Atorvastatin modulates drug transporters and ameliorates nicotine-induced testicular toxicity

Beneficial impact of Nesfatin-1 on reproductive dysfunction induced by nicotine in male rats: Possible modulation of autophagy and pyroptosis signaling pathways

This study was conducted to explore the beneficial impact of nesfatin-1 on reproductive dysfunction induced by nicotine (NT) in male rats with possible modulation of autophagy and pyroptosis signaling pathways. This research was performed on 40 Wistar male rats. They were distributed into four groups: control, normal+nesfatin-1, NT, and NT+nesfatin-1. At the end of the experimental period, the serum was separated for assay of testosterone, FSH and LH. Also, sperm parameters were determined. Histopathological examination of testicular tissue and immunohistochemical analysis was done for mammalian target of rapamycin, AMP-activated protein kinase, and mitogen-activated protein kinases including phosphorylated extracellular signal regulated kinase and phosphorylated cJun N-terminal kinase. Relative gene expression was determined for testicular nucleotide oligomerization domain (NOD)-like receptors proteins and Caspase-1, and autophagy markers including microtubule-associated protein 1 light chain 3 alpha and Beclin-1. Also, the following testicular parameters were assayed: 3β-hydroxysteroid dehydrogenase, 17β-hydroxysteroid dehydrogenase, malondialdehyde, superoxide dismutase activity, catalase, glucose-6 phosphate dehydrogenase, reactive oxygen species, caspase-3 activity, IL-1β, IL-18, mitochondrial transmembrane potential and Complex-I activity. The results revealed that the normal+nesfatin-1 group showed insignificant changes as compared to the control group. Meanwhile, the NT group exhibited prominent reproductive dysfunction in male rats. On the other hand, in the NT+nesfatin-1 group nesfatin-1 notably attenuated this reproductive dysfunction as evidenced by improvement of hormonal assay, sperm parameters, histopathological picture, immunohistochemical evaluation and real time relative gene expressions. In conclusion: Nesfatin-1 alleviated the impairment of male reproductive functions induced by NT via enhancement of autophagy pathways, suppression of pyroptosis, apoptosis, mitochondrial dysfunction and ROS production. Thus nesfatin-1 may offer a novel protective or therapeutic access for treating male infertility.

SLCO1B1 Phenotype and CYP3A5 Polymorphism Significantly Affect Atorvastatin Bioavailability

Atorvastatin, prescribed for the treatment of hypercholesterolemia, demonstrated overwhelming benefits in reducing cardiovascular morbidity and mortality. However, many patients discontinue therapy due to adverse reactions, especially myopathy. The Dutch Pharmacogenetics Working Group (DPWG) recommends an alternative agent to atorvastatin and simvastatin or a dose adjustment depending on other risk factors for statin-induced myopathy in SLCO1B1 rs4149056 CC or TC carriers. In contrast, the Clinical Pharmacogenetics Implementation Consortium (CPIC) published their guideline on simvastatin, but not on atorvastatin. In this work, we aimed to demonstrate the effect of SLCO1B1 phenotype and other variants (e.g., in CYP3A4/5, UGT enzymes or SLC transporters) on atorvastatin pharmacokinetics. For this purpose, a candidate-gene pharmacogenetic study was proposed. The study population comprised 156 healthy volunteers enrolled in atorvastatin bioequivalence clinical trials. The genotyping strategy comprised a total of 60 variants in 15 genes. Women showed higher exposure to atorvastatin compared to men (p = 0.001), however this difference disappeared after dose/weight (DW) correction. The most relevant pharmacogenetic differences were the following: AUC/DW and C_max /DW based on (a) SLCO1B1 phenotype (p < 0.001 for both) and (b) CYP3A5*3 (p = 0.004 and 0.018, respectively). As secondary findings: SLC22A1 *2/*2 genotype was related to higher C_max/DW (ANOVA p = 0.030) and SLC22A1 *1/*5 genotype was associated with higher Vd/F (ANOVA p = 0.032) compared to SLC22A1 *1/*1, respectively. Finally, UGT2B7 rs7439366 *1/*1 genotype was associated with higher t_max as compared with the *1/*3 genotype (ANOVA p = 0.024). Based on our results, we suggest that SLCO1B1 is the best predictor for atorvastatin pharmacokinetic variability and that prescription should be adjusted based on it. We suggest that the CPIC should include atorvastatin in their statin-SLCO1B1 guidelines. Interesting and novel results were observed based on CYP3A5 genotype, which should be confirmed with further studies.

Atorvastatin, prescribed for the treatment of hypercholesterolemia, demonstrated overwhelming benefits in reducing cardiovascular morbidity and mortality. However, many patients discontinue therapy due to adverse reactions, especially myopathy. The Dutch Pharmacogenetics Working Group (DPWG) recommends an alternative agent to atorvastatin and simvastatin or a dose adjustment depending on other risk factors for statin-induced myopathy in SLCO1B1 rs4149056 CC or TC carriers. In contrast, the Clinical Pharmacogenetics Implementation Consortium (CPIC) published their guideline on simvastatin, but not on atorvastatin. In this work, we aimed to demonstrate the effect of SLCO1B1 phenotype and other variants (e.g., in CYP3A4/5, UGT enzymes or SLC transporters) on atorvastatin pharmacokinetics. For this purpose, a candidate-gene pharmacogenetic study was proposed. The study population comprised 156 healthy volunteers enrolled in atorvastatin bioequivalence clinical trials. The genotyping strategy comprised a total of 60 variants in 15 genes. Women showed higher exposure to atorvastatin compared to men (p = 0.001), however this difference disappeared after dose/weight (DW) correction. The most relevant pharmacogenetic differences were the following: AUC/DW and C_max /DW based on (a) SLCO1B1 phenotype (p < 0.001 for both) and (b) CYP3A5*3 (p = 0.004 and 0.018, respectively). As secondary findings: SLC22A1 *2/*2 genotype was related to higher C_max/DW (ANOVA p = 0.030) and SLC22A1 *1/*5 genotype was associated with higher Vd/F (ANOVA p = 0.032) compared to SLC22A1 *1/*1, respectively. Finally, UGT2B7 rs7439366 *1/*1 genotype was associated with higher t_max as compared with the *1/*3 genotype (ANOVA p = 0.024). Based on our results, we suggest that SLCO1B1 is the best predictor for atorvastatin pharmacokinetic variability and that prescription should be adjusted based on it. We suggest that the CPIC should include atorvastatin in their statin-SLCO1B1 guidelines. Interesting and novel results were observed based on CYP3A5 genotype, which should be confirmed with further studies.

Multimodal network diffusion predicts future disease-gene-chemical associations

MOTIVATION

Precision medicine is an emerging field with hopes to improve patient treatment and reduce morbidity and mortality. To these ends, computational approaches have predicted associations among genes, chemicals and diseases. Such efforts, however, were often limited to using just some available association types. This lowers prediction coverage and, since prior evidence shows that integrating heterogeneous data is likely beneficial, it may limit accuracy. Therefore, we systematically tested whether using more association types improves prediction.

RESULTS

We study multimodal networks linking diseases, genes and chemicals (drugs) by applying three diffusion algorithms and varying information content. Ten-fold cross-validation shows that these networks are internally consistent, both within and across association types. Also, diffusion methods recovered missing edges, even if all the edges from an entire mode of association were removed. This suggests that information is transferable between these association types. As a realistic validation, time-stamped experiments simulated the predictions of future associations based solely on information known prior to a given date. The results show that many future published results are predictable from current associations. Moreover, in most cases, using more association types increases prediction coverage without significantly decreasing sensitivity and specificity. In case studies, literature-supported validation shows that these predictions mimic human-formulated hypotheses. Overall, this study suggests that diffusion over a more comprehensive multimodal network will generate more useful hypotheses of associations among diseases, genes and chemicals, which may guide the development of precision therapies.

AVAILABILITY AND IMPLEMENTATION

Code and data are available at https://github.com/LichtargeLab/multimodal-network-diffusion.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.