In Vitro Effects of Paclitaxel and Cremophor EL on Human Riboflavin Transporter SLC52A2.

Paclitaxel, a mitotic inhibitor with anti-cancer effects, is dissolved in Cremophor EL (CrEL). However, peripheral neuropathy is a known side effect. As one of the mechanisms of the neuropathy, mitochondrial dysfunction has been proposed, while peroxidation products are involved in the cause of CrEL-induced neurotoxicity. Riboflavin is an essential nutrient required for ATP production in mitochondria and has an antioxidant role as a coenzyme for glutathione. Therefore, riboflavin transporters might play a key role to mitigate neuropathy. However, it is unclear whether paclitaxel and CrEL affect these transporters. In this study, human riboflavin transporter SLC52A2 was used to analyze the effects of paclitaxel and CrEL. CrEL, but not paclitaxel, inhibited uptake of riboflavin in human embryonic kidney 293 cells transfected with the SLC52A2 expression vector, suggesting that altered riboflavin disposition may be involved in the pathogenesis of paclitaxel/CrEL toxicity.

Riboflavin uptake transporter Slc52a2 (RFVT2) is upregulated in the mouse mammary gland during lactation.

While it is well recognized that riboflavin accumulates in breast milk as an essential vitamin for neonates, transport mechanisms for its milk excretion are not well characterized. The multidrug efflux transporter ABCG2 in the apical membrane of milk-producing mammary epithelial cells (MECs) is involved with riboflavin excretion. However, it is not clear whether MECs possess other riboflavin transport systems, which may facilitate its basolateral uptake into MECs. We report here that transcripts encoding the second (SLC52A2) and third (SLC52A3) member of the recently discovered family of SLC52A riboflavin uptake transporters are expressed in milk fat globules from human breast milk. Furthermore, Slc52a2 and Slc52a3 mRNA are upregulated in the mouse mammary gland during lactation. Importantly, the induction ofSlc52a2, which was the major Slc52a riboflavin transporter in the lactating mammary gland, was also observed at the protein level. Subcellular localization studies showed that green fluorescent protein-tagged mouse SLC52A2 mainly localized to the cell membrane, with no preferential distribution to the apical or basolateral membrane in polarized kidney MDCK cells. These results strongly implicate a potential role for SLC52A2 in riboflavin uptake by milk-producing MECs, a critical step in the transfer of riboflavin into breast milk.

Role of STAT5 and epigenetics in lactation-associated upregulation of multidrug transporter ABCG2 in the mammary gland.

The multidrug resistance efflux transporter ATP-binding cassette subfamily G member 2 (ABCG2) is not only overexpressed in certain drug-resistant cancers but is also highly expressed in the mammary gland during lactation, carrying xenobiotics and nutrients into milk. We sought to investigate the molecular mechanisms involved in the upregulation of ABCG2 during lactation. Expression profiling of different mouse Abcg2 mRNA isoforms (E1a, E1b, and E1c) revealed that E1b is predominantly expressed and induced in the lactating mouse mammary gland. Despite this induction, analyses of CpG methylation status and published ChIP-seq datasets reveal that E1b promoter sequences in the virgin gland are already hypomethylated and marked with the open chromatin histone mark H3K4me2. Using a forced-weaning model to shut down lactation, we found that within 24 h there was a significant reduction in Abcg2 mRNA expression and a loss of signal transducer and activator of transcription-5 (STAT5) occupancy at the mouse Abcg2 gene. Luciferase reporter assays further showed that some of these STAT5-binding regions that contained interferon-γ-activated sequence (GAS) motifs function as an enhancer after prolactin treatment. We conclude that Abcg2 is already poised for expression in the virgin mammary gland and that STAT5 plays an important role in Abcg2 expression during lactation.

Maternal Ezetimibe Concentrations Measured in Breast Milk and Its Use in Breastfeeding Infant Exposure Predictions.

BACKGROUND: Lactating mothers taking ezetimibe, an antihyperlipidemic agent, may be hesitant to breastfeed despite the known benefit of breastfeeding to both mother and infant. Currently, no data exist on the presence or concentration of ezetimibe and its main active metabolite, ezetimibe-glucuronide (EZE-glucuronide), in human breast milk. METHODS: Voluntary breast milk samples containing ezetimibe and EZE-glucuronide were attained from lactating mothers taking ezetimibe as part of their treatment. An assay was developed and validated to measure ezetimibe and EZE-glucuronide concentrations in breast milk. A workflow that utilized a developed and evaluated pediatric physiologically based pharmacokinetic (PBPK) model, the measured concentrations in milk, and weight-normalized breast milk intake volumes was applied to predict infant exposures and determine the upper area under the curve ratio (UAR). RESULTS: Fifteen breast milk samples from two maternal-infant pairs were collected. The developed liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay showed an analytical range of 0.039-5.0 ng/mL and 0.39-50.0 ng/mL for ezetimibe and EZE-glucuronide, respectively. The measured concentrations in the breast milk samples were 0.17-1.02 ng/mL and 0.42-2.65 ng/mL of ezetimibe and EZE-glucuronide, respectively. The evaluated pediatric PBPK model demonstrated minimal exposure overlap in adult therapeutic dose and breastfed infant simulated area under the concentration-time curve from time zero to 24 h (AUC24). Calculated UAR across infant age groups ranged from 0.0015 to 0.0026. CONCLUSIONS: PBPK model-predicted ezetimibe and EZE-glucuronide exposures and UAR suggest that breastfeeding infants would receive non-therapeutic exposures. Future work should involve a 'mother-infant pair study' to ascertain breastfed infant plasma ezetimibe and EZE-glucuronide concentrations to confirm the findings of this work.

Induction of multidrug resistance transporter ABCG2 by prolactin in human breast cancer cells.

The multidrug transporter, breast cancer resistance protein, ABCG2, is up-regulated in certain chemoresistant cancer cells and in the mammary gland during lactation. We investigated the role of the lactogenic hormone prolactin (PRL) in the regulation of ABCG2. PRL dose-dependently induced ABCG2 expression in T-47D human breast cancer cells. This induction was significantly reduced by short-interfering RNA-mediated knockdown of Janus kinase 2 (JAK2). Knockdown or pharmacologic inhibition of the down-stream signal transducer and activator of transcription-5 (STAT5) also blunted the induction of ABCG2 by PRL, suggesting a role for the JAK2/STAT5 pathway in PRL-induced ABCG2 expression. Corroborating these findings, we observed PRL-stimulated STAT5 recruitment to a region containing a putative γ-interferon activation sequence (GAS) element at -434 base pairs upstream of the ABCG2 transcription start site. Introduction of a single mutation to the -434 GAS element significantly attenuated PRL-stimulated activity of a luciferase reporter driven by the ABCG2 gene promoter and 5'-flanking region containing the -434 GAS motif. In addition, this GAS element showed strong copy number dependency in its response to PRL treatment. Interestingly, inhibitors against the mitogen-activated protein kinase and phosphoinositide-3-kinase signaling pathways significantly decreased the induction of ABCG2 by PRL without altering STAT5 recruitment to the GAS element. We conclude that the JAK2/STAT5 pathway is required but not sufficient for the induction of ABCG2 by PRL.

Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture.

The coronavirus disease (COVID-19) is a very contagious and dangerous disease that affects the human respiratory system. Early detection of this disease is very crucial to contain the further spread of the virus. In this paper, we proposed a methodology using DenseNet-169 architecture for diagnosing the disease from chest X-ray images of the patients. We used a pretrained neural network and then utilised the transfer learning method for training on our dataset. We also used Nearest-Neighbour interpolation technique for data preprocessing and Adam Optimizer at the end for optimization. Our methodology achieved 96.37 % accuracy which was better than that obtained using other deep learning models like AlexNet, ResNet-50, VGG-16, and VGG-19.