern categorized into northern (NC), middle (MC), and southern (SC) groups, with potential origin in the southern region and subsequent introduction to the other regions. Selective sweeps analysis uncovers 120 and 75 significant selected genomic regions in MC and NC groups, respectively. By screening these genomic regions, we unveiled 184 common genes positively selected in these subpopulations for exploitation in sesame improvement. Genome-wide association study identifies 17 and 72 SNP loci for sesamin and sesamolin variation, respectively, and 11 candidate causative genes. The major pleiotropic SNPC/A locus for lignans variation is located in the exon of the gene SiNST1. Further analyses revealed that this locus was positively selected in higher lignan content sesame accessions, and the "C" allele is favorable for a higher accumulation of lignans. Overexpression of SiNST1C in sesame hairy roots significantly up-regulated the expression of SiMYB58, SiMYB209, SiMYB134, SiMYB276, and most of the monolignol biosynthetic genes. Consequently, the lignans content was significantly increased, and the lignin content was slightly increased. Our findings provide insights into lignans and lignin regulation in sesame and will facilitate molecular breeding of elite varieties and marker-traits association studies.

Genome-wide association analysis and transcriptome reveal novel loci and a candidate regulatory gene of fatty acid biosynthesis in sesame (Sesamum indicum L.)

The regulatory mechanisms of fatty acid (FA) biosynthesis and triacylglycerols (TAGs) assembly remain largely misunderstood in sesame. Gas chromatography was used to analyze the natural variation in FA compositions and oil content (OC) in 400 sesame accessions grown in three different environments. The phenotypic data was associated with the newly released SNP data from whole-genome resequencing, and 43 significant loci for FA and OC were identified. Comparative transcriptomics analysis of high-OC and low-OC materials was performed, and 515 differentially expressed genes (DEGs) were identified across three seed developmental stages. By integrating the genome-wide association study (GWAS) and DEGs analysis, twenty candidate genes were identified, of which SiTPS1 (trehalose-6-phosphate synthase 1) has emerged as a key regulatory gene of FAs and TAGs metabolism in sesame. Overexpression of SiTPS1 in transgenic Arabidopsis influenced FA composition and significantly increased OC. Our study provides resources for the markers-based improvement of OC and quality in sesame and other crops.

Molecular Characterization, Tumor Microenvironment Association, and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Accumulating evidence suggests that DNA methylation has essential roles in the development of renal cell carcinoma (RCC). Aberrant DNA methylation acts as a vital role in RCC progression through regulating the gene expression, yet little is known about the role of methylation and its association with prognosis in RCC. The purpose of this study is to explore the DNA methylation-driven genes for establishing prognostic-related molecular clusters and providing a basis for survival prediction. In this study, 5,198 differentially expressed genes (DEGs) and 270 DNA methylation-driven genes were selected to obtain 146 differentially expressed DNA methylation-driven genes (DEMDGs). Two clusters were distinguished by consensus clustering using 146 DEMDGs. We further evaluated the immune status of two clusters and selected 106 DEGs in cluster 1. Cluster-based immune status analysis and functional enrichment analysis of 106 DEGs provide new insights for the development of RCC. To predict the prognosis of patients with RCC, a prognostic model based on eight DEMDGs was constructed. The patients were divided into high-risk groups and low-risk groups based on their risk scores. The predictive nomogram and the web-based survival rate calculator (http://127.0.0.1:3496) were built to validate the predictive accuracy of the prognostic model. Gene set enrichment analysis was performed to annotate the signaling pathways in which the genes are enriched. The correlation of the risk score with clinical features, immune status, and drug susceptibility was also evaluated. These results suggested that the prognostic model might be a promising prognostic tool for RCC and might facilitate the management of patients with RCC.

Identified GNGT1 and NMU as Combined Diagnosis Biomarker of Non-Small-Cell Lung Cancer Utilizing Bioinformatics and Logistic Regression

Non-small-cell lung cancer (NSCLC) is one of the most devastating diseases worldwide. The study is aimed at identifying reliable prognostic biomarkers and to improve understanding of cancer initiation and progression mechanisms. RNA-Seq data were downloaded from The Cancer Genome Atlas (TCGA) database. Subsequently, comprehensive bioinformatics analysis incorporating gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the protein-protein interaction (PPI) network was conducted to identify differentially expressed genes (DEGs) closely associated with NSCLC. Eight hub genes were screened out using Molecular Complex Detection (MCODE) and cytoHubba. The prognostic and diagnostic values of the hub genes were further confirmed by survival analysis and receiver operating characteristic (ROC) curve analysis. Hub genes were validated by other datasets, such as the Oncomine, Human Protein Atlas, and cBioPortal databases. Ultimately, logistic regression analysis was conducted to evaluate the diagnostic potential of the two identified biomarkers. Screening removed 1,411 DEGs, including 1,362 upregulated and 49 downregulated genes. Pathway enrichment analysis of the DEGs examined the Ras signaling pathway, alcoholism, and other factors. Ultimately, eight prioritized genes (GNGT1, GNG4, NMU, GCG, TAC1, GAST, GCGR1, and NPSR1) were identified as hub genes. High hub gene expression was significantly associated with worse overall survival in patients with NSCLC. The ROC curves showed that these hub genes had diagnostic value. The mRNA expressions of GNGT1 and NMU were low in the Oncomine database. Their protein expressions and genetic alterations were also revealed. Finally, logistic regression analysis indicated that combining the two biomarkers substantially improved the ability to discriminate NSCLC. GNGT1 and NMU identified in the current study may empower further discovery of the molecular mechanisms underlying NSCLC's initiation and progression.

Phxnlme: An R package that facilitates pharmacometric workflow of Phoenix NLME analyses

BACKGROUND AND OBJECTIVE

Pharmacometric analyses are integral components of the drug development process, and Phoenix NLME is one of the popular software used to conduct such analyses. To address current limitations with model diagnostic graphics and efficiency of the workflow for this software, we developed an R package, Phxnlme, to facilitate its workflow and provide improved graphical diagnostics.

METHODS

Phxnlme was designed to provide functionality for the major tasks that are usually performed in pharmacometric analyses (i.e. nonlinear mixed effects modeling, basic model diagnostics, visual predictive checks and bootstrap). Various estimation methods for modeling using the R package are made available through the Phoenix NLME engine. The Phxnlme R package utilizes other packages such as ggplot2 and lattice to produce the graphical output, and various features were included to allow customizability of the output. Interactive features for some plots were also added using the manipulate R package.

RESULTS

Phxnlme provides enhanced capabilities for nonlinear mixed effects modeling that can be accessed using the phxnlme() command. Output from the model can be graphed to assess the adequacy of model fits and further explore relationships in the data using various functions included in this R package, such as phxplot() and phxvpc.plot(). Bootstraps, stratified up to three variables, can also be performed to obtain confidence intervals around the model estimates. With the use of an R interface, different R projects can be created to allow multi-tasking, which addresses the current limitation of the Phoenix NLME desktop software. In addition, there is a wide selection of diagnostic and exploratory plots in the Phxnlme package, with improvements in the customizability of plots, compared to Phoenix NLME.

CONCLUSIONS

The Phxnlme package is a flexible tool that allows implementation of the analytical workflow of Phoenix NLME with R, with features for greater overall efficiency and improved customizable graphics. Phxnlme is freely available for download on the CRAN repository (https://cran.r-project.org/web/packages/Phxnlme/).

Exploring population pharmacokinetic modeling with resampling visualization

Background. In the last decade, population pharmacokinetic (PopPK) modeling has spread its influence in the whole process of drug research and development. While targeting the construction of the dose-concentration of a drug based on a population of patients, it shows great flexibility in dealing with sparse samplings and unbalanced designs. The resampling approach has been considered an important statistical tool to assist in PopPK model validation by measuring the uncertainty of parameter estimates and evaluating the influence of individuals. Methods. The current work describes a graphical diagnostic approach for PopPK models by visualizing resampling statistics, such as case deletion and bootstrap. To examine resampling statistics, we adapted visual methods from multivariate analysis, parallel coordinate plots, and multidimensional scaling. Results. Multiple models were fitted, the information of parameter estimates and diagnostics were extracted, and the results were visualized. With careful scaling, the dependencies between different statistics are revealed. Using typical examples, the approach proved to have great capacity to identify influential outliers from the statistical perspective, which deserves special attention in a dosing regimen. Discussion. By combining static graphics with interactive graphics, we are able to explore the multidimensional data from an integrated and systematic perspective. Complementary to current approaches, our proposed method provides a new way for PopPK modeling analysis.

AMGET, an R-Based Postprocessing Tool for ADAPT 5

ADAPT 5 is a powerful modeling software for population pharmacokinetic and pharmacodynamic systems analysis, but provides limited built-in functionality for creating pre- and post-analysis diagnostic plots. ADAPT 5 Model Evaluation Graphical Toolkit (AMGET), an external package written in the open source R programming language, was developed specifically to support efficient postprocessing of ADAPT 5 runs, as well as NONMEM and S-ADAPT runs. Using interactive navigational menus, users of AMGET are able to rapidly create informative diagnostic plots enriched by the display of numerical and graphical elements with a high degree of customization using a simple settings spreadsheet. This article describes each feature of the AMGET package and illustrates how it allows users to utilize the powerful numerical routines of the ADAPT 5 package in a more efficient manner through the use of a simulated dataset and a simple pharmacokinetic model optimized using the maximum likelihood expectation maximization (MLEM) algorithm of ADAPT 5.