The polymorphic hepatic enzyme CYP2C19 catalyzes the metabolism of clinically important drugs such as clopidogrel, proton-pump inhibitors, and others and clinical pharmacogenetic testing for clopidogrel is increasingly common. The CYP2C19*10 SNP is located 1 bp upstream the CYP2C19*2 SNP. Despite the low frequency of the CYP2C19*10 allele, its impact on metabolism of CYP2C19 substrates and CYP2C19*2 genotyping makes it an important SNP to consider for pharmacogenetic testing of CYP2C19. However, the effect of the CYP2C19*10 allele on clopidogrel metabolism has not been explored to date. We measured the enzymatic activity of the CYP2C19.10 protein against clopidogrel. The catalytic activity of CYP2C19.10 in the biotransformation of clopidogrel and 2-oxo-clopidgorel was significantly decreased relative to wild type CYP2C19.1B. We also report that the CYP2C19*10 SNP interferes with the CYP2C19*2 TaqMan® genotyping assay, resulting in miscalling of CYP2C19*10/*2 as CYP2C19*2/*2. Our data provide evidence of CYP2C19.10’s reduced metabolism of clopidogrel and 2-oxo-clopidogrel.
CYP2C19*10; Clopidogrel; Pharmacokinetic; Pharmacogenetic; genotyping
AIM: To investigate precore/basal core promoter (PC/BCP) mutants throughout hepatitis B virus (HBV) infection and to determine their relationship to hepatitis B early antigen (HBeAg) titers.
METHODS: We enrolled 191 patients in various stages of HBV infection at the Huashan Hospital and the Taizhou Municipal Hospital from 2010 to 2012. None of the patients received antiviral therapy. HBV DNA from serum, was quantified by real-time PCR. The HBV genotype was determined by direct sequencing of the S gene. We used the Simpleprobe ultrasensitive quantitative method to detect PC/BCP mutants in each patient. We compared the strain number, percentage, and the changes in PC/BCP mutants in different phases, and analyzed the relationship between PC/BCP mutants and HBeAg by multiple linear regression and logistic regression.
RESULTS: Patients with HBV infection (n = 191) were assigned to groups by phase: Immune tolerance (IT) = 55, Immune clearance (IC) = 67, Low-replicative (LR) = 49, and HBeAg-negative hepatitis (ENH) = 20. Of the patients (male, 112; female, 79) enrolled, 122 were HBeAg-positive and 69 were HBeAg-negative. The median age was 33 years (range: 18-78 years). PC and BCP mutation detection rates were 84.82% (162/191) and 96.86% (185/191), respectively. In five HBeAg-negative cases, we detected double mutation G1896A/G1899A. The logarithm value of PC mutant quantities (log10 PC) significantly differed in IT, IC, and LR phases, as well as in the ENH phase (F = 49.350, P < 0.001). The logarithm value of BCP mutant quantities (log10 BCP) also differed during the four phases (F = 25.530, P < 0.001). Log10 PC and log10 BCP values were high in the IT and IC phases, decreased in the LR phase, and increased in the ENH phase, although the absolute value at this point remained lower than that in the IT and IC phases. PC mutant quantity per total viral load (PC%) and BCP mutant quantity per total viral load (BCP%) differed between phases (F = 20.040, P < 0.001; F = 10.830, P < 0.001), with PC% and BCP% gradually increasing in successive phases. HBeAg titers negatively correlated with PC% (Spearman’s rho = -0.354, P < 0.001) and BCP% (Spearman’s rho = -0.395, P < 0.001). The negative correlation between PC% and HBeAg status was significant (B = -5.281, P = 0.001), but there was no such correlation between BCP% and HBeAg status (B = -0.523, P = 0.552).
CONCLUSION: PC/BCP mutants become predominant in a dynamic and continuous process. Log10 PC, log10 BCP, PC% and BCP% might be combined to evaluate disease progression. PC% determines HBeAg status.
Precore mutant; Basal core promoter mutant; Hepatitis B virus; Quantification; Hepatitis B early antigen titers
Objectives. We investigated the action of triptolide in rats with adriamycin-induced nephropathy and evaluated the possible mechanisms underlying its protective effect against podocyte injury. Methods. In total, 30 healthy male Sprague-Dawley rats were randomized into three groups (normal group, model group, and triptolide group). On days 7, 28, 42, and 56, 24 h urine samples were collected. All rats were sacrificed on day 56, and their blood and renal tissues were collected for determination of biochemical and molecular biological parameters. Expression of miRNAs in the renal cortex was analyzed by a biochip assay and RT-PCR was used to confirm observed differences in miRNA levels. Results. Triptolide decreased proteinuria, improved renal function without apparent adverse effects on the liver, and alleviated renal pathological lesions. Triptolide also elevated the nephrin protein level. Furthermore, levels of miR-344b-3p and miR-30b-3p were elevated in rats with adriamycin-induced nephropathy, while triptolide treatment reversed the increase in the expression of these two miRNAs. Conclusions. These results suggest that triptolide may attenuate podocyte injury in rats with adriamycin-induced nephropathy by regulating expression of miRNA-344b-3p and miRNA-30b-3p.
To retrospectively identify the individual risk factors for the urethrocutaneous fistula (UCF) in pediatric patients after hypospadias repair (HR) with onlay island flap urethroplasty.
A total of 167 patients who underwent primary HR at Nanjing Medical University Affiliated Children Hospital from January 2009 to December 2012 were enrolled. Clinical data including the patient’ age at HR, hypospadias type and urethral defect length were documented.
Among 167 patients, 12.6% patients (n = 21) developed UCF after HR. Postoperative UCF occurred in 3.9% (3/76) cases at age of 0–2 years, 14.3% (9/63) at 2–4 years, 20.0% (2/10) at 4–6 years and 38.9% (7/18) at 6–12 years. The incidences of UCF were 12.0% (3/25), 11.4% (5/132) and 30.0% (3/10) for distal, middle and proximal types of hypospadias. As to the urethral defect length, the incidences of UCF were 8.2% (5/61) in patients with a length of ≤ 2 cm, 12.8% (9/70) in 2-3 cm, 22.6% (7/31) in 3–4 cm and 0% (0/5) in above 4 cm. Older age at HR was significantly associated with the high incidence of UCF formation (P = 0.004), while the hypospadias type and urethral defect length did not affect it (P = 0.264 and P = 0.312, respectively).
The patient’ age at HR was a risk factor for the UCF formation after HR, and treatment of HR within two years old might be with the least incidence of UCF.
Hypospadias; Urethrocutaneous fistula; Risk factors; Onlay island flap
A popular, if not centric, approach to the study of an event is to first consider that of the simplest cause. When dissecting the underlying mechanisms governing idiopathic diseases, this generally takes the form of an ab initio genetic approach. To date, this genetic ‘smoking gun’ has remained elusive in diabetes mellitus and for many affected by neurodegenerative diseases. With no single gene, or even subset of genes, conclusively causative in all cases, other approaches to the etiology and treatment of these diseases seem reasonable, including the correlation of a systems’ predisposed sensitivity to particular influence. In the cases of diabetes mellitus and neurodegenerative diseases, overlapping themes of mitochondrial influence or dysfunction and iron dyshomeostasis are apparent and relatively consistent. This mini-review discusses the influence of mitochondrial function and iron homeostasis on diabetes mellitus and neurodegenerative disease, namely Alzheimer’s disease. Also discussed is the incidence of diabetes accompanied by neuropathy and neurodegeneration along with neurodegenerative disorders prone to development of diabetes. Mouse models containing multiple facets of this overlap are also described alongside current molecular trends attributed to both diseases. As a way of approaching the idiopathic and complex nature of these diseases we are proposing the consideration of a MIND (mitochondria, iron, neurodegeneration, and diabetes) paradigm in which systemic metabolic influence, iron homeostasis, and respective genetic backgrounds play a central role in the development of disease.
Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time -consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process.
We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation.
The external predictivity of %F values was poor (R2=0.28, n=995, MAE=24), but was improved (R2=0.40, n=362, MAE=21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F<50% as “low”, %F≥50% as ‘high”) and developing category QSAR models resulted in an external accuracy of 76%.
In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models.
oral bioavailability; intestinal membrane transporter; QSAR; drugs
To develop accurate in silico predictors of Plasma Protein Binding (PPB).
Experimental PPB data were compiled for over 1,200 compounds. Two endpoints have been considered: (1) fraction bound (%PPB); and (2) the logarithm of a pseudo binding constant (lnKa) derived from %PPB. The latter metric was employed because it reflects the PPB thermodynamics and the distribution of the transformed data is closer to normal. Quantitative Structure-Activity Relationship (QSAR) models were built with Dragon descriptors and three statistical methods.
Five-fold external validation procedure resulted in models with the prediction accuracy (R2) of 0.67±0.04 and 0.66±0.04, respectively, and the mean absolute error (MAE) of 15.3±0.2% and 13.6±0.2%, respectively. Models were validated with two external datasets: 173 compounds from DrugBank, and 236 chemicals from the US EPA ToxCast project. Models built with lnKa were significantly more accurate (MAE of 6.2–10.7%) than those built with %PPB (MAE of 11.9–17.6%) for highly bound compounds both for the training and the external sets.
The pseudo binding constant (lnKa) is more appropriate for characterizing PPB binding than conventional %PPB. Validated QSAR models developed herein can be applied as reliable tools in early drug development and in chemical risk assessment.
machine learning; %PPB; drug fraction bound; ADMET; pharmacokinetics
MRI for in vivo stem cell tracking remains controversial. Here we tested the hypothesis that MRI can track the long-term fate of the superparamagnetic iron oxide (SPIO) nanoparticles labelled mesenchymal stem cells (MSCs) following intramyocardially injection in AMI rats. MSCs (1 × 106) from male rats doubly labeled with SPIO and DAPI were injected 2 weeks after myocardial infarction. The control group received cell-free media injection. In vivo serial MRI was performed at 24 hours before cell delivery (baseline), 3 days, 1, 2, and 4 weeks after cell delivery, respectively. Serial follow-up MRI demonstrated large persistent intramyocardial signal-voids representing SPIO during the follow-up of 4 weeks, and MSCs did not moderate the left ventricular dysfunction. The TUNEL analysis confirmed that MSCs engrafted underwent apoptosis. The histopathological studies revealed that the site of cell injection was infiltrated by inflammatory cells progressively and the iron-positive cells were macrophages identified by CD68 staining, but very few or no DAPI-positive stem cells at 4 weeks after cells transplantation. The presence of engrafted cells was confirmed by real-time PCR, which showed that the amount of Y-chromosome-specific SRY gene was consistent with the results. MRI may not reliably track the long-term fate of SPIO-labeled MSCs engraftment in heart.
Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.
Quantitative Structure Activity Relationship (QSAR) modeling; toxicogenomics; biological descriptors; hepatotoxicity
Regeneration capacity declines with age, but why juvenile organisms show enhanced tissue repair remains unexplained. Lin28a, a highly-conserved RNA binding protein expressed during embryogenesis, plays roles in development, pluripotency and metabolism. To determine if Lin28a might influence tissue repair in adults, we engineered the reactivation of Lin28a expression in several models of tissue injury. Lin28a reactivation improved hair regrowth by promoting anagen in hair follicles, and accelerated regrowth of cartilage, bone and mesenchyme after ear and digit injuries. Lin28a inhibits let-7 microRNA biogenesis; however let-7 repression was necessary but insufficient to enhance repair. Lin28a bound to and enhanced the translation of mRNAs for several metabolic enzymes, thereby increasing glycolysis and oxidative phosphorylation (OxPhos). Lin28a-mediated enhancement of tissue repair was negated by OxPhos inhibition, whereas a pharmacologically-induced increase in OxPhos enhanced repair. Thus, Lin28a enhances tissue repair in some adult tissues by reprogramming cellular bioenergetics.
Pathogen-host protein-protein interaction (PPI) plays an important role in revealing the underlying pathogenesis of viruses and bacteria. The need of rapidly mapping proteome-wide pathogen-host interactome opens avenues for and imposes burdens on computational modeling. For Salmonella typhimurium, only 62 interactions with human proteins are reported to date, and the computational modeling based on such a small training data is prone to yield model overfitting. In this work, we propose a multi-instance transfer learning method to reconstruct the proteome-wide Salmonella-human PPI networks, wherein the training data is augmented by homolog knowledge transfer in the form of independent homolog instances. We use AdaBoost instance reweighting to counteract the noise from homolog instances, and deliberately design three experimental settings to validate the assumption that the homolog instances are effective to address the problems of data scarcity and data unavailability. The experimental results show that the proposed method outperforms the existing models and some predictions are validated by the findings from recent literature. Lastly, we conduct gene ontology based clustering analysis of the predicted networks to provide insights into the pathogenesis of Salmonella.
Identification of Endocrine Disrupting Chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause Estrogen Receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous Quantitative Structure-Activity Relationships (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R2=0.71, STL R2=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R2=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation.
Endocrine Disrupting Chemicals; Estrogen Receptor; Quantitative Structure-Activity Relationships modeling; Multi-Task Learning; Docking; Virtual Screening
Objective: To explore the correlation between the plasma renalase level of coronary artery disease (CAD) patients and the degree of coronary artery stenosis.
Methods: A total of 180 patients who received coronary angiography in our hospitals from August 2013 to October 2013 were selected as the CAD group, of which 164 were finally diagnosed as CAD. Another 140 healthy subjects were selected as the control group. The plasma renalase levels of the two groups were detected by ELISA to analyze CA-induced changes and to clarify the correlations with the number of branches with coronary artery stenosis and Syntax scores.
Results: The plasma renalase level of the CAD group was significantly lower than that of the control group (P<0.05). The plasma renalase levels of the multi-branch and two-branch stenosis subgroups were significantly lower than that of the subgroup with normal coronary angiography outcomes (P<0.05), while the levels of the single-branch stenosis and normal subgroups were similar (P>0.05). Besides, the plasma renalase level of the low-risk subgroup was significantly higher than those of the medium-risk and high-risk subgroups (P<0.05), and the level of the medium-risk subgroup was significantly higher than that of the high-risk subgroup (P<0.05). Multivariate Logistic regression analysis showed that renalase level was the risk factor of CAD (OR=1.12, 95%CI: 1.03-3.34).
Conclusion: Plasma renalase level was correlated with CAD, the changes of which may reflect the degree of coronary artery stenosis. Therefore, plasma renalase level can be used to indicate the progression of CAD.
Coronary artery disease; Coronary artery stenosis; Renalase; Syntax score
Human T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported.
In this work we develop Multi-instance Adaboost method to conduct homolog knowledge transfer for computationally reconstructing proteome-wide HTLV-human PPI networks. In this method, the homolog knowledge in the form of gene ontology (GO) is treated as auxiliary homolog instance to address the problems of data scarcity and data unavailability, while the potential negative knowledge transfer is automatically attenuated by AdaBoost instance reweighting. The cross validation experiments show that the homolog knowledge transfer in the form of independent homolog instances can effectively enrich the feature information and substitute for the missing GO information. Moreover, the independent tests show that the method can validate 70.3% of the recently curated interactions, significantly exceeding the 2.1% recognition rate by the HT-Y2H experiment. We have used the method to reconstruct the proteome-wide HTLV-human PPI networks and further conducted gene ontology based clustering of the predicted networks for further biomedical research. The gene ontology based clustering analysis of the predictions provides much biological insight into the pathogenesis of HTLV retroviruses.
The Multi-instance AdaBoost method can effectively address the problems of data scarcity and data unavailability for the proteome-wide HTLV-human PPI interaction networks reconstruction. The gene ontology based clustering analysis of the predictions reveals some important signaling pathways and biological modules that HTLV retroviruses are likely to target.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2105-15-245) contains supplementary material, which is available to authorized users.
Abnormal histone acetylation occurs during neuropathic pain through an epigenetic mechanism. Silent information regulator 1 (sir2 or SIRT1), a NAD-dependent deacetylase, plays complex systemic roles in a variety of processes through deacetylating acetylated histone and other specific substrates. But the role of SIRT1 in neuropathic pain is not well established yet. The present study was intended to detect SIRT1 content and activity, nicotinamide (NAM) and nicotinamide adenine dinucleotide (NAD) in the spinal cord using immunoblotting or mass spectroscopy over time in mice following chronic constriction injury (CCI) or sham surgery. In addition, the effect of intrathecal injection of NAD or resveratrol on thermal hyperalgesia and mechanical allodynia was evaluated in CCI mice. Finally, we investigated whether SIRT1 inhibitor EX-527 could reverse the anti-nociceptive effect of NAD or resveratrol. It was found that spinal SIRT1 expression, deacetylase activity and NAD/NAM decreased significantly 1, 3, 7, 14 and 21 days after CCI surgery as compared with sham group. In addition, daily intrathecal injection of 5 µl 800 mM NAD 1 h before and 1 day after CCI surgery or single intrathecal injection of 5 µl 90 mM resveratrol 1 h before CCI surgery produced a transient inhibitory effect on thermal hyperalgesia and mechanical allodynia in CCI mice. Finally, an intrathecal injection of 5 µl 1.2 mM EX-527 1 h before NAD or resveratrol administration reversed the anti-nociceptive effect of NAD or resveratrol. These data indicate that the reduction in SIRT1 deacetylase activity may be a factor contributing to the development of neuropathic pain in CCI mice. Our findings suggest that the enhancement of spinal NAD/NAM and/or SIRT1 activity may be a potentially promising strategy for the prevention or treatment of neuropathic pain.
In vitro bioassays have been developed and are currently being evaluated as potential alternatives to traditional animal toxicity models. Already, the progress of high throughput screening techniques has resulted in an enormous amount of publicly available bioassay data having been generated for a large collection of compounds. When a compound is tested using a collection of various bioassays, all the testing results can be considered as providing a unique bio-profile for this compound, which records the responses induced when the compound interacts with different cellular systems or biological targets. Profiling compounds of environmental or pharmaceutical interest using useful toxicity bioassay data is a promising method to study complex animal toxicity. In this study, we developed an automatic virtual profiling tool to evaluate potential animal toxicants. First, we automatically acquired all PubChem bioassay data for a set of 4,841 compounds with publicly available rat acute toxicity results. Next, we developed a scoring system to evaluate the relevance between these extracted bioassays and animal acute toxicity. Finally, the top ranked bioassays were selected to profile the compounds of interest. The resulting response profiles proved to be useful to prioritize untested compounds for their animal toxicity potentials and form a potential in vitro toxicity testing panel. The protocol developed in this study could be combined with structure-activity approaches and used to explore additional publicly available bioassay datasets for modeling a broader range of animal toxicities.
Fistular onion stalk is used as a traditional herbal medicine, and its extract exhibits certain beneficial effects on cardiovascular disease. In this study, the effects of fistular onion stalk extract on the pathological features, circulating inflammatory cytokines, local renin-angiotensin-aldosterone system (RAAS) and signaling pathway activities were examined using an in vivo model of atherosclerosis. Atherosclerosis of the aorta was induced by loading Sprague Dawley rats with a high-fat diet and vitamin D2. Fistular onion stalk extract administration began five weeks after the induction of atherosclerosis and continued for 12 weeks. Rats treated with fistular onion stalk extract showed a significant reduction in the pathological region compared with the vehicle-treated controls. Inhibition of atherosclerosis was associated with preservation of the vascular wall and immune cell infiltration. The extract also reduced the levels of the local inflammatory cytokines interleukin (IL)-1β, IL-6, monocyte chemoattractant protein-1 and tumor necrosis factor-α. Furthermore, the extract downregulated the local activity of the RAAS. In addition, extract treatment inhibited several inflammatory signaling pathways by preventing phosphorylation, including the nuclear factor κB, Janus kinase/signal transducers and activators of transcription and mitogen-activated protein kinase pathways. These data indicate that fistular onion stalk extract may be useful for the attenuation of atherosclerosis, and the mechanism includes the regulation of the local inflammatory response.
fistular onion stalk extract; atherosclerosis; inflammatory cytokine; renin-angiotensin-aldosterone system
The treatment of cancer draws interest from researchers worldwide. Of the different extracts from traditional Chinese medicines, Tubeimoside 1 (TBMS 1) is regarded as an effective treatment for cancer. To determine the mechanism of TBMS 1, the shape/pattern of HepG2 cells based on the microscopic imaging technology was determined to analyze experimental results; then the fluorescent spectra method was designed to investigate whether TBMS 1 affected HepG2 cells. A three-dimensional (3D) fluorescent spectra sweep was performed to determine the characteristic wave peak of HepG2 cells. A 2D fluorescent spectra method was then used to show the florescence change in HepG2 cells following treatment with TBMS 1. Finally, flow cytometry was employed to analyze the cell cycle of HepG2 cells. It was shown that TBMS 1 accelerated the death of HepG2 cells and had a strong dose- and time-dependent growth inhibitory effect on HepG2 cells, especially at the G2/M phase. These results indicate that the fluorescent spectra method is a promising substitute for flow cytometry as it is rapid and cost-effective in HepG2 cells.
Objectives. This study aimed to identify the active compounds in Oldenlandia diffusa (OD) decoction and the compounds absorbed into plasma, and to determine whether the absorbed compounds derived from OD exerted any anti-inflammatory effects in rats with collagen induced arthritis (CIA). Methods. The UPLC-PDA (Ultra Performance Liquid Chromatography Photo-Diode Array) method was applied to identify the active compounds both in the decoction and rat plasma. The absorbable compound was administered to the CIA rats, and the effects were dynamically observed. X-ray films of the joints and HE stain of synovial tissues were analyzed. The levels of IL-1β and TNF-α in the rats from each group were measured by means of ELISA. The absorbed compound in the plasma of CIA rats was identified as ferulic acid (FA), following OD decoction administration. Two weeks after the administration of FA solution or OD decoction, the general conditions improved compared to the model group. The anti-inflammatory effect of FA was inferior to that of the OD decoction (P < 0.05), based on a comparison of IL-1β TNF-α levels. FA from the OD decoction was absorbed into the body of CIA rats, where it elicited anti-inflammatory responses in rats with CIA. Conclusions. These results suggest that FA is the bioactive compound in OD decoction, and FA exerts its effects through anti-inflammatory pathways.
It is well recognized that PIAS1, a SUMO (small ubiquitin-like modifier) E3 ligase, modulates such cellular processes as cell proliferation, DNA damage responses, and inflammation responses. Recent studies have shown that PIAS1 also plays a part in cell differentiation. However, the role of PIAS1 in adipocyte differentiation remains unknown. CCAAT/enhancer-binding protein β (C/EBPβ), a major regulator of adipogenesis, is a target of SUMOylation, but the E3 ligase responsible for the SUMOylation of C/EBPβ has not been identified. The present study showed that PIAS1 functions as a SUMO E3 ligase of C/EBPβ to regulate adipogenesis. PIAS1 expression was significantly and transiently induced on day 4 of 3T3-L1 adipocyte differentiation, when C/EBPβ began to decline. PIAS1 was found to interact with C/EBPβ through the SAP (scaffold attachment factor A/B/acinus/PIAS) domain and SUMOylate it, leading to increased ubiquitination and degradation of C/EBPβ. C/EBPβ became more stable when PIAS1 was silenced by RNA interference (RNAi). Moreover, adipogenesis was inhibited by overexpression of wild-type PIAS1 and promoted by knockdown of PIAS1. The mutational study indicated that the catalytic activity of SUMO E3 ligase was required for PIAS1 to restrain adipogenesis. Importantly, the inhibitory effect of PIAS1 overexpression on adipogenesis was rescued by overexpressed C/EBPβ. Thus, PIAS1 could play a dynamic role in adipogenesis by promoting the SUMOylation of C/EBPβ.
Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles.
Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1.
Results & Conclusions
QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71–100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.
membrane transport proteins; ADMET; drug transport; permeability; efflux
Quantitative structure–activity relationship (QSAR) models have been developed for a dataset of 3133 compounds defined as either active or inactive against P. falciparum. Since the dataset was strongly biased towards inactive compounds, different sampling approaches were employed to balance the ratio of actives vs. inactives, and models were rigorously validated using both internal and external validation approaches. The balanced accuracy for assessing the antimalarial activities of 70 external compounds was between 87% and 100% depending on the approach used to balance the dataset. Virtual screening of the ChemBridge database using QSAR models identified 176 putative antimalarial compounds that were submitted for experimental validation, along with 42 putative inactives as negative controls. Twenty five (14.2%) computational hits were found to have antimalarial activities with minimal cytotoxicity to mammalian cells, while all 42 putative inactives were confirmed experimentally. Structural inspection of confirmed active hits revealed novel chemical scaffolds, which could be employed as starting points to discover novel antimalarial agents.
Antimalarial activity; quantitative structure–activity relationships; virtual screening; experimental confirmation
Autophagy is a highly conserved self-digestion pathway involved in various physiological and pathophysiological processes. Recent studies have implicated a pivotal role of autophagy in adipocyte differentiation, but the molecular mechanism for its role and how it is regulated during this process are not clear. Here, we show that CCAAT /enhancer-binding protein β (C/EBPβ), an important adipogenic factor, is required for the activation of autophagy during 3T3-L1 adipocyte differentiation. An autophagy-related gene, Atg4b, is identified as a de novo target gene of C/EBPβ and is shown to play an important role in 3T3-L1 adipocyte differentiation. Furthermore, autophagy is required for the degradation of Klf2 and Klf3, two negative regulators of adipocyte differentiation, which is mediated by the adaptor protein p62/SQSTM1. Importantly, the regulation of autophagy by C/EBPβ and the role of autophagy in Klf2/3 degradation and in adipogenesis are further confirmed in mouse models. Our data describe a novel function of C/EBPβ in regulating autophagy and reveal the mechanism of autophagy during adipocyte differentiation. These new insights into the molecular mechanism of adipose tissue development provide a functional pathway with therapeutic potential against obesity and its related metabolic disorders.
LIN28A/B are RNA binding proteins implicated by genetic association studies in human growth and glucose metabolism. Mice with ectopic over-expression of Lin28a have shown related phenotypes. Here we describe the first comprehensive analysis of the physiologic consequences of Lin28a and Lin28b deficiency in knockout (KO) mice. Lin28a/b-deficiency led to dwarfism starting at different ages, and compound gene deletions showed a cumulative dosage effect on organismal growth. Conditional gene deletion at specific developmental stages revealed that fetal but neither neonatal nor adult deficiency resulted in growth defects and aberrations in glucose metabolism. Tissue-specific KO mice implicated skeletal muscle-deficiency in the abnormal programming of adult growth and metabolism. The effects of Lin28b KO can be rescued by Tsc1 haplo-insufficiency in skeletal muscles. Our data implicate fetal expression of Lin28a/b in the regulation of life-long effects on metabolism and growth, and demonstrate that fetal Lin28b acts at least in part via mTORC1 signaling.
Lin28a; Lin28b; dwarfism; growth; glucose metabolism; diabetes; let-7; mTOR
We introduce the Interactive Decision Committee method for classification when high-dimensional feature variables are grouped into feature categories. The proposed method uses the interactive relationships among feature categories to build base classifiers which are combined using decision committees. A two-stage or a single-stage 5-fold cross-validation technique is utilized to decide the total number of base classifiers to be combined. The proposed procedure is useful for classifying biochemicals on the basis of toxicity activity, where the feature space consists of chemical descriptors and the responses are binary indicators of toxicity activity. Each descriptor belongs to at least one descriptor category. The support vector machine, the random forests, and the tree-based AdaBoost algorithms are utilized as classifier inducers. Forward selection is used to select the best combinations of the base classifiers given the number of base classifiers. Simulation studies demonstrate that the proposed method outperforms a single large, unaggregated classifier in the presence of interactive feature category information. We applied the proposed method to two toxicity data sets associated with chemical compounds. For these data sets, the proposed method improved classification performance for the majority of outcomes compared to a single large, unaggregated classifier.
Chemical toxicity; Decision committee method; Ensemble; Ensemble feature selection; QSAR modeling; Statistical learning