Search

Additional file 1: Fig. S1. Serum cholesterol and triglycerides levels in WT and FXR KO mice fed with either a CD or WD in 3 age groups. (A) Serum cholesterol levels (Compared with 5-month-old CD-WT, * p < 0.05, ** p < 0.01, *** p < 0.001) (B) Serum triglycerides levels (Compared with 5-month-old CD-WT, # p < 0.05, ## p < 0.01, ### p < 0.001). Unpaired t-test.

Additional file 4: Table S2. RNA sequence quality data.

Additional file 2: Fig. S2. Heatmaps show the fold changes of WD-altered 36 transcripts in (A) WT mice and 6 transcripts in (B) FXR KO mice regardless of ages (fold change ≥2 and adjusted p value < 0.05). Fig. S3. Altered liver metabolites due to differential dietary intake. (A) Diet altered metabolites in both WT and FXR KO mice. (B) Diet changed metabolites only in WT or FXR KO mice (raw p value < 0.05 and FDR < 0.1). Fig. S4. The effects of diets on hepatic bile acids in WT and FXR KO mice. (A) Principal component analyses of hepatic bile acids of WT and FXR KO mice fed with either a CD or WD. (B) Venn diagrams show the numbers of distinct and overlapping bile acids that were changed by differential diets intake in 3 age groups (p < 0.05). (C) A heatmap of relative concentrations of hepatic bile acids. Fig. S5. The effects of diets on serum metabolomes in WT and FXR KO mice. (A) Principal component analyses of serum metabolomes of WT and FXR KO mice fed with either a CD or WD. (B) Venn diagrams show the numbers of distinct and overlapping metabolites that were changed by differential diets intake in 3 age groups (raw p value < 0.05 and FDR < 0.1). The metabolites in purple were affected by diet in both genotypes. Fig. S6. The effects of diets on urine metabolomes in WT and FXR KO mice. (A) Principal component analyses of urine metabolomes of WT and FXR KO mice. (B) Venn diagrams show the numbers of distinct and overlapping metabolites that were changed due to differential diet intake in 3 age groups (raw p value < 0.05 and FDR < 0.1). The metabolites in purple are commonly affected by diet in both genotypes. Fig. S7. The effects of diets on cecal microbiota. (A) Principal component analyses of cecal microbiota at genus level of WT and FXR KO mice fed with either a CD or WD. (B) Venn diagrams show the numbers of distinct and overlapping cecal microbiota at the genus level that were changed by differential diets intake in 3 age groups (raw p value < 0.05). Fig. S8. Age altered liver metabolites in WT and FXR KO mice. (A) Heatmap of metabolites that changed due to age (15 vs. 5) shared in both WT and FXR KO mice. (B) Age-changed metabolites in WT or FXR KO mice (raw p value < 0.05 and FDR < 0.1). Fig. S9. The influence of ages on hepatic bile acids in WT and FXR KO mice. (A) Principal component analyses of hepatic bile acids of 5- or 15-month-old WT and FXR KO mice fed with either a CD or WD. (B) Venn diagrams show the numbers of distinct and overlapping bile acids that were changed in two age groups. Fig. S10. The influence of ages on serum metabolomes in WT and FXR KO mice. (A) Principal component analyses of serum metabolomes of 5- or 15-month-old WT and FXR KO mice fed with either a CD or WD. (B) Venn diagrams show the numbers of distinct and overlapping metabolites that were changed in two age groups. The metabolites in purple were commonly affected by ages in both genotypes (raw p value < 0.05 and FDR < 0.1). Fig. S11. The influence of ages on urine metabolomes in WT and FXR KO mice. (A) Principal component analyses of urine metabolomes of 5- or 15-month-old WT and FXR KO mice fed with either a CD or WD. (B) Venn diagrams show the numbers of distinct and overlapping metabolites that were changed by age (raw p value < 0.05 and FDR < 0.1). The metabolites in purple are commonly affected by age in both genotypes. Fig. S12. The influence of ages on cecal microbiota in WT and FXR KO mice. (A) Principal component analyses of cecal microbiota at genus level of 5- or 15-month-old WT and FXR KO mice fed with either a CD or WD . (B) Venn diagrams show the numbers of distinct and overlapping bacteria at the genus level that were changed by age (raw p value < 0.05). Fig. S13. Altered liver metabolites due to FXR KO. (A) A heatmap shows FXR KO-altered metabolites commonly found in mice with either a CD or WD. (B) FXR KO changed metabolites in WT or FXR KO mice (raw p value < 0.05 and FDR < 0.1). Fig. S14. The effects of FXR KO on hepatic bile acids. (A) Principal component analyses of serum metabolomes of WT and FXR KO mice fed with either a CD or WD and euthanized when they were 5, 10, and 15 months old. (B) Venn diagrams show the numbers of distinct and overlapping metabolites that were altered by FXR KO in CD- and WD-fed mice (raw p value < 0.05 and FDR < 0.1). Fig. S15. The effects of FXR KO on serum metabolomes. (A) Principal component analyses of serum metabolomes of WT and FXR KO mice fed with either a CD or WD and euthanized when they were 5, 10, and 15 months old. (B) Venn diagrams show the numbers of distinct and overlapping metabolites that were altered by FXR KO in CD- and WD-fed mice (raw p value < 0.05 and FDR < 0.1). The metabolites in purple were commonly changed by FXR KO in both CD- and WD-fed mice. Fig. S16. A heatmap shows serum metabolite levels in WT and FXR KO mice fed with either a CD or WD for different durations. Fig. S17. The effects of FXR KO on urine metabolomes. (A) Principal component analyses of urine metabolomes of WT and FXR KO mice fed with either a CD or WD and euthanized when mice were 5, 10, and 15 months old. (B) Venn diagrams show the numbers of distinct and overlapping metabolites that were altered by FXR KO in CD- and WD-fed mice (raw p value < 0.05 and FDR < 0.1). The metabolites in purple were commonly changed by FXR KO in both CD- and WD-fed mice. Fig. S18. A heatmap shows urine metabolite levels in WT and FXR KO mice fed with either a CD or WD for different durations. Fig. S19. The effects of FXR KO on cecal microbiota. (A) Principal component analyses of cecal microbiota at genus level of WT and FXR KO mice fed with either a CD or WD and euthanized when mice were 5, 10, and 15 months old. (B) Venn diagrams show the numbers of distinct and overlapping bacteria that were altered by FXR KO in CD- and WD-fed mice. The bacteria in purple were commonly changed by FXR KO in both CD- and WD-fed mice. (C) A heatmap of relative abundances of cecal microbiota at genus level that were changed by FXR KO irrespective of ages (raw p value < 0.05). Fig. S20. Common changes in hepatic metabolites due to WD intake, aging, and FXR KO. Venn diagram shows the number of altered metabolites by each risk factor. Heatmap of 44 hepatic metabolites that were commonly altered by diet, age, and FXR KO (raw p value < 0.05 and FDR < 0.1). Fig. S21. Spearman’s correlation analysis between hepatic features (76 transcripts and 44 metabolites that were commonly altered by diet, age, and FXR KO) and serum/urine metabolites as well as cecal microbiota at the genus level. *p < 0.05, **p < 0.01 with Hochberg correction. Upregulated transcripts are in red, while downregulated ones are in blue. Fig. S22. Diet, age, and FXR KO altered hepatic, serum, and urine metabolites involved in the urea cycle, TCA cycle, and methionine cycle.

Management of critically ill patients requires simultaneous administration of many medications. Treatment for patient comorbidities may lead to drug-drug interactions which decrease drug efficacy or increase adverse reactions. Current practices rely on a one-size-fits-all dosing approach. Pharmacogenetic testing is generally reserved for addressing problems rather than used proactively to optimize care. We hypothesized that burn and surgery patients will have one or more genetic variants in drug metabolizing pathways used by one or more medications administered during the patient's hospitalization. The aim of this study was to determine the frequency of variants with abnormal function in the primary drug pathways and identify which medications may be impacted. Genetic (19 whole exome and 11 whole genome) and medication data from 30 pediatric burn and surgery patients were analyzed to identify pharmacogene-drug associations. Nineteen patients were identified with predicted altered function in one or more of the following genes: CYP2C9, CYP2C19, CYP2D6, and CYP3A4. The majority had decreased function, except for several patients with CYP2C19 rapid or ultrarapid variants. Some drugs administered during hospitalization that rely on these pathways include hydrocodone, oxycodone, methadone, ibuprofen, ketorolac, celecoxib, diazepam, famotidine, diphenhydramine, and glycopyrrolate. Approximately one-third of the patients tested had functionally impactful genotypes in each of the primary drug metabolizing pathways. This study suggests that genetic variants may in part explain the vast variability in drug efficacy and suggests that future pharmacogenetics research may optimize dosing regimens.

Purpose: Activation of the PI3K pathway occurs in over 40% of bladder urothelial cancers. The aim of this study is to determine the therapeutic potential, the underlying action, and the resistance mechanisms of drugs targeting the PI3K pathway.Experimental Design: Urothelial cancer cell lines and patient-derived xenografts (PDXs) were analyzed for alterations of the PI3K pathway and for their sensitivity to the small-molecule inhibitor pictilisib alone and in combination with cisplatin and/or gemcitabine. Potential predictive biomarkers for pictilisib were evaluated, and RNA sequencing was performed to explore drug resistance mechanisms.Results: The bladder cancer cell line TCCSUP, which harbors a PIK3CA E545K mutation, was sensitive to pictilisib compared to cell lines with wild-type PIK3CA Pictilisib exhibited stronger antitumor activity in bladder cancer PDX models with PI3KCA H1047R mutation or amplification than the control PDX model. Pictilisib synergized with cisplatin and/or gemcitabine in vitro, significantly delayed tumor growth, and prolonged survival compared with single-drug treatment in the PDX models. The phosphorylation of ribosomal protein S6 correlated with response to pictilisib both in vitro and in vivo, and could potentially serve as a biomarker to predict response to pictilisib. Pictilisib activated the compensatory MEK/ERK pathway that likely contributed to pictilisib resistance, which was reversed by cotreatment with the RAF inhibitor sorafenib. RNA sequencing of tumors resistant to treatment suggested that LSP1 downregulation correlated with drug resistance.Conclusions: These preclinical results provide new insights into the therapeutic potential of targeting the PI3K pathway for the treatment of bladder cancer. Clin Cancer Res; 23(21); 6580-91. ©2017 AACR.

We report progress on predicting tumor response to platinum-based chemotherapy with a novel mass spectrometry approach. Fourteen bladder cancer patients were administered one diagnostic microdose each of [14C]carboplatin (1% of the therapeutic dose). Carboplatin-DNA adducts were quantified by accelerator mass spectrometry in blood and tumor samples collected within 24 hours, and compared with subsequent chemotherapy response. Patients with the highest adduct levels were responders, but not all responders had high adduct levels. Four patient-derived bladder cancer xenograft mouse models were used to test the possibility that another drug in the regimen could cause a response. The mice were dosed with [14C]carboplatin or [14C]gemcitabine and the resulting drug-DNA adduct levels were compared with tumor response to chemotherapy. At least one of the drugs had to induce high drug-DNA adduct levels or create a synergistic increase in overall adducts to prompt a corresponding therapeutic response, demonstrating proof-of-principle for drug-DNA adducts as predictive biomarkers. Mol Cancer Ther; 16(2); 376-87. ©2016 AACR.