Human Epithelial-2 Research Articles

Evaluation of the effect of cannabidiol on the THLE-2 liver cell line exposed to lead

Environmental and soil pollution increase the likelihood of human exposure to toxic metals. Therefore, there is a need for new methods and substances to protect individuals against the harmful effects caused by toxic metals. The study is the first to aim at determining the protective effect of cannabidiol (CBD) against oxidative stress and inflammation induced by toxic metal exposure in Transformed Human Liver Epithelial-2 (THLE-2) cell lines representing healthy liver cells. The IC50 value was determined by exposing THLE-2 human liver healthy cell line to different molarities of lead (Pb) using the XTT kit. The protective efficacy of CBD was assessed by adding 5 μM CBD in addition to the Pb doses determined at IC50 levels to the Pb groups created in cell lines. The levels of GSH, MDA, MPO, CAT, TNF-α, IL-1β, and IL-6 in cell lines were determined using ELISA kits. The inhibition of toxic metal entry into the cells by CBD was assessed through ICP-MS analysis. The IC50 value for Pb was determined as 10 μM in 2D cell lines and 25 μM in 3D cell lines. It was observed that the application of 5 μM concentration of CBD, along with the determined IC50 doses for Pb, increased the cell proliferation rate. Furthermore, the decrease in GSH and CAT levels and the increase in MDA, MPO, TNF-α, IL-1β, and IL-6 levels observed in cell lines treated only with Pb were reversed with the application of CBD. The ICP-MS analysis revealed that CBD reduced the cellular uptake of Pb. The reversal of oxidative stress and inflammation induced by Pb, the increase in cell proliferation, and the reduction in the cellular uptake of toxic metals by CBD can be considered as strong evidence for the protective use of CBD in Pb exposures.

New proapoptotic chemotherapeutic agents based on the quinolone-3-carboxamide scaffold acting by VEGFR-2 inhibition

In the current study, we designed and synthesized a series of new quinoline derivatives 10a-p as antiproliferative agents targeting cancer through inhibition of VEGFR-2. Preliminary molecular docking to assess the interactions of the designed derivatives with the binding site of VEGFR-2 (PDB code: 4ASD) displayed binding poses and interactions comparable to sorafenib. The synthesized compounds exhibited VEGFR-2 inhibitory activity with IC50 ranging from 36 nM to 2.23 μM compared to sorafenib (IC50 = 45 nM), where derivative 10i was the most potent. Additionally, the synthesized derivatives were evaluated in vitro for their cytotoxic activity against HepG2 cancer cell line. Seven compounds 10a, 10c, 10d, 10e, 10i, 10n and 10o (IC50 = 4.60, 4.14, 1.07, 0.88, 1.60, 2.88 and 2.76 μM respectively) displayed better antiproliferative activity than sorafenib (IC50 = 8.38 μM). Compound 10i was tested against Transformed Human Liver Epithelial-2 normal cell line (THLE-2) to evaluate its selective cytotoxicity. Furthermore, 10i, as a potent representative of the series, was assayed for its apoptotic activity and cell cycle kinetics’ influence on HepG2, its effects on the gene expression of VEGFR-2, and protein expression of the apoptotic markers Caspase-7 and Bax. Compound 10i proved to have a potential role in apoptosis by causing significant increase in the early and late apoptotic quartiles, a remarkable activity in elevating the relative protein expression of Bax and Caspase-7 and a significant reduction of VEGFR-2 gene expression. Collectively, the obtained results indicate that compound 10i has a promising potential as a lead compound for the development of new anticancer agents.

Anti-cancer and antimicrobial potential of five soil Streptomycetes: a metabolomics-based study.

Lack of new anti-cancer and anti-infective agents directed the pharmaceutical research to natural products' discovery especially from actinomycetes as one of the major sources of bioactive compounds. Metabolomics- and dereplication-guided approach has been used successfully in chemical profiling of bioactive actinomycetes. We aimed to study the metabolomic profile of five bioactive actinomycetes to investigate the interesting metabolites responsible for their antimicrobial and anti-cancer activities. Three actinomycetes, namely, Streptomyces sp. SH8, SH10 and SH13, were found to exhibit broad spectrum of antimicrobial activities, whereas isolate SH4 showed the broadest antimicrobial activity against all tested strains. In addition, isolates SH8, SH10 and SH12 displayed potent cytotoxicity against the breast cancer cell line Michigan Cancer Foundation-7 (MCF-7), whereas isolates SH4 and SH12 exhibited potent anti-cancer activity against the hepatoma cell line hepatoma G2 (HepG2) compared with their weak inhibitory properties on the normal breast cells MCF-10A and normal liver cells transformed human liver epithelial-2 (THLE2), respectively. All bioactive isolates were molecularly identified as Streptomyces sp. via 16S rRNA gene sequencing. Our actinobacterial dereplication analysis revealed putative identification of several bioactive metabolites including tetracycline, oxytetracycline and a macrolide antibiotic, novamethymycin. Together, chemical profiling of bioactive Streptomycetes via dereplication and metabolomics helped in assigning their unique metabolites and predicting the bioactive compounds instigating their diverse bioactivities.

Inhibition of microRNA-297 alleviates THLE-2 cell injury induced by hypoxia/reoxygenation by inhibiting NLRP3 inflammasome activation via sirtuin 3.

It has been acknowledged that microRNAs (miRNAs/miRs) assume a critical role in hypoxia/reoxygenation (H/R) - induced hepatocyte injury. Therefore, cell experiments were performed in this study to investigate the mechanism of miR-297 in H/R-induced hepatocyte injury with the involvement of sirtuin 3 (SIRT3) and NOD-like receptor pyrin domain containing 3 (NLRP3). Initially, transformed human liver epithelial-2 (THLE-2) cells were utilized for H/R challenge. After miR-297 antagomir and NLRP3 adenovirus vector delivery, THLE-2 cell proliferation and apoptosis were measured by MTT, EdU, and TUNEL assays, respectively. Enzyme-linked immunosorbent assay was conducted to evaluate the levels of apoptosis-related indicators (Bax and Bcl-2) and inflammation-related indicators (interleukin 6 (IL-6) and IL-10), Western blot analysis to detect NLRP3, and cleaved caspase-1 expression. The binding relation between miR-297 and SIRT3 was examined using dual-luciferase assay. The results showed that miR-297 antagomir repressed the apoptosis and inflammation induced by H/R treatment in THLE-2 cells. Mechanistically, miR-297 antagomir diminished the extent of IκBα and nuclear factor-kappa B (NF-κB) phosphorylation and NLRP3 activation in H/R-induced THLE-2 cells by targeting SIRT3. Furthermore, NLRP3 overexpression normalized the promoting effects of miR-297 antagomir on proliferation and its inhibitory effects on apoptosis and inflammation in H/R-induced THLE-2 cells. In summary, our results elucidated that miR-297 antagomir repressed H/R-induced THLE-2 cell injury via SIRT3 promotion and NLRP3 inactivation.

Evaluation of In Vitro Models for Pyrrolizidine Alkaloid‐Induced Hepatocellular Injury

ObjectivePyrrolizidine alkaloids (PAs) are toxic chemicals found in many herbal medicines, dietary supplements and even food sources such as honey, bread, and tea. Cases of drug‐induced liver injury due to herbal and dietary supplements are relatively common in Asia and are increasing in the United States. The literature suggests that PAs containing macrocyclic structure are more toxic than those with smaller sidechains. The goal of this project was to evaluate two PAs with macrocyclic structure (monocrotaline and retrorsine) and one PA will a smaller sidechain (retronecine) for their potential to elicit toxicity across two in vitro model systems.MethodsPA toxicity was measured by determining the concentration at which 50% of the cell population was killed (LC50). PAs were dissolved in DMSO and PA‐induced toxicity was measured after 48 hours in two in vitro models: TAMH (transforming growth factor‐α transgenic mouse hepatocytes) and THLE‐2 (transformed human liver epithelial‐2 cells). Statistical analysis of cell viability data was performed using SPSS (Probit regression) to obtain LC50 values.ResultsA decrease in cell viability was observed as PA concentration increased. Interestingly, both cell lines were relatively resistant to PA‐induced toxicity. In many cases only predicted LC50 values for each compound could be generated and are indicated by an asterisk (7.0mM*, 11mM*, and 30.mM* in TAMH for monocrotaline, retrorsine and retronecine, respectively; and 2.2mM, 3.2mM, and >100mM* in THLE‐2 for monocrotaline, retrorsine and retronecine, respectively. Based on the toxicologic parameters, monocrotaline and retrorsine were more toxic (lower LC50 values) than retronecine.ConclusionsWhile PAs have been associated with hepatotoxicity, limited toxicity was seen in both the TAMH and THLE‐2 cell lines. Predicted LC50 values were higher than expected, suggesting TAMH and THLE‐2 cells are not good models for studying PA‐induced liver toxicity. Retronecine, which contains a smaller sidechain, was found to be less toxic than the larger compounds. Future work is currently underway to identify biomarkers responsible for the observed resistance to PA‐induced toxicity as potential targets for preventing hepatocellular injury.Support or Funding InformationThis work was supported by the Pacific University Research Incentive Grant Program and an ASPET Summer Undergraduate Research Fellowship (SURF) award at Pacific University.

Synthesis and biological evaluation of novel pyrimidine-5-carbonitriles featuring morpholine moiety as antitumor agents.

Aim: Design and synthesis of novel morpholinopyrimidine-5-carbonitriles as antitumor agents. Materials & methods: New series of morpholinopyrimidine-5-carbonitriles have been synthesized. 19derivatives (3b, 4a, 5-6, 9-12, 13a-e, 14a-c and 15-17) were evaluated for their invitro antitumor activity by the National Cancer Institute (NCI; MD, USA). Moreover, compound 13e was evaluated against PI3K (α, β and δ) and the mechanism of its cytotoxic activity on leukemia SR was studied. Results: Compound 13e possessed remarkable broad spectrum antitumor activity with GI50 (median growth inhibition) and TGI (total growth inhibition) values of 6.15 and 28.66μM, respectively, caused cell cycle arrest at G2-M phase and significant increase in the percentage of annexin V-FITC - positive apoptotic cells, also increased the level of active caspase-3. Moreover, 13e revealed good safety profile against transformed human liver epithelial-2 (THLE2).

Anti-hepatitis-C virus activity and QSAR study of certain thiazolidinone and thiazolotriazine derivatives as potential NS5B polymerase inhibitors

The present study reports on evaluation of anti-HCV activity and QSAR of certain arylidenethiazolidinone derivatives as potential inhibitors of HCV-NS5B polymerase. The pursued compounds involving, 5-aryliden-3-arylacetamidothiazolidin-2,4-diones 4–6(a–f), 5-arylidine-2-(N-arylacetamido)-iminothiazolidin-4-one (10) and their rigid counterparts 5-arylidinethiazolotriazines 13–15(a–f), were synthesized and their structures confirmed by spectral and elemental analyses. The results of NS5B polymerase inhibition assay revealed compound 4e, as the most active inhibitor (IC50 = 0.035 μM), which is four folds greater than that of the reference agent, VCH-759, (IC50 = 0.14 μM). Meanwhile, compounds 4b, 4c, 5a, and 5c, and 13b, 14e and 15c displayed equipotency to 2 folds higher activity than VCH-759 (IC50 values: 0.085, 0.14, 0.14, 0.10, 0.12, 0.09 and 0.07 μM, respectively). Assessment of the anti-HCV activity (GT1a) using human hepatoma cell line (Huh-7.5) illustrates superior activity of 4e (EC50 = 3.80 μM) relative to VCH-759 (EC50 = 5.29 μM). Cytotoxicity evaluation on, Transformed normal cell lines (Human Liver Epithelial-2, THLE-2 and Proximal Tubular Epithelial, RPTEC/TERT1), demonstrate enhanced safety profile of 4e (CC50 = 102.77, 161.37 μM, respectively) compared to VCH-759 (CC50 = 61.83, 81.28 μM, respectively). Molecular docking of the synthesized derivatives to NS5B polymerase allosteric site (PDB: 2HWH) showed similar binding modes to that of the co-crystallized ligand. Moreover, QSAR models were established for the studied thiazolidinones and thiazolotriazines to investigate the molecular characteristics contributing to the observed NS5B polymerase inhibition activity. The obtained results inspire further investigations of thiazolidinones and thiazolotriazine aiming at affording more potent, safe and orally active non-nucleoside NS5B polymerase inhibitors as anti-HCV drug candidates.

Deeply supervised full convolution network for HEp-2 specimen image segmentation

Human Epithelial-2 (HEp-2) cell images play an important role for the detection of antinuclear autoantibodies (ANA) in autoimmune diseases. Segmentation is the primary step for classification, further treatment and diagnosis. However, the staining patterns and scales of HEp-2 specimen images have different variances, which still make segmentation quite a challenging task. To solve it, we propose a novel deeply supervised full convolutional network (DSFCN) for robust segmentation of different HEp-2 cell images dataset. DSFCN is based on a very deep network, which integrates the dense deconvolution layer (DDL) and hierarchical supervision structure (HS). Specifically, The DDL uses the up-sampling to restore the high resolution of the original input images to replace the traditional deconvolution layer, and the hierarchical supervision is added to capture feature information of the shallow layers. The high-resolution predictive output is obtained by capturing local and global information between layers. Without relying on the prior knowledge and complex post-processing, DSFCN can be effectively trained in an end-to-end manner. The proposed method is trained and tested on the I3A-2014 public dataset, and the segmentation result demonstrates that the performance of our model outperforms other state-of-the-art methods.

In vivo antimalarial activity, toxicity, and phytochemical composition of total extracts from securidaca longepedunculata Fresen. (polygalaceae)

Introduction: Alternative antimalarial drugs are urgently required. Securidaca longepedunculata Fresen. (Polygalaceae) is a medicinal plant with a long history of use in African ethnomedicine to treat malaria and other illnesses. The efficacy, safety, and chemical composition of chloroform: methanol (1:1) and aqueous total extracts from the leaves, stem, and roots of S. longepedunculata were investigated. Methods: Adult Swiss female mice were infected with 107 erythrocytes parasitized with Plasmodium berghei (strain ANKA) on day 0 as a model of malaria. Effects of crude extracts at a dosage rate of 100 mg/kg of body weight on parasitemia were measured over a 4-day period. To evaluate acute toxicity, the mice were administered crude extracts by oral gavage at 50, 300, and 2000 mg/kg of body weight and observed over a 24-h period. Cytotoxic effects of crude extracts were measured using human epithelial-2 cells in a 96-well microtiter plate over a 24-h period. Results: Chloroform: methanol (1:1) and aqueous root extracts demonstrated significant chemosuppressive activities of 91.46% and 87.64%, respectively (P 2000 mg/kg body weight. Crude extracts contained alkaloids, anthraquinones, flavonoids, saponins, steroids, tannins, and triterpenoids. Conclusion: The findings from the current study validate ethnopharmacological use of the plant, while demonstrating its potential as a possible source of new lead molecules against malaria.

Stability, safety, and transcorneal mechanistic studies of ophthalmic lyophilized cyclosporine-loaded polymeric micelles.

IntroductionCyclosporine-A (CsA) is generally used as an immunosuppressant and is also prescribed for some ophthalmic applications such as vernal keratoconjunctivitis and dry eye. However, it is limited clinically due to its low aqueous solubility and ocular bioavailability.MethodsIn this work, lyophilized methoxy poly(ethylene glycol)-poly(lactide) (mPEG-PLA) polymer micelles were prepared for ophthalmic formulations as a promising nanocarrier for hydrophobic drugs like CsA. A mPEG-PLA diblock polymer was synthesized by ring opening polymerization and CsA was loaded into mPEG-PLA micelles by a simple film dispersion method. A uniform design of experiments was utilized to optimize the final formulation. The obtained formulation was characterized for diameter (57.0±3.2 nm), entrapment efficiency % (98.51±1.4), and in vitro release. Moreover, incorporating the stabilizer mPEG2000 could increase the in vitro stability of the lyophilized CsA-loaded mPEG-PLA micelles.ResultsResults showed a sustained release of CsA from the micelles. Drug concentration and time-dependent cytotoxicity of human corneal epithelial-2 cells was observed. Additionally, the transcorneal mechanism of mPEG-PLA micelles was studied and the results showed that the mPEG-PLA micelles mainly absorbed by a paracellular pathway via corneal epithelial cells.ConclusionTaken together, the results proved that this mPEG-PLA diblock polymer can be potentially used as a nanoscopic carrier to deliver hydrophobic drugs in a controlled manner to the ocular region and, thus, deserves further attention.

HEp-Net: a smaller and better deep-learning network for HEp-2 cell classification

Indirect immunofluorescence of Human Epithelial-2 (HEp-2) cells is a commonly used method for the diagnosis of autoimmune diseases. Traditional approach relies on specialists to observe HEp-2 slides via the fluorescence microscope, which suffers from a number of shortcomings like being subjective and labour intensive. In this paper, we proposed a deep-learning network, namely HEp-Net, to automatically classify HEp-2 cell images. The proposed HEp-Net uses multi-scale convolutional component to extract features from Hep-2 cell images and fuses the features extracted by shallow and deep layers for performance improvement. The proposed model is evaluated on publicly available I3A (Indirect Immunofluorescence Image Analysis) and MIVIA data-sets. Experimental result demonstrates that, compared to the state-of-the-art approaches, our proposed HEp-Net yields better performance with smaller network size.

A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer learning

Accurate Human Epithelial-2 (HEp-2) cell image classification plays an important role in the diagnosis of many autoimmune diseases and subsequent treatment. One of the key challenges is huge intra-class variations caused by inhomogeneous illumination. To address it, we propose a framework based on very deep supervised residual network (DSRN) to classify HEp-2 cell images. Specifically, we adopt a residual network of 50 layers (ResNet-50) that is substantially deep to extract rich and discriminative features. The deep supervision is imposed on the ResNet-based framework to further boost the classification performance by directly guiding the training of the lower and upper levels of the network. The proposed method is evaluated using two publicly available datasets (i.e., International Conference on Pattern Recognition (ICPR) 2012 and ICPR2016-Task1 cell classification contest datasets). Different from the previous deep learning models learned from scratch, a cross-modal transfer learning strategy is developed. Namely, we pretrain ICPR2012 dataset to fine-tune ICPR2016 dataset based on our DSRN model since both datasets are similar. Extensive experiments show that the proposed method delivers state-of-the-art performance and outperforms the traditional methods based on deep convolutional neural network (DCNN).

Automated evaluation of ANA under real-life conditions

IntroductionVisual evaluation of indirect immunofluorescence (IIF) on human epithelial-2 cells is the routine method for screening for antinuclear antibodies (ANA) in connective tissue diseases. Since visual IIF is time-consuming and...

HEp-2 Specimen Image Segmentation and Classification Using Very Deep Fully Convolutional Network.

Reliable identification of Human Epithelial-2 (HEp-2) cell patterns can facilitate the diagnosis of systemic autoimmune diseases. However, traditional approach requires experienced experts to manually recognize the cell patterns, which suffers from the inter-observer variability. In this paper, an automatic pattern recognition system using fully convolutional network (FCN) was proposed to simultaneously address the segmentation and classification problem of HEp-2 specimen images. The proposed system transforms the residual network (ResNet) to fully convolutional ResNet (FCRN) enabling the network to perform semantic segmentation task. A sand-clock shape residual module is proposed to effectively and economically improve the performance of FCRN. The publicly available I3A-2014 data set was used to train the FCRN model to classify HEp-2 specimen images into seven catalogs: homogeneous, speckled, nucleolar, centromere, golgi, nuclear membrane, and mitotic spindle. The proposed system achieves a mean class accuracy of 94.94% for leave-one-out tests, which outperforms the winner of ICPR 2014, i.e., 89.93%. At the same time, our model also achieves a segmentation accuracy of 89.03%, which is 19.05% higher than that of the benchmark approach, i.e., 69.98%.

HEp-2 Cell Image Classification With Deep Convolutional Neural Networks.

Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. In addition to describing the proposed classification framework, this paper elaborates several interesting observations and findings obtained by our investigation. They include the important factors that impact network design and training, the role of rotation-based data augmentation for cell images, the effectiveness of cell image masks for classification, and the adaptability of the CNN-based classification system across different datasets. Extensive experimental study is conducted to verify the above findings and compares the proposed framework with the well-established image classification models in the literature. The results on benchmark datasets demonstrate that 1) the proposed framework can effectively outperform existing models by properly applying data augmentation, 2) our CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014.

AB1238 A comparison between automated and manual methods for detection antinuclear autoantibodies on human epithelial-2 cells

BackgroundA variety of antinuclear antibodies (ANAs) are found in serum samples obtained from patients with rheumatic autoimmune diseases1. Identification of the corresponding antigens and measurement of the antibody titer are...

A novel automated indirect immunofluorescence autoantibody evaluation

Autoantibodies (AAb), especially antinuclear (ANAs) and anticytoplasmatic antibodies (ACyA), are essential diagnosing markers for several autoimmune diseases. The current gold standard method for ANA detection is manual indirect immunofluorescence (IIF) on human epithelial-2 (HEp-2) cells. However, this technique is cost and time consuming, and characterized by considerable intra- and interlaboratory variability. Thus, an automated IIF-HEp-2 reader has been developed recently. In the current study, we compared the performance of the automated AAb IIF-HEp-2 interpretation to conventional detection methods. Autoantibody detection by IIF on HEp-2 cells was performed in a total of 260 sera of patients, including 34 with systemic lupus erythematosus, 111 with dermatomyositis or polymyositis, 74 with systemic sclerosis, 41 with rare AAb patterns, and 137 healthy individuals. Visual interpretation and routine immunoassays were compared with a novel automated IIF-HEp-2 system using Aklides pattern recognition algorithms. Positive AAbs were detected in 95-100% of rheumatic patients by automated interpretation, in 74-100% with manual reading, and in 64-100% by immunodot assay. Receiver operating characteristic curve analysis of fluorescent intensity revealed a high sensitivity and specificity for automated reading of AAb with an agreement ranging from 90% to 95% between manual and automated interpretation (kappa 0.554-0.69) for systemic sclerosis and myositis, respectively. This study demonstrates a good correlation between manual and automated interpretation of AAb including ANA and ACyA in patients with autoimmune diseases. Full automation of HEp-2 cell assay reading may minimize errors in ANA pattern interpretation and thus help in the standardization of ANA assessment.

Automated evaluation of autoantibodies on human epithelial-2 cells as an approach to standardize cell-based immunofluorescence tests

IntroductionAnalysis of autoantibodies (AAB) by indirect immunofluorescence (IIF) is a basic tool for the serological diagnosis of systemic rheumatic disorders. Automation of autoantibody IIF reading including pattern recognition may improve intra- and inter-laboratory variability and meet the demand for cost-effective assessment of large numbers of samples. Comparing automated and visual interpretation, the usefulness for routine laboratory diagnostics was investigated.MethodsAutoantibody detection by IIF on human epithelial-2 (HEp-2) cells was conducted in a total of 1222 consecutive sera of patients with suspected systemic rheumatic diseases from a university routine laboratory (n = 924) and a private referral laboratory (n = 298). IIF results from routine diagnostics were compared with a novel automated interpretation system.ResultsBoth diagnostic procedures showed a very good agreement in detecting AAB (kappa = 0.828) and differentiating respective immunofluorescence patterns. Only 98 (8.0%) of 1222 sera demonstrated discrepant results in the differentiation of positive from negative samples. The contingency coefficients of chi-square statistics were 0.646 for the university laboratory cohort with an agreement of 93.0% and 0.695 for the private laboratory cohort with an agreement of 90.6%, P < 0.0001, respectively. Comparing immunofluorescence patterns, 111 (15.3%) sera yielded differing results.ConclusionsAutomated assessment of AAB by IIF on HEp-2 cells using an automated interpretation system is a reliable and robust method for positive/negative differentiation. Employing novel mathematical algorithms, automated interpretation provides reproducible detection of specific immunofluorescence patterns on HEp-2 cells. Automated interpretation can reduce drawbacks of IIF for AAB detection in routine diagnostics providing more reliable data for clinicians.