Therapeutic potential of gossypol: an overview

CONTEXT

Polyphenols are naturally occurring compounds found in fruits, vegetables, cereals, and beverages. Polyphenols occupy a unique place in biological science for their pharmacological properties. Gossypol is a polyphenolic compound that has attracted attention because of its biological effects.

OBJECTIVE

Gossypol is reported to exhibit antifertility, antioxidant, anticancer, antivirus, antiparasitic, and antimicrobial properties and lower plasma cholesterol. These are summarized with attention to the mechanisms of activity.

METHODS

This review summarizes the results of studies obtained in a comprehensive search of ScienceDirect, PubMed, Scirus, and Web of Science.

RESULTS AND CONCLUSION

The results of these studies provide a comprehensive understanding of the biological action of gossypol and its potential for the prevention of and therapy for resistant tumors and chronic human diseases such as HIV, malaria, and psoriasis.

Magnetic bio-metal-organic framework nanocomposites decorated with folic acid conjugated chitosan as a promising biocompatible targeted theranostic system for cancer treatment

In this work, a multifunctional magnetic Bio-Metal-Organic Framework (Fe₃O₄@Bio-MOF) coated with folic acid-chitosan conjugate (FC) was successfully prepared for tumor-targeted delivery of curcumin (CUR) and 5-fluorouracil (5-FU) simultaneously. Bio-MOF nanocomposite based on CUR as organic linker and zinc as metal ion was prepared by hydrothermal method in the presence of amine-functionalized Fe₃O₄ magnetic nanoparticles (Fe₃O₄@NH₂ MNPs). 5-FU was loaded in the magnetic Bio-MOF and the obtained nanocarrier was then coated with FC network. The prepared nanocomposite (NC) was fully characterized by high resolution-transmission electron microscope (HR-TEM), field emission scanning electron microscopy (FE-SEM), Dynamic light scattering (DLS), X-ray diffraction analysis (XRD), thermogravimetric analysis (TGA), vibrating sample magnetometry (VSM), nuclear magnetic resonance (NMR), and UV-vis analyses. In vitro release study showed controlled release of CUR and 5-FU in acidic pH confirming high selectivity and performance of the carrier in cancerous microenvironments. The selective uptake of 5-FU-loaded Fe₃O₄@Bio-MOF-FC by folate receptor-positive MDA-MB-231 cells was investigated and verified. The ultimate nanocarrier exhibited no significant toxicity, while drug loaded nanocarrier showed selective and higher toxicity against the cancerous cells than normal cells. SDS PAGE was also utilized to determine the protein pattern attached on the surface of the nanocarriers. In vitro and in vivo MRI studies showed negative signal enhancement in tumor confirming the ability of the nanocarrier to be applied as diagnostic agent. Owing to the selective anticancer release and cellular uptake, acceptable blood compatibility as well as suitable T₂ MRI contrast performance, the target nanocarrier could be considered as favorable theranostic in breast cancer.

Predicting protein-protein interactions between human and hepatitis C virus via an ensemble learning method

An estimated 170 million people, approximately 3% of the world population, are chronically infected with the hepatitis C virus (HCV). More than 350,000 deaths are reported annually, which are caused by HCV. HCV, similar to a variety of viruses, causes disease in humans by altering protein-protein interactions within the host cells. Experimental approaches for the detection of host-virus PPIs have many inherent limitations. Computational approaches to predict these interactions are therefore of significant importance. While many studies have been developed to predict intra-species PPIs in the last decade, predictions on inter-species PPIs such as human-HCV PPIs are rare. In this study, we developed an ensemble learning method to predict PPIs between human and HCV proteins. Our model utilises four well-established diverse learners as base classifiers including random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and multilayer perceptron (MLP). In addition, an MLP was used as a meta-learner to combine base learners' predictions to provide the final prediction. To encode human and HCV proteins as feature vectors, we used six different descriptors as follows: amino acid composition (ACC), pseudo amino acid composition (PAC), evolutionary information feature, network centrality measures, tissue information and post-translational modification information. To assess the prediction power of the proposed method, we assembled a benchmark dataset composed of confident positive and negative PPIs. In a 10-fold cross-validation experiment, our prediction method achieved accuracy and specificity as high as 83% and 94%, respectively. Furthermore, in an independent test set the proposed method achieved an accuracy of 84% and a specificity of 92%. When compared with the existing method, our method showed a better performance. These results revealed that our method is suitable for performing PPI prediction in a host-pathogen context.

Gold-capped mesoporous silica nanoparticles as an excellent enzyme-responsive nanocarrier for controlled doxorubicin delivery

Mesoporous silica nanoparticles (MSNs) have ideal characteristics as next generation of controlled drug delivery systems. In this study, a MSN-based nanocarrier was fabricated and gold nanoparticle (GNP)-biotin conjugates were successfully grafted onto the pore outlets of the prepared MSN. This bioconjugate served as a capping agent with a peptide-cleavable linker sensitive to matrix metalloproteinases (MMPs), which are overexpressed extracellular proteolytic enzymes in cancerous tissue. The prepared nanocarriers were fully characterised by scanning electron microscopy (SEM), transmission electron microscopy (TEM), nitrogen adsorption/desorption, Fourier transform infra-red spectroscopy (FTIR), dynamic light scattering (DLS) and thermo gravimetric analysis (TGA). In vitro release studies showed efficient capping of MSNs with gold gate and controlled release of Doxorubicin (DOX) in the presence of matrix metalloproteinase-2 (MMP-2) and acidic pH values. High DOX-loading capacity (21%) and encapsulation efficiency (95.5%) were achieved using fluorescence technique. DOX-loaded nanocarriers showed high cytocompatibility and could efficiently induce cell death and apoptosis in the MMP-2 overexpressed cell lines. Moreover, Haemolysis, platelet activation and inflammatory responses assessment approved excellent hemocompatibility and minimal side effects by encapsulation of DOX in MSNs carrier.

Caffeic Acid Phenethyl Ester Increases Radiosensitivity of Estrogen Receptor-Positive and -Negative Breast Cancer Cells by Prolonging Radiation-Induced DNA Damage

Purpose

Breast cancer is an important cause of death among women. The development of radioresistance in breast cancer leads to recurrence after radiotherapy. Caffeic acid phenethyl ester (CAPE), a polyphenolic compound of honeybee propolis, is known to have anticancer properties. In this study, we examined whether CAPE enhanced the radiation sensitivity of MDA-MB-231 (estrogen receptor-negative) and T47D (estrogen receptor-positive) cell lines.

Methods

The cytotoxic effect of CAPE on MDA-MB-231 and T47D breast cancer cells was evaluated by performing an 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) assay. To assess clonogenic ability, MDA-MB-231 and T47D cells were treated with CAPE (1 µM) for 72 hours before irradiation, and then, a colony assay was performed. A comet assay was used to determine the number of DNA strand breaks at four different times.

Results

CAPE decreased the viability of both cell lines in a dose- and time-dependent manner. In the clonogenic assay, pretreatment of cells with CAPE before irradiation significantly reduced the surviving fraction of MDA-MB-231 cells at doses of 6 and 8 Gy. A reduction in the surviving fraction of T47D cells was observed relative to MDA-MB-231 at lower doses of radiation. Additionally, CAPE maintained radiation-induced DNA damage in T47D cells for a longer period than in MDA-MB-231 cells.

Conclusion

Our results indicate that CAPE impairs DNA damage repair immediately after irradiation. The induction of radiosensitivity by CAPE in radioresistant breast cancer cells may be caused by prolonged DNA damage.

Photobiomodulation leads to enhanced radiosensitivity through induction of apoptosis and autophagy in human cervical cancer cells

The radiomodulatory effect of photobiomodulation (PBM) has recently been studied in cancer cells. The aim of this study was to investigate cellular mechanisms involved in the X-ray radiosensitivity of HeLa cells pre-exposed to PBM. HeLa cells were irradiated with 685 nm laser at different energy densities prior to X-ray ionizing radiation. After irradiation, clonogenic cell survival, cell death due to apoptosis and autophagy were determined. Levels of intracellular reactive oxygen species (ROS), DNA damage and, cell cycle distribution after PBM were measured. PBM at different energy densities (5-20 J/cm² ) was not cytotoxic. However, HeLa cells pre-exposed to 20 J/cm² showed enhanced inhibition of colony formation following ionizing radiation. Enhanced radiosensitivity was due to increased oxidative stress, DNA damage, and radiation-induced apoptosis and autophagy. These results suggest that 685 nm PBM at a higher energy density could possibly be a promising radiosensitizing agent in cervical cancer, to decrease the radiation dose delivered, and therefore prevent the side-effects that are associated with cancer radiotherapy.

Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models

Rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important in diagnosis of this disease. Furthermore sequence-derived structural and physicochemical descriptors are very useful for machine learning prediction of protein structural and functional classes, classifying proteins and the prediction performance. Herein, in this study is the classification of lung tumors based on 1497 attributes derived from structural and physicochemical properties of protein sequences (based on genes defined by microarray analysis) investigated through a combination of attribute weighting, supervised and unsupervised clustering algorithms. Eighty percent of the weighting methods selected features such as autocorrelation, dipeptide composition and distribution of hydrophobicity as the most important protein attributes in classification of SCLC, NSCLC and COMMON classes of lung tumors. The same results were observed by most tree induction algorithms while descriptors of hydrophobicity distribution were high in protein sequences COMMON in both groups and distribution of charge in these proteins was very low; showing COMMON proteins were very hydrophobic. Furthermore, compositions of polar dipeptide in SCLC proteins were higher than NSCLC proteins. Some clustering models (alone or in combination with attribute weighting algorithms) were able to nearly classify SCLC and NSCLC proteins. Random Forest tree induction algorithm, calculated on leaves one-out and 10-fold cross validation) shows more than 86% accuracy in clustering and predicting three different lung cancer tumors. Here for the first time the application of data mining tools to effectively classify three classes of lung cancer tumors regarding the importance of dipeptide composition, autocorrelation and distribution descriptor has been reported.

Gossypol enhances radiation induced autophagy in glioblastoma multiforme

Malignant gliomas (glioblastoma multiforme) are the most aggressive of the primary brain tumors. Radiotherapy is an important tool for treatment of cancer but malignant gliomas are usually resistant to radiotherapy and other adjuvant therapies. Thus new drugs are needed to increase the efficiency of radiotherapy in order to improve the therapeutic outcome of tumor patients. Recent investigations showed that gossypol, natural polyphenolic compound produced by cotton plants, is a promising agent against solid tumors. The current study was defined to evaluate whether the combinatorial effect of radiation and gossypol would induce higher level of cell death on U-87 MG than single agent treatment and its possible mechanism of action. Clonogenic survival assay showed that ionizing radiation plus gossypol significantly inhibited clonogenic growth of irradiated cells as compared with either treatment alone. Acridine orange/etidium bromide staining confirmed that there was no significant increase in necrotic and apoptotic cells, but irradiated cells in combination with gossypol showed a significant increase in accumulation of acidic vesicular organelle. The results obtained herein indicated that gossypol is a promising drug that induced autophagic cell death in radioresistant malignant glioma.

Prediction of lung tumor types based on protein attributes by machine learning algorithms

Early diagnosis of lung cancers and distinction between the tumor types (Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are very important to increase the survival rate of patients. Herein, we propose a diagnostic system based on sequence-derived structural and physicochemical attributes of proteins that involved in both types of tumors via feature extraction, feature selection and prediction models. 1497 proteins attributes computed and important features selected by 12 attribute weighting models and finally machine learning models consist of seven SVM models, three ANN models and two NB models applied on original database and newly created ones from attribute weighting models; models accuracies calculated through 10-fold cross and wrapper validation (just for SVM algorithms). In line with our previous findings, dipeptide composition, autocorrelation and distribution descriptor were the most important protein features selected by bioinformatics tools. The algorithms performances in lung cancer tumor type prediction increased when they applied on datasets created by attribute weighting models rather than original dataset. Wrapper-Validation performed better than X-Validation; the best cancer type prediction resulted from SVM and SVM Linear models (82%). The best accuracy of ANN gained when Neural Net model applied on SVM dataset (88%). This is the first report suggesting that the combination of protein features and attribute weighting models with machine learning algorithms can be effectively used to predict the type of lung cancer tumors (SCLC and NSCLC).

Down-regulation of Hsp27 radiosensitizes human prostate cancer cells

AIM

In this study, we examined whether human Hsp27 is involved in the radiation sensitization induced by gamma radiation in cultured human prostate cancer cells.

METHODS

We transfected DU145 cells with full length Hsp27 antisense cDNA to obtain viable cell lines which expressed reduced Hsp27. Selected individual clones were subjected to western blot analyses to confirm reduced expression of Hsp27.

RESULTS

Hsp27 belongs to a family of abundant and ubiquitous stress proteins, the small heat shock proteins. It has been shown that Hsp27 can inhibit apoptosis both in a caspase-dependent and independent manner. Colony assays showed that the cells engineered to express reduced Hsp27 levels had a significantly increased sensitivity to gamma radiation compared with control cells that were transfected with the vector alone. However, there was also a significant difference in viability of cells with reduced Hsp27 levels and control cells 72 h after gamma-irradiation.

CONCLUSIONS

Our results suggest the possible application of antisense Hsp27 cDNA or other methods to reduce Hsp27 expression as a radiation sensitizer in radiation oncology.

Apoptotic pathway induced by noscapine in human myelogenous leukemic cells

It has been shown that noscapine, an opium-derived phthalideisoquinoline alkaloid that is currently being used as an oral antitussive drug, induces apoptosis in myeloid leukemia cells. The molecular mechanism responsible for the anticancer effects of noscapine is poorly understood. In the current study, the apoptotic effects of noscapine on two myeloid cell lines, apoptosis-proficient HL60 cells and apoptosis-resistant K562 cells, were analyzed. An increase in the activity of caspase-2, -3, -6, -8 and -9, poly(ADP ribose) polymerase cleavage, detection of phosphatidylserine on the outer layer of the cell membrane, nucleation of chromatin, and DNA fragmentation suggested the induction of apoptosis. Noscapine increased the Bax/Bcl-2 ratio with a significant decrease of Bcl-2 expression accompanied with Bcl-2 phosphorylation. Using an inhibitory approach, the activation of the caspase cascade involved in the noscapine-induced apoptosis was analyzed. We observed no inhibitory effect of the caspase-8 inhibitor on caspase-9 activity. In view of these results and taking into consideration that K562 cells are Fas-null, we suggested that caspase-8 is activated in a Fas-independent manner downstream of caspase-9. In conclusion, noscapine can induce apoptosis in both apoptosis-proficient and apoptosis-resistant leukemic cells, and it can be a novel candidate in the treatment of hematological malignancies.

A biophysical study on the mechanism of interactions of DOX or PTX with α-lactalbumin as a delivery carrier

Doxorubicin and paclitaxel, two hydrophobic chemotherapeutic agents, are used in cancer therapies. Presence of hydrophobic patches and a flexible fold could probably make α-Lactalbumin a suitable carrier for hydrophobic drugs. In the present study, a variety of thermodynamic, spectroscopic, computational, and cellular techniques were applied to assess α-lactalbumin potential as a carrier for doxorubicin and paclitaxel. According to isothermal titration calorimetry data, the interaction between α-lactalbumin and doxorubicin or paclitaxel is spontaneous and the K (M^-1) value for the interaction of α-lactalbumin and paclitaxel is higher than that for doxorubicin. Differential scanning calorimetry and anisotropy results indicated formation of α-lactalbumin complexes with doxorubicin or paclitaxel. Furthermore, molecular docking and dynamic studies revealed that TRPs are not involved in α-Lac's interaction with Doxorubicin while TRP 60 interacts with paclitaxel. Based on Pace analysis to determine protein thermal stability, doxorubicin and paclitaxel induced higher and lower thermal stability in α-lactalbumin, respectively. Besides, fluorescence lifetime measurements reflected that the interaction between α-lactalbumin with doxorubicin or paclitaxel was of static nature. Therefore, the authors hypothesized that α-lactalbumin could serve as a carrier for doxorubicin and paclitaxel by reducing cytotoxicity and apoptosis which was demonstrated during our in vitro cell studies.

The role of heat shock protein 70 in the thermoresistance of prostate cancer cell line spheroids

Heat shock protein 70 (Hsp70), a protein induced in cells exposed to sublethal heat shock, is present in all living cells and has been highly conserved during evolution. The aim of the current study was to determine the role of heat shock proteins in the resistance of prostate carcinoma cell line spheroids to hyperthermia. In vitro, the expression of Hsp70 by the DU 145 cell line, when cultured as monolayer or multicellular spheroids, was studied using Western blotting and enzyme-linked immunosorbent assay methods. The level of Hsp70 in spheroid cultures for up to 26 days at 37 degrees C remained similar to monolayer cultures. However, in samples treated with hyperthermia at 43 degrees C for 120 min, the spheroid cultures expressed a higher level of Hsp70 as compared to monolayer culture. Under similar conditions of heat treatment, the spheroids showed more heat resistance than monolayer cultures as judged by the number of colonies that they formed in suspension cultures. The results suggest that cells cultured in multicellular spheroids showed more heat resistance as compared to monolayer cultures by producing higher levels of Hsp70.

Inferring interaction type in gene regulatory networks using co-expression data

Background

Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction.

Results

This paper describes a novel algorithm, “Signing of Regulatory Networks” (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN.

Conclusions

SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type.

Electronic supplementary material

The online version of this article (doi:10.1186/s13015-015-0054-4) contains supplementary material, which is available to authorized users.

Characterization of the interaction between human serum albumin and diazinon via spectroscopic and molecular docking methods

Human serum albumin (HSA) is a soluble blood protein which binds to small molecules (such as drugs and toxins) and transfers them within the blood circulation. In this research, the interaction of diazinon, as a toxic organophosphate, with HSA was investigated. Various biophysical methods such as fluorescence, ultraviolet–visible (UV-vis), Fourier transform infrared spectroscopy, and molecular docking were utilized to characterize the binding properties of diazinon to HSA under physiological-like condition. The UV-vis spectroscopy showed that the absorption increased and the fluorescence intensity of HSA decreased regularly with regard to the gradual increases of the concentrations of diazinon. Due to the binding constant of ( ka = 3.367 × 10+4 M−1), the α-helix structure for the first day and 35 days of incubation were obtained 66.09–55.4% and 59.99–46.48%, respectively, and their amounts in other secondary structures (β-sheet, β-anti, and random (r) coils) were increased. The molecular docking revealed a good binding site in HSA (Trp-214) for diazinon which was related to the considerable alterations in HSA secondary and tertiary structures. There is a close relationship between the secondary structure of protein and its biological activity and after 35 days of incubation, the high toxic concentrations of diazinon can make HSA to be partially unfolded and lose its structure.

The state of the art of biomedical applications of optogenetics

BACKGROUND AND OBJECTIVE

Optogenetics has opened new insights into biomedical research with the ability to manipulate and control cellular activity using light in combination with genetically engineered photosensitive proteins. By stimulating with light, this method provides high spatiotemporal and high specificity resolution, which is in contrast to conventional pharmacological or electrical stimulation. Optogenetics was initially introduced to control neural activities but was gradually extended to other biomedical fields.

STUDY DESIGN

In this paper, firstly, we summarize the current optogenetic tools stimulated by different light sources, including lasers, light-emitting diodes, and laser diodes. Second, we outline the variety of biomedical applications of optogenetics not only for neuronal circuits but also for various kinds of cells and tissues from cardiomyocytes to ganglion cells. Furthermore, we highlight the potential of this technique for treating neurological disorders, cardiac arrhythmia, visual impairment, hearing loss, and urinary bladder diseases as well as clarify the mechanisms underlying cancer progression and control of stem cell differentiation.

CONCLUSION

We sought to summarize the various types of promising applications of optogenetics to treat a broad spectrum of disorders. It is conceivable to expect that optogenetics profits a growing number of patients suffering from a range of different diseases in the near future.

Exceptional pairs of amino acid neighbors in alpha-helices

Amino acids seem to have specific preferences for various locations in alpha-helices. These specific preferences, called singlet local propensity (SLP), have been determined by calculating the preference of occurrence of each amino acid in different positions of the alpha-helix. We have studied the occurrence of amino acids, single or pairs, in different positions, singlet or doublet, of alpha-helices in a database of 343 non-homologous proteins representing a unique superfamily from the SCOP database with a resolution better than 2.5 A from the Protein Data Bank. The preference of single amino acids for various locations of the helix was shown by the relative entropy of each amino acid with respect to the background. Based on the total relative entropy of all amino acids occurring in a single position, the N(cap) position was found to be the most selective position in the alpha-helix. A rigorous statistical analysis of amino acid pair occurrences showed that there are exceptional pairs for which, the observed frequency of occurrence in various doublet positions of the alpha-helix is significantly different from the expected frequency of occurrence in that position. The doublet local propensity (DLP) was defined as the preference of occurrences of amino acid pairs in different doublet positions of the alpha-helix. For most amino acid pairs, the observed DLP (DLP(O)) was nearly equal to the expected DLP (DLP(E)), which is the product of the related SLPs. However, for exceptional pairs of amino acids identified above, the DLP(O) and DLP(E) values were significantly different. Based on the relative values of DLP(O) and DLP(E), exceptional amino acid pairs were divided into two categories. Those, for which the DLP(O) values are higher than DLP(E), should have a strong tendency to pair together in the specified position. For those pairs which the DLP(O) values are less than DLP(E), there exists a hindrance in neighboring of the two amino acids in that specific position of the alpha-helix. These cases have been identified and listed in various tables in this paper. The amount of mutual information carried by the exceptional pairs of amino acids was significantly higher than the average mutual information carried by other amino acid pairs. The average mutual information conveyed by amino acid pairs in each doublet position was found to be very small but non-zero.