Intracellular Ellagic Acid Derived from Goat Urine DMSO Fraction (GUDF) Predicted as an Inhibitor of c-Raf Kinase.

BACKGROUND: Dietary chemicals and their gut-metabolized products are explored for their anti-proliferative and pro-cell death effects. Dietary and metabolized chemicals are different from ruminants such as goats over humans. METHODS: Loss of cell viability and induction of death due to goat urine DMSO fraction (GUDF) derived chemicals were assessed by routine in vitro assays upon MCF-7 breast cancer cells. Intracellular metabolite profiling of MCF-7 cells treated with goat urine DMSO fraction (GUDF) was performed using an in-house designed vertical tube gel electrophoresis (VTGE) assisted methodology, followed by LC-HRMS. Next, identified intracellular dietary chemicals such as ellagic acid were evaluated for their inhibitory effects against transducers of the c-Raf signaling pathway employing molecular docking and molecular dynamics (MD) simulation. RESULTS: GUDF treatment upon MCF-7 cells displayed significant loss of cell viability and induction of cell death. A set of dietary and metabolized chemicals in the intracellular compartment of MCF-7 cells, such as ellagic acid, 2-hydroxymyristic acid, artelinic acid, 10-amino-decanoic acid, nervonic acid, 2,4-dimethyl-2-eicosenoic acid, 2,3,4'- Trihydroxy,4-Methoxybenzophenone and 9-amino-nonanoic acid were identified. Among intracellular dietary chemicals, ellagic acid displayed a strong inhibitory affinity (-8.7 kcal/mol) against c-Raf kinase. The inhibitory potential of ellagic acid was found to be significantly comparable with a known c-Raf kinase inhibitor sorafenib with overlapping inhibitory site residues (ARG450, GLU425, TRP423, VA403). CONCLUSION: Intracellular dietary-derived chemicals such as ellagic acid are suggested for the induction of cell death in MCF-7 cells. Ellagic acid is predicted as an inhibitor of c-Raf kinase and could be explored as an anti-cancer drug.

Protective role of host complement system in Aspergillus fumigatus infection.

Invasive aspergillosis (IA) is a life-threatening fungal infection for immunocompromised hosts. It is, therefore, necessary to understand the immune pathways that control this infection. Although the primary infection site is the lungs, aspergillosis can disseminate to other organs through unknown mechanisms. Herein we have examined the in vivo role of various complement pathways as well as the complement receptors C3aR and C5aR1 during experimental systemic infection by Aspergillus fumigatus, the main species responsible for IA. We show that C3 knockout (C3-/-) mice are highly susceptible to systemic infection of A. fumigatus. Intriguingly, C4-/- and factor B (FB)-/- mice showed susceptibility similar to the wild-type mice, suggesting that either the complement pathways display functional redundancy during infection (i.e., one pathway compensates for the loss of the other), or complement is activated non-canonically by A. fumigatus protease. Our in vitro study substantiates the presence of C3 and C5 cleaving proteases in A. fumigatus. Examination of the importance of the terminal complement pathway employing C5-/- and C5aR1-/- mice reveals that it plays a vital role in the conidial clearance. This, in part, is due to the increased conidial uptake by phagocytes. Together, our data suggest that the complement deficiency enhances the susceptibility to systemic infection by A. fumigatus.

An Intracellular Tripeptide Arg-His-Trp of Serum Origin Detected in MCF-7 Cells is a Possible Agonist to ß2 Adrenoceptor.

BACKGROUND: The need for agonists and antagonists of ß2 adrenoceptor (ß2AR) is warranted in various human disease conditions, including cancer, cardiovascular and other metabolic disorders. However, the sources of agonists of ß2AR are diverse in nature. Interestingly, there is a complete gap in the exploration of agonists of ß2AR from serum that is a well-known component of culture media that supports growth and proliferation of normal and cancer cells in vitro. METHODS: In this paper, we employed a novel vertical tube gel electrophoresis (VTGE)-assisted purification of intracellular metabolites of MCF-7 cells grown in vitro in complete media with fetal bovine serum (FBS). Intracellular metabolites of MCF-7 cells were then analyzed by LC-HRMS. Identified intracellular tripeptides of FBS origin were evaluated for their molecular interactions with various extracellular and intracellular receptors, including ß2AR (PDB ID: 2RH1) by employing molecular docking and molecular dynamics simulations (MDS). A known agonist of ß2AR, isoproterenol was used as a positive control in molecular docking and MDS analyses. RESULTS: We report here the identification of a few novel intracellular tripeptides, namely Arg-His- Trp, (PubChem CID-145453842), Pro-Ile-Glu, (PubChem CID-145457492), Cys-Gln-Gln, (PubChem CID-71471965), Glu-Glu-Lys, (PubChem CID-11441068) and Gly-Cys-Leu (PubChem CID-145455600) of FBS origin in MCF-7 cells. Molecular docking and MDS analyses revealed that among these molecules, the tripeptide Arg-His-Trp shows a favorable binding affinity with ß2AR (-9.8 Kcal/mol). The agonistic effect of Arg-His-Trp is significant and comparable with that of a known agonist of ß2AR, isoproterenol. CONCLUSION: In conclusion, we identified a unique Arg-His-Trp tripeptide of FBS origin in MCF-7 cells by employing a novel approach. This unique tripeptide Arg-His-Trp is suggested to be a potential agonist of ß2AR and it may have applications in the context of various human diseases like bronchial asthma and chronic obstructive pulmonary disease (COPD).

Identifying Drug Resistant miRNAs Using Entropy Based Ranking.

MicroRNAs play an important role in controlling drug sensitivity and resistance in cancer. Identification of responsible miRNAs for drug resistance can enhance the effectiveness of treatment. A new set theoretic entropy measure (SPEM) is defined to determine the relevance and level of confidence of miRNAs in deciding their drug resistant nature. Here, a pattern is represented by a pair of values. One of them implies the degree of its belongingness (fuzzy membership) to a class and the other represents the actual class of origin (crisp membership). A measure, called granular probability, is defined that determines the confidence level of having a particular pair of membership values. The granules used to compute the said probability are formed by a histogram based method where each bin of a histogram is considered as one granule. The width and number of the bins are automatically determined by the algorithm. The set thus defined, comprising a pair of membership values and the confidence level for having them, is used for the computation of SPEM and thereby identifying the drug resistant miRNAs. The efficiency of SPEM is demonstrated extensively on six data sets. While the achieved F-score in classifying sensitive and resistant samples ranges between 0.31 & 0.50 using all the miRNAs by SVM classifier, the same score varies from 0.67 to 0.94 using only the top 1 percent drug resistant miRNAs. Superiority of the proposed method as compared to some existing ones is established in terms of F-score. The significance of the top 1 percent miRNAs in corresponding cancer is also verified by the different articles based on biological investigations. Source code of SPEM is available at http://www.jayanta.droppages.com/SPEM.html.

A dosimetric study of electron beam therapy vs. high-dose-rate mould brachytherapy in adjuvant treatment of non-melanoma skin carcinomas of the head and neck region.

PURPOSE: Radiation for superficial tumours of the head and neck region can be given either by brachytherapy or electrons. Brachytherapy (BT), due to rapid dose fall-off and minor set-up errors, should be superior to external beam radiotherapy (EBRT) for treatment of lesions in difficult locations such as the nose and earlobe. The present study is a dosimetric comparison of computed tomography (CT)-based mould brachytherapy treatment plans with 3D conformal electron beam therapy in the treatment of non-melanoma skin cancers (NMSC). MATERIAL AND METHODS: From December 2017 to November 2018 10 patients with NMSC of the head and neck region (forehead, nose, cheek) who underwent adjuvant radiation with HDR brachytherapy (BT) with a surface mould individual applicator were enrolled for analysis. We evaluated dose coverage by minimal dose to 90% of planning target volume (PTV, D90), volumes of PTV receiving 90-150% of prescribed dose (PD) (VPTV90-150), conformal index for 90% and 100% of PD (COIN90, COIN100), dose homogeneity index (DHI), dose nonuniformity ratio (DNR), and exposure of organs at risk (OARs) (eyes, lens, underlying bone and skin). Prospectively, we created CT-based treatment plans for electron beam therapy. We compared conformity (COIN90, COIN100), dose coverage of PTV (D90, VPTV90, VPTV100), volumes of body receiving 10-90% of PD (V10-V90), doses to OARs (D0.1cc and D2cc) of BT and electron plans. RESULTS: We obtained mean BT-DHI 0.81, BT-DNR 0.608, Electron-DHI 1.25. We observed no significant differences in VPTV90,100 and D90 between BT and electron beam. Mean BT-VPTV125,150 were significantly higher than Electron-VPTV100,125. COIN90 was superior for BT plans. CONCLUSIONS: CT-based surface mould brachytherapy results in better conformity of superficial lesions on small, irregular surfaces such as the nose and inner canthus than electrons with a slightly higher skin dose.

Aspergillus fumigatus conidial metalloprotease Mep1p cleaves host complement proteins.

Innate immunity in animals including humans encompasses the complement system, which is considered an important host defense mechanism against Aspergillus fumigatus, one of the most ubiquitous opportunistic human fungal pathogens. Previously, it has been shown that the alkaline protease Alp1p secreted from A. fumigatus mycelia degrades the complement components C3, C4, and C5. However, it remains unclear how the fungal spores (i.e. conidia) defend themselves against the activities of the complement system immediately after inhalation into the lung. Here, we show that A. fumigatus conidia contain a metalloprotease Mep1p, which is released upon conidial contact with collagen and inactivates all three complement pathways. In particular, Mep1p efficiently inactivated the major complement components C3, C4, and C5 and their activation products (C3a, C4a, and C5a) as well as the pattern-recognition molecules MBL and ficolin-1, either by directly cleaving them or by cleaving them to a form that is further broken down by other proteases of the complement system. Moreover, incubation of Mep1p with human serum significantly inhibited the complement hemolytic activity and conidial opsonization by C3b and their subsequent phagocytosis by macrophages. Together, these results indicate that Mep1p associated with and released from A. fumigatus conidia likely facilitates early immune evasion by disarming the complement defense in the human host.

Fuzzy-Rough Entropy Measure and Histogram Based Patient Selection for miRNA Ranking in Cancer.

MicroRNAs (miRNAs) are known as an important indicator of cancers. The presence of cancer can be detected by identifying the responsible miRNAs. A fuzzy-rough entropy measure (FREM) is developed which can rank the miRNAs and thereby identify the relevant ones. FREM is used to determine the relevance of a miRNA in terms of separability between normal and cancer classes. While computing the FREM for a miRNA, fuzziness takes care of the overlapping between normal and cancer expressions, whereas rough lower approximation determines their class sizes. MiRNAs are sorted according to the highest relevance (i.e., the capability of class separation) and a percentage among them is selected from the top ranked ones. FREM is also used to determine the redundancy between two miRNAs and the redundant ones are removed from the selected set, as per the necessity. A histogram based patient selection method is also developed which can help to reduce the number of patients to be dealt during the computation of FREM, while compromising very little with the performance of the selected miRNAs for most of the data sets. The superiority of the FREM as compared to some existing methods is demonstrated extensively on six data sets in terms of sensitivity, specificity, and score. While for these data sets the score of the miRNAs selected by our method varies from 0.70 to 0.91 using SVM, those results vary from 0.37 to 0.90 for some other methods. Moreover, all the selected miRNAs corroborate with the findings of biological investigations or pathway analysis tools. The source code of FREM is available at http://www.jayanta.droppages.com/FREM.html.

Fuzzy mutual information based grouping and new fitness function for PSO in selection of miRNAs in cancer.

MicroRNAs (miRNA) are one of the important regulators of cell division and also responsible for cancer development. Among the discovered miRNAs, not all are important for cancer detection. In this regard a fuzzy mutual information (FMI) based grouping and miRNA selection method (FMIGS) is developed to identify the miRNAs responsible for a particular cancer. First, the miRNAs are ranked and divided into several groups. Then the most important group is selected among the generated groups. Both the steps viz., ranking of miRNAs and selection of the most relevant group of miRNAs, are performed using FMI. Here the number of groups is automatically determined by the grouping method. After the selection process, redundant miRNAs are removed from the selected set of miRNAs as per user's necessity. In a part of the investigation we proposed a FMI based particle swarm optimization (PSO) method for selecting relevant miRNAs, where FMI is used as a fitness function to determine the fitness of the particles. The effectiveness of FMIGS and FMI based PSO is tested on five data sets and their efficiency in selecting relevant miRNAs are demonstrated. The superior performance of FMIGS to some existing methods are established and the biological significance of the selected miRNAs is observed by the findings of the biological investigation and publicly available pathway analysis tools. The source code related to our investigation is available at http://www.jayanta.droppages.com/FMIGS.html.

Identifying relevant group of miRNAs in cancer using fuzzy mutual information.

MicroRNAs (miRNAs) act as a major biomarker of cancer. All miRNAs in human body are not equally important for cancer identification. We propose a methodology, called FMIMS, which automatically selects the most relevant miRNAs for a particular type of cancer. In FMIMS, miRNAs are initially grouped by using a SVM-based algorithm; then the group with highest relevance is determined and the miRNAs in that group are finally ranked for selection according to their redundancy. Fuzzy mutual information is used in computing the relevance of a group and the redundancy of miRNAs within it. Superiority of the most relevant group to all others, in deciding normal or cancer, is demonstrated on breast, renal, colorectal, lung, melanoma and prostate data. The merit of FMIMS as compared to several existing methods is established. While 12 out of 15 selected miRNAs by FMIMS corroborate with those of biological investigations, three of them viz., "hsa-miR-519," "hsa-miR-431" and "hsa-miR-320c" are possible novel predictions for renal cancer, lung cancer and melanoma, respectively. The selected miRNAs are found to be involved in disease-specific pathways by targeting various genes. The method is also able to detect the responsible miRNAs even at the primary stage of cancer. The related code is available at http://www.jayanta.droppages.com/FMIMS.html .

Computational approaches for identifying cancer miRNA expressions.

MicroRNAs (miRNAs) play a major role in cancer development and also act as a key factor in many other diseases. In this investigation, we propose three methods for handling miRNA expressions. The first two methods determine whether a miRNA is indicating normal or cancer condition, and the third one determines how many miRNAs are supporting the cancer sample/patient. While Method 1 acts as a two-class classifier and is based on normalized average expression value, Method 2 also does the same and is based on the normalized average intraclass distance. Method 3 checks whether a miRNA belongs to the cancer class or not, provides the percentage of supporting miRNAs for a cancer patient, and is based on weighted normalized average intraclass distance. The values of the weights are determined using exhaustive search by maximizing the accuracy in training samples. The proposed methods are tested on the differentially regulated miRNAs in three types of cancers (breast, colon, and melanoma cancer). The performances of Method 1 and Method 2 are evaluated by F score, Matthews Correlation Coefficient (MCC), and plotting "1--specificity versus sensitivity" in Receiver Operating Characteristic (ROC) space and are found to be superior to the kNN and SVM classifiers for breast, colon, and melanoma cancer data sets. It is also observed that both the sensitivity and the specificity of Method 1 and Method 2 are higher than 0.5. For the same data sets, Method 3 achieved an average accuracy of more than 98% in detecting the miRNAs, supporting the cancer condition.