Assessment of the effects of rosemary extract on mast cell-mediated allergic inflammation

The prevalence of allergic inflammatory disorders is increasing at an alarming rate, with 40-50% of school-aged children suffering today. Mast cells are immune sentinels and a driving force in both normal and pathological contexts of inflammation. Crosslinking of FcεRI by allergen-bound IgE antibodies leads to mast cell degranulation resulting in an early phase response, and the release of newly synthesized pro-inflammatory mediators, contributing to a late phase response. The mitogen-activated protein kinase (MAPK) family, phosphoinositide 3-kinase/protein kinase B (PI3K-Akt), and nuclear factor-κ-light-chain-enhancer of activated B cells (NF-κB) pathways have been established to be driving mechanisms behind mast cell-induced inflammation. Rosemary extract (RE) is rich in polyphenols and has been shown to inhibit the MAPK, PI3K-Akt, and NF-κB pathways in other cellular contexts in vitro and in in vivo. However, the effect of RE on mast cell activation has not been explored. Therefore, the aim of this study was to evaluate RE in modulating mast cell activation and FcεRI signaling via these pathways toward understanding the mechanism of action and functional outcomes. Mast cells were sensitized with anti-TNP IgE and were stimulated with the cognate allergen (TNP-BSA) under stem cell factor (SCF) potentiation and treated with 0 – 25 µg/ml RE. Samples were then collected for western blot analysis, quantitative polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA), β-hexosaminidase assay, and NFкB transcription factor activity assay. Western blot analysis demonstrated that RE treatment at both 5 and 25 µg/ml inhibited phosphorylation of p38-MAPK, and treatment with 25 µg/ml inhibited JNK. qPCR analysis showed that RE treatment at 25 µg/mL resulted in decreased gene expression of IL6, TNF, IL13, CCL1, and CCL3. It also reduced Rcan1, and NFкBIA mRNA levels. ELISA analysis further supported the qPCR data showing decreases in pro-inflammatory IL-6, TNF, IL-13, CCL1, and CCL3. The β-hexosaminidase assay demonstrated that RE treatment inhibited mast cell degranulation dose-dependently to a maximum (down to 15% of control) at 25 µg/mL RE. Finally, RE reduced NFкB activity. This work suggests that RE is capable of modulating mast cell functional outcomes, and warrants further investigation for use as a potential therapeutic

Mechanisms of action of hESC-secreted proteins that enhance human and mouse myogenesis.

Adult stem cells grow poorly in vitro compared to embryonic stem cells, and in vivo stem cell maintenance and proliferation by tissue niches progressively deteriorates with age. We previously reported that factors produced by human embryonic stem cells (hESCs) support a robust regenerative capacity for adult and old mouse muscle stem/progenitor cells. Here we extend these findings to human muscle progenitors and investigate underlying molecular mechanisms. Our results demonstrate that hESC-conditioned medium enhanced the proliferation of mouse and human muscle progenitors. Furthermore, hESC-produced factors activated MAPK and Notch signaling in human myogenic progenitors, and Delta/Notch-1 activation was dependent on MAPK/pERK. The Wnt, TGF-β and BMP/pSmad1,5,8 pathways were unresponsive to hESC-produced factors, but BMP signaling was dependent on intact MAPK/pERK. c-Myc, p57, and p18 were key effectors of the enhanced myogenesis promoted by the hECS factors. To define some of the active ingredients of the hESC-secretome which may have therapeutic potential, a comparative proteomic antibody array analysis was performed and identified several putative proteins, including FGF2, 6 and 19 which as ligands for MAPK signaling, were investigated in more detail. These studies emphasize that a youthful signaling of multiple signaling pathways is responsible for the pro-regenerative activity of the hESC factors

hESC-secreted proteins can be enriched for multiple regenerative therapies by heparin-binding.

This work builds upon our findings that proteins secreted by hESCs exhibit pro-regenerative activity, and determines that hESC-conditioned medium robustly enhances the proliferation of both muscle and neural progenitor cells. Importantly, this work establishes that it is the proteins that bind heparin which are responsible for the pro-myogenic effects of hESC-conditioned medium, and indicates that this strategy is suitable for enriching the potentially therapeutic factors. Additionally, this work shows that hESC-secreted proteins act independently of the mitogen FGF-2, and suggests that FGF-2 is unlikely to be a pro-aging molecule in the physiological decline of old muscle repair. Moreover, hESC-secreted factors improve the viability of human cortical neurons in an Alzheimers disease (AD) model, suggesting that these factors can enhance the maintenance and regeneration of multiple tissues in the aging body

Embryonic anti-aging niche

Although functional organ stem cells persist in the old, tissue damage invariably overwhelms tissue repair, ultimately causing the demise of an organism. The poor performance of stem cells in an aged organ, such as skeletal muscle, is caused by the changes in regulatory pathways such as Notch, MAPK and TGF-β, where old differentiated tissue actually inhibits its own regeneration. This perspective analyzes the current literature on regulation of organ stem cells by their young versus old niches and suggests that determinants of healthy and prolonged life might be under a combinatorial control of cell cycle check point proteins and mitogens, which need to be tightly balanced in order to promote tissue regeneration without tumor formation. While responses of adult stem cells are regulated extrinsically and age-specifically, we put forward experimental evidence suggesting that embryonic cells have an intrinsic youthful barrier to aging and produce soluble pro-regenerative proteins that signal the MAPK pathway for rejuvenating myogenesis. Future identification of this activity will improve our understanding of embryonic versus adult regulation of tissue regeneration suggesting novel strategies for organ rejuvenation. Comprehensively, the current intersection of aging and stem cell science indicates that if the age-imposed decline in the regenerative capacity of stem cells was understood, the debilitating lack of organ maintenance in the old could be ameliorated and perhaps, even reversed

Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data

<p>Abstract</p> <p>Background</p> <p>Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE) rather than recursive feature elimination (RFE). We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE.</p> <p>Results</p> <p>We have developed a novel method for selecting significant genes in comparative gene expression studies. This method, which we refer to as SVM-RCE, combines K-means, a clustering method, to identify correlated gene clusters, and Support Vector Machines (SVMs), a supervised machine learning classification method, to identify and score (rank) those gene clusters for the purpose of classification. K-means is used initially to group genes into clusters. Recursive cluster elimination (RCE) is then applied to iteratively remove those clusters of genes that contribute the least to the classification performance. SVM-RCE identifies the clusters of correlated genes that are most significantly differentially expressed between the sample classes. Utilization of gene clusters, rather than individual genes, enhances the supervised classification accuracy of the same data as compared to the accuracy when either SVM or Penalized Discriminant Analysis (PDA) with recursive feature elimination (SVM-RFE and PDA-RFE) are used to remove genes based on their individual discriminant weights.</p> <p>Conclusion</p> <p>SVM-RCE provides improved classification accuracy with complex microarray data sets when it is compared to the classification accuracy of the same datasets using either SVM-RFE or PDA-RFE. SVM-RCE identifies clusters of correlated genes that when considered together provide greater insight into the structure of the microarray data. Clustering genes for classification appears to result in some concomitant clustering of samples into subgroups.</p> <p>Our present implementation of SVM-RCE groups genes using the correlation metric. The success of the SVM-RCE method in classification suggests that gene interaction networks or other biologically relevant metrics that group genes based on functional parameters might also be useful.</p> <p/

Learning from positive examples when the negative class is undetermined- microRNA gene identification

<p>Abstract</p> <p>Background</p> <p>The application of machine learning to classification problems that depend only on positive examples is gaining attention in the computational biology community. We and others have described the use of two-class machine learning to identify novel miRNAs. These methods require the generation of an artificial negative class. However, designation of the negative class can be problematic and if it is not properly done can affect the performance of the classifier dramatically and/or yield a biased estimate of performance. We present a study using one-class machine learning for microRNA (miRNA) discovery and compare one-class to two-class approaches using naïve Bayes and Support Vector Machines. These results are compared to published two-class miRNA prediction approaches. We also examine the ability of the one-class and two-class techniques to identify miRNAs in newly sequenced species.</p> <p>Results</p> <p>Of all methods tested, we found that 2-class naive Bayes and Support Vector Machines gave the best accuracy using our selected features and optimally chosen negative examples. One class methods showed average accuracies of 70–80% versus 90% for the two 2-class methods on the same feature sets. However, some one-class methods outperform some recently published two-class approaches with different selected features. Using the EBV genome as and external validation of the method we found one-class machine learning to work as well as or better than a two-class approach in identifying true miRNAs as well as predicting new miRNAs.</p> <p>Conclusion</p> <p>One and two class methods can both give useful classification accuracies when the negative class is well characterized. The advantage of one class methods is that it eliminates guessing at the optimal features for the negative class when they are not well defined. In these cases one-class methods can be superior to two-class methods when the features which are chosen as representative of that positive class are well defined.</p> <p>Availability</p> <p>The OneClassmiRNA program is available at: <abbrgrp><abbr bid="B1">1</abbr></abbrgrp></p