Pharmacokinetic interactions between tacrolimus and Wuzhi capsule in liver transplant recipients: Genetic polymorphisms affect the drug interaction

Wuzhi capsule (WZC), a commonly used Chinese patent medicine to treat various types of liver dysfunction in China, increases the exposure of tacrolimus (TAC) in liver transplant recipients. However, this interaction has inter-individual variability, and the underlying mechanism remains unclear. Current research indicates that CYP3A4/5 and drug transporters influence the disposal of both drugs. This study aims to evaluate the association between TAC dose-adjusted trough concentration (C/D) and specific genetic polymorphisms of CYP3A4/5, drug transporters and pregnane x receptor (PXR), and plasma levels of major WZC components, deoxyschisandrin and γ-schisandrin, in liver transplant patients receiving both TAC and WZC. Liquid chromatography-tandem-mass spectrometry was used to detect the plasma levels of deoxyschisandrin and γ-schisandrin, and nine polymorphisms related to metabolic enzymes, transporters and PXR were genotyped by sequencing. A linear mixed model was utilized to assess the impact of the interaction between genetic variations and WZC components on TAC lnC/D. Our results indicate a significant association of TAC lnC/D with the plasma levels of deoxyschisandrin and γ-schisandrin. Univariate analysis demonstrated three polymorphisms in the genes ABCB1 (rs2032582), ABCC2 (rs2273697), ABCC2 (rs3740066), and PXR (rs3842689) interact with both deoxyschisandrin and γ-schisandrin, influencing the TAC lnC/D. In multiple regression model analysis, the interactions between deoxyschisandrin and both ABCB1 (rs2032582) and ABCC2 (rs3740066), post-operative day (β < 0.001, p < 0.001), proton pump inhibitor use (β = -0.152, p = 0.008), body mass index (β = 0.057, p < 0.001), and ABCC2 (rs717620, β = -0.563, p = 0.041), were identified as significant factors of TAC lnC/D, accounting for 47.89% of the inter-individual variation. In summary, this study elucidates the influence of the interaction between ABCB1 and ABCC2 polymorphisms with WZC on TAC lnC/D. These findings offer a scientific basis for their clinical interaction, potentially aiding in the individualized management of TAC therapy in liver transplant patients.

Integrated Bioinformatics Analysis of DNA Methylation Biomarkers in Thyroid Cancer Based on TCGA Database

Previous studies have reported a cluster of aberrant promoter methylation changes associated with silencing of tumor suppressor genes in thyroid cancer (TC), but these results of individual genes are far from enough. In this work, we aimed to investigate the onset and pattern of methylation changes during the progression of TC by informatics analysis. We downloaded the DNA methylation and RNA sequencing datasets from The Cancer Genome Atlas focusing on TC. Abnormally methylated differentially expressed genes (DEGs) were sorted and pathways were analyzed. The KEGG and GO were then used to perform enrichment and functional analysis of identified pathways and genes. Gene-drug interaction network and human protein atlas were applied to obtain feature DNA methylation biomarkers. In total, we identified 2170 methylation-driven DEGs, including 1054 hypermethylatedlow-expression DEGs and 1116 hypomethylated-high-expression DEGs at the screening step. Further analysis screened total of eight feature DNA methylation biomarkers (RXRG, MET, PDGFRA, FCGR3A, VEGFA, CSF1R, FCGR1A and C1QA). Pathway analysis showed that aberrantly methylated DEGs mainly associated with transcriptional misregulation in cancer, MAPK signaling, and intrinsic apoptotic signaling in TC. Taken together, we have identified novel aberrantly methylated genes and pathways linked to TC, which might serve as novel biomarkers for precision diagnosis and disease treatment.

Weighted gene co-expression network analysis and drug-gene interaction bioinformatics uncover key genes associated with various presentations of malaria infection in African children and major drug candidates

Malaria is a fatal parasitic disease with unelucidated pathogenetic mechanism. Herein, we aimed to uncover genes associated with different clinical aspects of malaria based on the GSE1124 dataset that is publicly accessible by using WGCNA. We obtained 16 co-expression modules and their correlations with clinical features. Using the MCODE tool, we identified THEM4, STYX, VPS36, LCOR, KIAA1143, EEA1, RAPGEF6, LOC439994, ZBTB33, PTPN22, ESCO1, and KLF3 as hub genes positively associated with Plasmodium falciparum infection (ASPF). These hub genes were involved in the biological processes of endosomal transport, regulation of natural killer cell proliferation, and KEGG pathways of endocytosis and fatty acid elongation. For the purple module negatively correlated with ASPF, we identified 19 hub genes that were involved in the biological processes of positive regulation of cellular protein catabolic process and KEGG pathways of other glycan degradation. For the salmon module positively correlated with severe malaria anemia (SMA), we identified 17 hub genes that were among those driving the biological processes of positive regulation of erythrocyte differentiation. For the brown module positively correlated with cerebral malaria (CM), we identified eight hub genes and these genes participated in phagolysosome assembly and positive regulation of exosomal secretion, and animal mitophagy pathway. For the tan module negatively correlated with CM, we identified four hub genes that were involved in CD8-positive, alpha-beta T cell differentiation and notching signaling pathway. These findings may provide new insights into the pathogenesis of malaria and help define new diagnostic and therapeutic approaches for malaria patients.

Identification of gene-drug interactions that impact patient survival in TCGA

BACKGROUND

With the advent of large scale biological data collection for various diseases, data analysis pipelines and workflows need to be established to build frameworks for integrative analysis. Here the authors present a pipeline for identifying disease specific gene-drug interactions using CNV (Copy Number Variation) and clinical data from the TCGA (The Cancer Genome Atlas) project. Two cancer types were selected for analysis, LGG (Brain lower grade glioma) and GBM (Glioblastoma multiforme), due to the possible progression from LGG to GBM in some cases. The copy number and clinical data were then used to preform survival analysis on a gene by gene basis on sub-populations of patients exposed to a given drug.

RESULTS

Several gene-drug interactions are identified, where the copy number of a gene is associated to survival of a patient exposed to a certain drug. Both Irinotecan/HAS2 (Hyaluronan synthase 2) and Bevacizumab/PGAM1 (Phosphoglycerate mutase 1) are interactions found in this study with independent confirmation. Independent work in colon, breast cancer and leukemia (Györffy, Breast Cancer Res Treat 123:725-731, 2010; Mueller, Mol Cancer Ther 11:3024-3032, 2010; Hitosugi, Cancer Cell 13:585-600, 2012) showed these two interactions can lead to increased survival.

CONCLUSION

While the pipeline produced several possible interactions where increased survival is linked to normal or increased copy number of a given gene for patients treated with a given drug, no instance of low copy number or full deletion was linked to increased survival. The development of this pipeline shows a promising utility to identify possible beneficial gene-drug interactions that could improve patient survival and may illustrate some of the problems inherent in this kind of analysis on these data.