text:
Mining Chemical Activity Status from High-Throughput Screening Assays

High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote and can be found on Figshare.

Introduction
Experimental screening of chemical compounds for their biological activity has partial coverage and leaves millions of chemical compounds untested [1]. Such experiments are usually pursued through high-throughput screening (HTS) assays in which chemical molecules (e.g. drugs) are tested against specific biological targets (e.g. protein) [2]. With existence of emerging and growing public repositories (e.g. PubChem database [3]) that provide access to biological activity information from HTS experiments, there is an opportunity to develop computational methods to predict the biological activities of millions of chemical compounds that remain untested [3,4]. For example, data mining techniques may help narrow down promising candidate chemicals aimed at interaction with specific molecular targets before they are experimentally evaluated [5][6][7]. This, in principle, may help in speeding up the drug discovery process. Developing accurate prediction models for in silico HTS is however challenging. For datasets such as those obtained from HTS assays, achieving high prediction accuracy may be misleading since this may be accompanied by unacceptable false positive rate [8] as high accuracy does not always imply small proportion of false predictions. The fact that should be considered is that HTS experimental data is usually characterized by a great disproportion of active and inactive chemical compounds out of thousands screened [9]. This class imbalance may affect accuracy and precision of resultant predictors of activity status in individual assays [10]. If the imbalance ratio (IR) between the inactive and active compound classes can be adjusted, the performance may improve [10][11][12].
In this study we examine robust solutions that can be used for in silico screening of compound activity status in individual HTS assays that are characterized by great class imbalance. For such cases, several data mining techniques have been developed to model chemical-target interactions [13][14][15][16]. These techniques differ from virtual screening based on ligand-protein docking [17], as they do not require any prior knowledge about the 3D surface representation of the target and its cognate interactor. Also, once trained, data mining models are usually faster than ligand-protein docking models in predicting biological activity status of a given chemical compound [18].Several web tools for predicting chemical-protein interactions have also been developed [19][20][21][22].Decision trees are used by Han et al. [23] to predict the activity of a chemical compound based on the standard set of PubChem features that define chemical fingerprints [24]. The study demonstrated that the great imbalance between data classes limits classification accuracy. Different studies [25,26] were focused on finding solution to this problem. Cost-sensitive classifiers were explored by Schierz et al. [25] to assign a prior importance weight to the minority class for training, whereas an optimization procedure for selecting informative samples, specifically aimed at enhancing performance of support vector machines (SVMs) was also explored [26].
Although good progress has been achieved for building predictive models for HTS data, there are still many issues in current methods that need to be investigated further.
First, many studies have developed prediction models for HTS data without considering precision or other precision relevant scores like F 1 Score in optimizing the performance of these models. Recently, some studies [27][28][29] explored applying random under-sampling or synthetic over-sampling techniques to some assays (BioAssays) from the PubChem database. These studies did not focus on or report the precision of predictions and their impact over the number of false positives, which are highly relevant [9,11,12,30]. In the case of in silico screening of chemical activity status, the increased precision will reduce the number of falsely predicted candidate compounds thus reducing the cost of the potential follow up laboratory experiments [8].
Second, generating and selecting a good subset of features is an important step in developing a well-performing prediction model, and may help in the cases of data with large class imbalance [31,32]. Few efforts, however, have been dedicated for finding strong discriminating features for HTS data [26,33,34].
To tackle the above-mentioned problems, in this study we examine robust solutions to be used for in silico screening of compound activity status in individual HTS assays. For this purpose, we run experiments using various state-of-the-art methods and compare their effect on prediction of chemical activity status using different performance metrics. Also, we developed a variant method, DRAMOTE, based on ideas from active learning, which favors selection of precision-informative training samples. We describe the data by a rich set of features that includes PubChem fingerprint features. The set of feature we generated is, to the best of our knowledge, the most comprehensive feature set used for problems of this type. This set of features was further subjected to a feature selection method to propose a set of features that may result in an improved prediction performance in comparison to the PubChem fingerprint features alone. The results of 1,350 in silico experiments that involved close to 500,000 interactions, suggest that DRAMOTE is the most efficient variant of data preprocessing in the case of great class imbalance based on the datasets from PubChem we used. DRAMOTE, which favors selection of interactions that enhances the overall precision of a learning model, improves F 1 Score on average by over 41%, relative to other methods. Finally, we illustrate the usefulness of our DRAMOTE method through a case study of screening all FDA approved drugs in the DrugBank database [35] against the thyroid stimulating hormone receptor (TSHR) in humans and suggest top 10 candidates that potentially interact with TSHR. Our findings are further partially and indirectly supported by 3D docking results and literature information.

Datasets
PubChem BioAssay Database. For this study we selected nine datasets from the Pub-Chem BioAssay database where targets are proteins except for one dataset where the target is cell-based. Although we have a special interest in protein targets, we choose a case that is cellbased to illustrate the generality of our method. It is worth noting that all the datasets we chose are based on the confirmatory assays and we avoided selection of primary assays based on recommendation of [25]. The datasets are based on the PubChem's BioAssay protocol, where assays can be referenced by a unique AID identifier. A single BioAssay reports experimental activity results for a set of chemical compounds over a specific biological target, which in most cases is a protein. So, a BioAssay dataset contains a list of chemical compounds with assigned labels, where label '+1' indicates that the compound shows activity with the examined target, while '-1' relates to inactive compounds. Table 1 provides a summary of the datasets used in the study. Eight of these datasets AID: 596, AID: 618, AID: 644, AID: 886, AID: 899, AID: 938, AID: 743042 and AID: 743288, were chosen to demonstrate different imbalance ratios (IR) between the active and inactive compound classes. The ninth one, called BenchSet, is a benchmark dataset that is obtained by merging three BioAssays, AID: 773, AID: 1006 and AID: 1379, as described previously by Li et al. [26]. In total, these datasets are composed of 11 BioAssays that represent 487,557 inactive and active interactions and offer a wide variety of class imbalance ratios ranging from 0.26% (i.e. high IR) to 48% (i.e. small IR), where IR is represented as ratio of the number of minority active cases to the number of majority inactive cases. For reporting performance over these datasets, 5-fold cross-validation setup is followed in all computational experiments. Given the large size of our experimental datasets (as shown in Table 1), 5-fold cross-validation for evaluation is a proper choice for computing a representative (i.e. non-biased) estimate [36,37]. In order to avoid any potential bias, testing data is never used in the training process.
DrugBank database. The DrugBank database data (accessed on August, 2014) was downloaded from the website: http://www.drugbank.ca/ [35]. The initial database had about 6,800 drug entries including 1,491 FDA-approved drugs. We considered only the FDA-approved drug list to screen the model we developed for thyroid stimulating hormone receptor (TSHR).

Generation of features
Generating and selecting a good subset of features is an important step in developing a wellperforming classification model, and may also help in the cases of large class imbalance [31,32]. A variety of feature sets of varying complexity have been compiled for virtual screening and prediction of biological activity [25,38]. In this study, we used the combined set of fingerprint features from two major cheminformatics toolkits, RDKit [39] and OpenBabel [40], as well as features from PubChem fingerprints [24]. OpenBabel [40] was specifically used to generate different SMARTS patterns and 3D spectrophore descriptors. In addition, several basic chemical descriptors, such as the molecular weight, number of H-acceptors and donors, and Log-P, were calculated. The finally generated set of features contained 2,940 features. A detailed description of all the features used in the study, as well as the ones we selected, is provided in S1 Text. This, to the best of our knowledge, is the largest set of features compiled for use in prediction of chemical activity status from HTS assays.

Feature selection (FS)
A large set of compiled features, as described in the previous section, leads to generating information of different level of redundancy, as well as introduces features that may not be relevant to the types of biological activity of chemicals as observed in particular assays. A good FS method should be able to remove a lot of such redundant or irrelevant information [41]. FS methods, in general, can be categorized into: the filters, the wrappers, and those based on the embedded FS models [32,42]. In this study, the wrapper FS model of DWFS tool [43], which selects features so as to maximize the performance of a classifier, is applied. The default setup of DWFS tool was used for FS experiments. As an illustration, an analysis of the effect of FS on the classification performance for one of the datasets can be found in S2 Text.

Classifiers
Six widely used classifiers are applied as a basis for comparing different solutions of the class imbalance problem for activity testing in PubChem assays. These include support vector machines [44,45] (SVM) with linear and radial basis function (RBF) kernels, K-nearest neighbors (KNN; K = 3) [46], Linear Discriminant Analysis (LDA) [47], Naïve Bayes Classifier (NBC) [48] and Random Forests (RF) [49]. For SVM, LIBSVM [50] implementation was used for building the different SVM models. The default cost parameter as well as RBF kernel widths were used.

Performance evaluation
Performance of all methods referred to in the results section is obtained form a 5-fold crossvalidation. The testing fold was never used in the training phase. Since we performed 5-fold cross-validation, with six classifiers and five class imbalance solutions, we performed 150 (5 folds × 6 classifiers × 5 solutions = 150) experiments for each dataset and 1,350 in total for all nine datasets. We report the average performance over the 5-folds of every dataset, as well as the standard deviation. In addition, we perform significance analysis between the methods using one-way analysis of variance (ANOVA). In cases where there is a significant difference between the methods, we further apply the well-known pair-wise Tukey mean-mean multiple comparison (MCC) to determine which pairs are significantly different, while simultaneously examining all methods [see S1 Table]. Giving the characteristics of this problem and the nature of having highly imbalanced classes, we provide results over many performance metrics to gain a generic view of the performances of different solutions. Let TP be the number of true positives, FP the number of false positives, TN the number of true negatives and FN the number of false negatives. The results in this study are reported based on Eqs (1-7).
ROC AUC ¼ Area under the receiver operating characteristic curve ð7Þ The predictions of a classifier for a HTS dataset should result in high precision in order for the set of predicted active compounds to contain as few FP predictions as possible. The number of FPs is a crucial factor in measuring the reliability of predictions as minimizing it leads to increased chances of successful follow up experiments. F 1 Score [9] is a summary metric that computes the weighted average of precision and sensitivity. It is also known as balanced F-Score since it balances both precision and sensitivity equally. F 0.5 Score [11,51,52] is another summary metric that weights precision twice as much as sensitivity. Given that intention to use the classifier for computational screening of millions of compounds, sensitivity is of less importance than precision. A conservative sensitivity rate with higher precision will still lead to large number of accurate new findings when screening a large number of candidates in the context of HTS. Thus, we give preference to precision and F 0.5 Score as more indicative performance measures in such scenario. We consider also in the results section, discussion over sensitivity and F 1 Score. For other metrics like specificity and ROC AUC scores are reported in details in S2 Table. Methods Data preprocessing for class imbalance case. HTS experiments are usually characterized by only a small number of active chemical compounds obtained after screening a big compound set. This nature of imbalanced distributions of the active and inactive compound classes may lead to a degraded classification performance that should be addressed. The class imbalance problem is one of the challenging tasks that received a lot of attention [53][54][55]. There exists a wide variety of state-of-the-art solutions of the class imbalance problem, which can be categorized abstractly into algorithmic and data-based ones [9]. In our study we consider the following approaches: majority random under-sampling (RU), synthetic minority oversampling technique (SMOTE) [56], granular SVMs for under-sampling (GSVM-RU) [57,58], majority weighted minority over-sampling technique (MWMOTE) [59] and our precisionaware proposed method DRAMOTE. Further details about the existing methods are provided in S3 Text.
DRAMOTE: our proposed solution. There are certain limitations with the existing solutions for data preprocessing in the case of class imbalance. Methods like RU and SMOTE apply sampling procedures to data without considering the effect of sampling on the classification performance. These methods are independent of any feedback from the classifier and may affect the performance only to a certain limit. In other words, these methods do not provide a mechanism to have a control over precision or other performance metrics. Other algorithms like GSVM-RU, take into account the performance of the classifier, but are limited to a specific classifier, e.g. GSVM-RU is limited to SVM and cannot be applied to other classifiers. MWMOTE needs more parameters for selecting an informative set of minority samples and is limited to optimize the performance over nearest neighbor type of classifiers. We propose here a novel method motivated by ideas from active learning (AL) (for more details about AL see S3 Text). The method is based on establishing a feedback loop with the classifier to highlight points contributing most to its precision (other performance metrics can be used).

Performance Comparison
We made a number of experiments to evaluate performance of the methods we used. The results are provided in Table 2 over the analyzed BioAssays. Table 2 shows the 5-fold cross-validation comparison results between the different class imbalance solutions. The summary scores in Table 2 are based on averaging the performance over six types of classifiers for each dataset. Another summary results with statistical significance analysis including p-values can be found in S1 Table and the detailed results including other performance metrics can be found in S2 Table. Illustration of generating synthetic instances. A) SMOTE generates the light blue samples by interpolation between a randomly chosen minority sample and k-nearest neighbors. B) DRAMOTE generates the light blue samples by choosing a minority sample based on its importance (i.e. contribution to precision) and the direction towards a safe region. A minority sample (red colored) that is very close to the majority negatives circles will be probably misclassified as a negative one and hence, it should get more support compared to the green colored minority samples. Once a minority sample is chosen, another point needs to be chosen for interpolation. The direction of interpolation can be controlled by choosing a nearest neighbor which is not overlapping with the negative class. This, in turn, helps in providing support for the red colored point while not harming the classifier performance in its surrounding region.  In Table 2, we consider examining more closely sensitivity, precision, F 1 Score and F 0.5 Score [51] for evaluating classification results on HTS types of data. These performance metrics shall better reflect the impact over the data with imbalanced classes [11], but other performance metrics like specificity, GMean, and ROC-AUC are also included in S2 Table. To see where a particular solution stands among all the remaining ones, we also ranked the performance of each of the methods for every classifier based on the F 1 Score. We then averaged the rank position for each of the methods. The method with the lowest score is the best performing. We provide in Table 3 the rank position and averaged rank position for each of the methods. Table 3 clearly demonstrates that overall DRAMOTE and SMOTE were the best performing method in terms of F 1 Score. SMOTE, MWMOTE and DRAMOTE are all methods that generate synthetic data with exactly the same number of new over-sampling points. However, DRAMOTE gives preference to generating points contributing more to the precision of a particular classifier. Results of Table 2 confirm this in all nine datasets, based on the fact that DRAMOTE achieves the highest precision with an improvement of a factor of 2.4 relative to precision of every other method, on average. In three out of nine cases SMOTE achieves the best in terms of F 1 Score and in four cases the second best. For four out of nine datasets, DRAMOTE (compared to other solutions) achieved the highest F 1 Score, while appeared the best in terms of F 0.5 Score for six out of the nine datasets.
Compared to GSVM-RU that was reported as an effective method for PubChem BioAssays [26], DRAMOTE shows a significant improvement in precision for five out of nine datasets, while sacrifices sensitivity significantly as compared to GSVM-RU in only three cases [see S1 Table].

Compounds Interacting with Thyroid Stimulating Hormone Receptor (TSHR)
This section describes a case study for prediction of activity status of FDA drugs with TSHR protein. TSHR is a key protein in the control of thyroid function and belongs to the superfamily of G-protein-coupled receptors (GPCRs) [60]. Thyroid stimulating hormone (TSH) is the main factor responsible for regulating both differentiated function and growth of thyroid follicular epithelial cells [61]. Specifically, BioAssay AID 938 in PubChem database is an assay for finding agonists of the TSHR, which is based on stimulation of cAMP production that causes the cyclic nucleotide gated ion channel (CNG) to open to control for compounds signaling through endogenous receptors and other targets of HEK 293 cells.
The biochemical relevance of the 10 top ranked predictions by DRAMOTE was further indirectly supported by in silico docking results and literature. For this case study, we use the previous results to select a proper solution to preprocess the data and then, build a system based on ensemble of all examined classifiers. Following is the discussion of the results related to this carefully tuned system. Computational prediction and support. The application of DRAMOTE to the TSHR dataset (AID 938) resulted in precision of 81.02% and sensitivity of 91.39%. After building an ensemble of all six trained classifiers, the performance improved by maintaining similar level of precision (~81%) but with a sensitivity of 98.84% (i.e. more than 7% increase in sensitivity).
We investigated the potential interaction of approved drugs from the DrugBank database [35] over TSHR. The ensemble of classifiers trained using BioAssay (AID 938) as its training set, is used to computationally screen approved drugs extracted from DrugBank. We report the top 10 predictions as candidate drugs with strong potential to be interact with TSHR. Table 4 provides a brief description of each drug and highlights their ranking score based on the ensemble system. The drugs, also, docked to TSHR are shown with their corresponding names and structures in S1 Fig. Docking simulations can indirectly support the previous top findings in our data-driven approach. While docking simulations are prone to false positives, the presence of consistent levels in binding values between our findings and the top experimentally ranked interactions reported in AID 938 gives more confidence about our suggested candidates having active interaction status with TSHR. Fig 2 illustrates comparison of the docking scores of the top 10 predictions suggested by DRAMOTE, against two other sets of docking experiments we used as references for evaluation. The two sets of docking experiments include the actual top 10 experimental interactions as ranked and reported in PubChem database for AID 938 and another set (Random set) of 10 randomly selected drugs from the approved list of DrugBank [38]. In Fig 2, the listed energies correspond to the lowest predicted binding energy and they are given in kcal/mol as calculated by AutoDock Vina [62]. Part B of Fig 2 provides the root mean squared distance (RMSD) values of the best poses of each drug compound docked to an activation site in TSHR. While difference is not apparent with regard to the free energy values between all the three sets of docking experiments, the RMSD levels achieved by docking predictions using DRAMOTE are very similar to the levels achieved by the experimentally validated ones as compared to the Random set. Detailed docking procedure and scores are provided in S4 Text.
A literature review of our top predictions points out that Tasosartan (third ranked prediction) and Forasartan (eighth ranked prediction) are both angiotensin II receptor antagonist. These drugs are used to treat hypertension [63] and are known to block the renin-angiotensin system thereby protecting the kidney from damage caused by increased kidney blood pressure  [64]. Several studies have demonstrated a positive correlation between high blood pressure and the concentration of thyroid stimulating hormone [65][66][67]. Literature review for remaining top ranked drugs can be found in S5 Text. These findings strengthen our proposition that the proposed top 10 predictions could be candidate drugs for interacting with TSHR. In order to show that DRAMOTE can be used for drugs for different diseases other than those related to TSHR, an additional top ranked list is included in S6 Text for 17beta-Hydroxysteroid Dehydrogenase Type 10 (17β-HSD10) as the protein target in AID 886 assay. The expression level of this protein is elevated in the brains of Alzheimer's disease patients [68]. Thus, the predicted/suggested drugs (S6 Text) could serve as potential drugs for Alzheimer's disease aimed to inhibit expression of 17β-HSD10 since AID 886 assay is testing inhibition of 17β-HSD10.

Conclusions
In this study, we extensively compare several state-of-the-art methods that handle class imbalance problem based on advanced sampling techniques. The results based on approximately 500,000 interactions suggest that DRAMOTE can be used for developing robust virtual screening models to recognize candidate chemical compounds for potential activity with specific molecular targets in specific assays. Moreover, we applied DRAMOTE to screen for drugs likely to interact with the TSHR as a case study and we presented the top 10 drugs that potentially interact with TSHR along with indirect supporting evidence of their validity from literature and simulated 3D docking.


extfieldsofstudy: Physics Medicine

text:
Exposure to Radiofrequency Radiation Emitted from Common Mobile Phone Jammers Alters the Pattern of Muscle Contractions: an Animal Model Study.

Introduction The rapid growth of wireless communication technologies has caused public concerns regarding the biological effects of electromagnetic radiations on human health. Some early reports indicated a wide variety of non-thermal effects of electromagnetic radiation on amphibians such as the alterations of the pattern of muscle extractions. This study is aimed at investigating the effects of exposure to radiofrequency (RF) radiation emitted from mobile phone jammers on the pulse height of contractions, the time interval between two subsequent contractions and the latency period of frog’s isolated gastrocnemius muscle after stimulation with single square pulses of 1V (1 Hz). Materials and Methods Frogs were kept in plastic containers in a room. Animals in the jammer group were exposed to radiofrequency (RF) radiation emitted from a common Jammer at a distance of 1m from the jammer’s antenna for 2 hours while the control frogs were only sham exposed. Then animals were sacrificed and isolated gastrocnemius muscles were exposed to on/off jammer radiation for 3 subsequent 10 minute intervals. Isolated gastrocnemius muscles were attached to the force transducer with a string. Using a PowerLab device (26-T), the pattern of muscular contractions was monitored after applying single square pulses of 1V (1 Hz) as stimuli. Results The findings of this study showed that the pulse height of muscle contractions could not be affected by the exposure to electromagnetic fields. However, the latency period was effectively altered in RF-exposed samples. However, none of the experiments could show an alteration in the time interval between two subsequent contractions after exposure to electromagnetic fields. Conclusion These findings support early reports which indicated a wide variety of non-thermal effects of electromagnetic radiation on amphibians including the effects on the pattern of muscle extractions.

mals in the jammer group were exposed to radiofrequency (RF) radiation emitted from a common Jammer at a distance of 1m from the jammer's antenna for 2 hours while the control frogs were only sham exposed. Then animals were sacrificed and isolated gastrocnemius muscles were exposed to on/off jammer radiation for 3 subsequent 10 minute intervals. Isolated gastrocnemius muscles were attached to the force transducer with a string. Using a PowerLab device (26-T), the pattern of muscular contractions was monitored after applying single square pulses of 1V (1 Hz) as stimuli.

Introduction
T he fast-growing telecommunication and wireless technologies and more dependence to these communication devices have led to higher levels of exposure to electromagnetic radiations emitted by different sources such as cellular phones and their base stations. Mobile phone jammers are devices that emit radiofrequency radiation in the same frequencies that mobile phones operate to disable signaling in www.jbpe.org Rafati A. et al places where silence is valued or where information quarantine measures are required such as examination halls, prisons, or military centers. Rapid growth of wireless communication technologies, has led to public concern regarding the biological effects of electromagnetic radiations on human health. Understanding the effects of exposure of humans or higher animals to electromagnetic fields (EMFs) is clearly associated with the understanding of the targets of these fields in exposed cells and tissues. However, it is still unclear what these targets are and how they may cause complex biological responses to very low-energy nonionizing radiations. It is puzzling that low-energy photons (in energy ranges which usually cannot individually alter the chemistry of the cell) can lead to non-thermal effects in the irradiated cells [1]. The function of brain and nervous systems is based on using electrical signals. Hence, brain and nervous systems can be particularly vulnerable to low frequency EMFs and the electric fields and currents which are induced during exposure. It is worth mentioning that some of the studies performed on animal models have shown that the bioeffects of exposure to electromagnetic fields can be mediated by the nervous system. These studies have revealed that several biochemical changes in nervous systems (alterations in water, oxygen, calcium and some regulatory peptides such as serotonin and histamine) are associated with exposure to electromagnetic fields.
Over the past years, our laboratory has focused on studying the health effects of exposure of laboratory animals and humans to some common and/or occupational sources of electromagnetic fields such as mobile phones [2][3][4][5][6][7][8][9] and their base stations [10], mobile phone jammers [11], laptop computers [12], radars [3], dentistry cavitrons [13] and MRI [14 15]. This study is aimed at investigating the effects of exposure to radiofrequency radiation emitted by a mobile jammer on the pulse height of contractions, the time interval between two subsequent contractions and the latency period of frog's isolated gastrocnemius muscle after stimulation with single square pulses of 1V (1 Hz)

Animals
Frogs of both sexes (20-30 g) were obtained from Animal Lab of the Physiology Department, SUMS. Animals were kept in plastic containers in a room (20 ± 1 °C) for one week before the experiments. The tap water in the plastic containers was changed 2 times a week.

Exposure
Control frogs were kept in special cages during the sham exposure phases, while the jammer group was exposed to radiofrequency (RF) radiation emitted from a common Jammer at a distance of 1m from the jammer's antenna for 2 hours. Isolated gastrocnemius muscles, in the next stage, were exposed to on/off jammer radiation for 3 subsequent 10 minute intervals.

Experiment Setup
Frogs were double pithed using a needle. Then the skin over the gastrocnemius muscle and its distal tendon was removed. Isolated gastrocnemius muscles were attached to the force transducer with a string. Nerve and muscle stimulations were performed separately. Using a PowerLab device (26-T), the pattern of muscular contractions was monitored after applying single square pulses of 1V (1 Hz) as stimuli. The pulse height of contractions, the time interval between two subsequent contractions and the delta (latency; the time interval between stimulus and response) were measured.

Statistical Analysis
All statistical analyses were performed using Statistical Package for the Social Sciences (SPSS, ver: 19.0), and the comparisons of the means of the physiological parameters were conducted using non-parametric Kruskal Wallis and Mann-Whitney tests. The statistical significance was considered as P<0.05. Table 1 shows the pulse height of contractions, the time interval between two subsequent contractions and the latency period in non-exposed control group after in-vivo sham-exposure, and when isolated gastrocne-mius muscles were sham-exposed to mobile jammer radiation. As it was expected, no statistically significant differences were observed in different sham exposure phases.

Muscle Stimulation
The pulse height of contractions, the time interval between two subsequent contractions and the latency period in moboile phone jammer group after in-vivo exposure, and when isolated gastrocnemius muscles were exposed to mobile jammer radiation are summarized in Table 2. We observed a statistically significant difference among the latency periods in different off/on exposure phases (P=0.0001). The Table 1: The pulse height of contractions, the time interval between two subsequent contractions and the latency period in non-exposed Mobile Phone Jammers group after in-vivo shamexposure, and when isolated gastrocnemius muscles were sham-exposed to mobile phone Jammers (muscle stimulation).  mean (±SD) latency period after a 2 hour invivo exposure was 0.006 ± 0.001 seconds but when isolated gastrocnemius muscles were in 10 min on/off/on exposure phases, the latencies were 0.009 ± 0.001, 0.004 ± 0.001, 0.008 ± 0.001 seconds, respectively. The inter-group comparison could not show any statistically significant differences in pulse height and time interval between two subsequent contractions (Figure 1-2).However, a statically significant difference was found in the latency (Figure 3). Table 3 shows the pulse height of contractions, the time interval between two subse-quent contractions and the latency period in non-exposed control group after in-vivo sham-exposure, and when isolated gastrocnemius muscles were sham-exposed to mobile jammer radiation. In contrast with what was expected, a statistically significant difference was observed for the pulse height of contractions in different sham exposure phases (P=0.033).

Nerve Stimulation
The pulse height of contractions, the time interval between two subsequent contractions and the latency period in mobile jammer group after in-vivo exposure, and when isolated gastrocnemius muscles were exposed to mobile jammer radiation are summarized in Table 4. In this part of the experiment, we observed only a statistically significant difference among the latency periods in different off/on exposure phases (P=0.0001). The mean (±SD) latency period after a 2 hour in-vivo exposure was 0.006 ± 0.001 seconds but when isolated gastrocnemius muscles were in 10 min on/off/on exposure phases, the latencies were 0.009 ± 0.001, 0.006 ± 0.001, 0.009 ± 0.001 seconds, respectively. The inter-group comparison could not show any statistically significant differences in pulse height and time interval between two subsequent contractions ( Figure 4-5). However, a statically significant difference was found in the latency ( Figure 6). Table 5 shows the calculated SAR values (W/kg) for 3G, CDMA, DCS and GSM jammer's antenna.

Discussion
In this study it is revealed that the pulse height of contractions in frog's gastrocnemius muscle cannot be affected by the exposure to electromagnetic fields. However, our experiments showed that the latency period can be easily altered by the irradiation. However, none of the experiments could show an alteration in the time interval between two subsequent contractions after exposure to electromagnetic fields. The general pattern which could be observed in these experiments indicates that exposure to electromagnetic fields usually increase the latency period. To the best of our knowledge, this is the first report that shows the role of exposure to electromagnetic fields generated by mobile phone jammers on the pulse height of contractions, time interval between two subsequent contractions and the latency period in frog's gastrocnemius muscle after applying single square pulses of 1V (1 Table 3: Panel A: The pulse height of contractions, the time interval between two subsequent contractions and the latency period in non-exposed control group after in-vivo sham-exposure, and when isolated gastrocnemius muscles were sham-exposed to Mobile Phone Jammers (Nerve stimulation). Panel B: Pairwise comparisons of the pulse height of contractions.     Hz) as stimuli.
Generally speaking our results are in line with numerous reports which showed that a wide variety of cells including epithelial, endothelial and epidermal cells, cardiac muscle cells, fibroblasts, yeast, E. coli, developing chick eggs, and dipteran cells respond to EMF, both in vivo and in vitro [16]. It has been reported that tissue cultured cells are less susceptible to the effects of EMF, possibly due to this point that immortalized cells have been altered in an important manner to enable them to live limitlessly in unnatural laboratory conditions. However, our results are in contrast with some old reports which indicated that exposure of the isolated frog sciatic nerves, cat saphenous nerves, rabbit vagus nerves, superior cervical ganglia, and rat diaphragm muscles to 2450 MHz microwave radiation cannot change the characteristics of nerves or muscles exposed to CW specific absorption rate (SAR) of 0.3-1500 W/kg and pulsed peak SAR of 0.3-220 kW/kg. [17] Our findings support early reports which indicated the non-thermal effects of electromagnetic radiation on amphibians [18 19]. Some early reports have indicated that exposure of amphibians to electromagnetic fields may cause teratogenic effects [20]. On the other hand, it is reported that common frogs (Rana temporaria L.) developed under electromagnetic field shows a higher rate of mortality, slow and less synchronously development, allergies, and alterations in their blood counts [21].
It has been also reported that exposure of frogs to electromagnetic radiation with power densities of 30-60 mWcm-2 caused a heart rhythm change, possibly due to activation of the nervous system. Furthermore, an increase in the heart rate and arrhythmia after irradiation of toad hearts with pulses of 1425 MHz at a power density of 0.6 mWcm-2, has been reported [18]. Interestingly, in Spain A. Balmori in a paper entitled "The incidence of electromagnetic pollution on the amphibian decline: Is this an important piece of the puzzle?" published in the journal Toxicological and Environmental Chemistry in 2006 reports the possible association of exposure to microwave radiation and the global disappearance of frogs [22].


extfieldsofstudy: Biology Medicine

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The anti-Candida activity by Ancillary Proteins of an Enterococcus faecium strain

An antimycotic activity toward seven strains of Candida albicans was demonstrated erstwhile by a wild-type Enterococcus faecium isolated from a penguin rookery of the Antarctic region. In the present study the antimicrobial principle was purified by ion exchange and gel permeation chromatography and further was analyzed by LC-ESI-MS/MS. In the purification steps, the dialyzed concentrate and ion exchange fractions inhibited C. albicans MTCC 3958, 183, and SC 5314. However, the gel filtration purified fractions inhibited MTCC 3958 and 183. The data obtained from the LC-ESI-MS/MS indicate that the antimicrobial activity of the anti-Candida protein produced by E. faecium is facilitated by Sag A/Bb for the binding of the indicator organism's cell membrane. Partial N-terminal sequence revealed 12 N-terminal amino acid residues and its analysis shown that it belongs to the LysM motif. The nucleotide sequence of PCR-amplified product could detect 574 nucleotides of the LysM gene responsible for binding to chitin of the cell wall of Candida sp.

Introduction
Antimicrobial and antimycotic peptides are small cationic and amphipathic molecules, generally with fewer than 50 amino acids. These peptides are omnipresent and have been isolated from prokaryotes and eukaryotes in plants, bacteria, fungi, and animals (Zasloff, 2002;Bulet et al., 2004).
Amongst Lactic Acid Bacteria (LAB), members of the genus Enterococcus are widely distributed throughout nature as inhabitants of the gastrointestinal tract of humans and other animals and are also present in vegetables, plant materials and foods (Giraffa, 2002). Many LAB bacteriocins, particularly those produced by enterococci (enterocins), are characterized by their broad range of activity against many gram-positive bacteria (van Belkum and Stiles, 1995;Nes et al., 2007). Bacteriocins may also play an important role in maintaining bacterial community structures (Riley and Wertz, 2002) in specific ecological niche. They have also been proposed as probiotics for both the gastrointestinal and urogenital tracts (Redondo-Lopez et al., 1990). One of the main mechanisms used by the LAB to interfere with the colonization of pathogens and avoid proliferation of those potential pathogens is production of antimicrobial agents, such as organic acids, hydrogen peroxide and bacteriocins or related substances (Redondo-Lopez et al., 1990;Jack et al., 1995).
Though the vast majority of enterocins produced by Enterococcus faecalis and Enterococcus faecium are found active only against Gram-positive bacteria (Larsen, 1993), some exceptions with broad activity spectra have been described in recent years to show the ability of bacteriocins to inhibit the Gram-negative microorganisms (De Kwaadsteniet et al., 2005;Line et al., 2008). Reports on antifungal prowess of E. faecium or E. faecalis are relatively rare. It was demonstrated in one of the previous investigations that the antifungal compounds such as phenyllactic acid and 4-hydrophenyllactic acid were produced by Lactobacillus plantarum (Lavermicocca et al., 2000). Besides this, bacteriocin-like substances and other compounds were produced by L. pentosus, L. coryniformis, and E. faecalis (Okkers et al., 1999;Magnusson and Schnurer, 2001;Lupetti et al., 2002).
Candida albicans is the major human fungal pathogen of immunocompromised patients (Lupetti et al., 2002). The nature of the resistance to a few drugs has been identified as related to altered transport, modification of an enzyme, and a change in membrane composition (Lupetti et al., 2002). Additionally, antimicrobial peptides are promising candidates for the treatment of fungal infections since they have both mechanisms of action distinct from available antifungal agents and the ability to regulate the host immune defense systems as well (Lupetti et al., 2002). Probiotic strains that can be used for the treatment of vulvo-vaginal infection should be able to produce metabolites that are fungistatic for C. albicans and C. glabrata (Strus et al., 2005).
Though an umpteen number of peptide bacteriocins from Enterococci have been purified and genetically characterized over the last several years, yet the anti-Candida peptide/proteins are less investigated from the E. faecalis and E. faecium. Due to the increasing frequency of fungal infections in immunocompromised patients and development of an ominous trend in the treatment failures amongst the candidiasis patients receiving long-term antifungal chemotherapy resulted from the rapidly acquiring multidrug resistance (MDR) amongst pathogenic C. albicans a strong and pressing need has been felt in finding alternative form of antifungal antibiotics (Matejuk et al., 2010).
In this study, the APR210, the anti-Candida factor producer strain that was earlier identified as E. faecalis based on the biochemical tests and fatty acid methyl ester (FAME) analysis got redisgnated as E. faecium based on rDNA sequence analysis (Shekh et al., 2011), biochemical tests (e.g., L-Arabinose+, Raffinose+, Melibiose and Sorbitol−), and PCR-amplification data obtained using the E. faecium-specific primers (Cheng et al., 1997) and the genomic DNA of the producer strain. In this study the anti-Candida protein produced by the wild type E. faecium was gel-filtration purified and the peptides responsible for the anti-Candida activity in the pooled active fractions was identified by LC-ESI-MS/MS Supplementary 1 (Image 1-a gel picture).

Results
We attempted to purify the antimicrobial protein by using crude proteins in the cell free supernatant. A three-step method was followed that included salting-out by ammonium sulfate fractionation, ion exchange and gel filtration chromatography. The fractions collected from each step of purification was checked for the antimicrobial activity by cut-well agar assay using three C. albicans strains that are different in drug-resistance pattern and are from different sources. The protein band that produced the anti-Candida activity in the zymogram assay was subjected to the LC-ESI-MS/MS analysis. The PCR-amplified product corresponding to the LysM domain gene was sequenced and analyzed.

Gel Filtration of the Antimycotic Protein
Dialyzed concentrate (after ammonium sulfate fractionation and dialysis) showed a clear zone of inhibition against C. albicans SC 5314 ( Figure 1A) and MTCC 3958 (photo not shown). Prior to gel filtration, the ion exchange fractions were tested against C. albicans MTCC 3958, 183, and SC 5314. The ion exchange fractions showed clearly the antimicrobial activity against these three yeast strains. However, we have shown here the activity against SC 5314 only ( Figure 1B). The Sephadex G-75 gel filtration chromatography showed that the recovery of antifungal activity was approximately 22-fold (Shekh and Roy, 2012) with a reduction of 45%. The chromatogram of fractions collected during gel filtration on Sephadex G-75 is shown in Figure 2. The   fractions were tested against C. albicans MTCC 3958, MTCC 183, and SC 5314. It was observed that fractions between 31 and 36 showed antimicrobial activity against MTCC 3958 and MTCC 183 (Figure 2 inset), whereas no zone of inhibition was recorded against SC 5314. These active fractions were pooled for further processing. As observed in Figure 3A, 10% SDS-PAGE fractionation indicated the presence of a protein band from ion exchange and gel filtration fractions that showed antimicrobial activity in the zymogram assay of Tricine Native PAGE ( Figure 3B). Minimum inhibitory concentration (MIC) values against MTCC 183 and 3958 were 133 and 267 µg/ml respectively as determined in the earlier study (Shekh and Roy, 2012).

PCR-Amplification from the N-Terminal Sequence
N-terminal amino acid analysis of the ion-exchange purified antimicrobial protein revealed the following partial sequence: NH2-DEVYTVKSGDSL (Shekh and Roy, 2012). Homology search was performed using NCBI BLAST (http://blast.ncbi.nlm. nih.gov/). The sequence DEVYTVKSGD displays high similarity (as evident from the e-values) to LysM domain containing protein of most of the Enterococcus species 29 amino acids residues away from the N-terminus of the LysM domain protein (sequences similarity and alignments are shown below). This sequence demonstrated identity with LysM domain proteins of six E. faecium strains.
The forward (34-50) and reverse (592-610) primers were designed based on the N-terminal and C-terminal sequence of the LysM domain respectively. The 574 bp amplicon (Figure 4) was obtained by PCR and was further sequenced and the sequence analysis revealed 94.7% similarity with the 574 bp LysM domain-encoding gene of the strain E. faecium DO chosen for the PCR amplification. The nucleotide sequence that encodes 12 amino acids (DEVYTVKSGDSL) previously found as a result of N-terminal sequencing was detected.

Identification of the Protein by LC-ESI-MS/MS
The gel-filtration purified band that showed anti-Candida activity in zymogram assay ( Figure 3B, right side plate), was subjected to ESI-MS/MS analysis. The resulting protein sequence was blasted against the NCBI database with the best overall match showing 30 % identity with the NlpC/P60 family protein Tax_Id=791161 protein of E. faecium [PC4.1]. Out of 509 amino acids of the NlpC/P60, 149 amino acids were detected by LC-MS/MS and the subsequent data analysis revealed that 30% sequence match with the NlpC/P60 family protein (Figure 5). Based on the LC-ESI-MS/MS, the peaks corresponding to (NQQADAQSQIDALESQVSEIN-TQAQDLLAK ( Figure 6C), DIADLQER, VQAMTTMVK, and TSLAAEQATAEDKK) were obtained in the mass spectra.

Discussion
A number of species belonging to the genus Enterococcus have been reported to synthesize bacteriocins active against Gram-positive and negative bacteria. However, the reports on the anti-Candida activity of Enterococcus are rather rare.
In the present investigation, the anti-Candida substance was purified to a near homogeneity from the cell free supernatants via a three-step purification protocol. Sephadex G-75 gel filtration fractions containing nearly purified protein showed antimicrobial activity against C. albicans MTCC 3958 and MTCC 183 (Figure 2).
Based on the 12 amino acid residues from the N-terminal sequence determined in the previous study (Shekh and Roy, 2012), primers were designed and the PCR-amplified fragment of size 574 bp obtained (Figure 4) using the purified genomic DNA (as template) of the producer strain was sequenced; the nucleotide sequence was analyzed by BLAST using the NCBI search. The deduced amino acid sequence from the nucleotide sequence generated from the PCR amplified fragment (Figure 4) reveals the LysM motif that includes the DEVYTVKSGDSL. In a mutational analysis study conducted by Onaga and Tiara (2008), the LysM domain of PrChi-A was found to bind a chitin contributing significantly to the antifungal activity mediated by PrChi-A through their binding activity. The N-terminal sequence of PrChi-A was determined as DCTTYTVKSGDTCYAISQAN. This sequence is not homologous to the sequence of any plant chitinase, but very interestingly shares homology with the LysM of other proteins from several organisms (Onaga and Tiara, 2008). The nucleotide sequence encoding the LysM motif and the N-terminal sequence belonging to the LysM motif derived from the protein band that produced the zone of inhibition in zymogram (in-gel assay) in our study revealed striking similarity with the PrChi-A that exhibited antifungal activity (Onaga and Tiara, 2008). The shaded serine amino acid residue present adjacent to YTVKSGD is of same nature since threonine is the hydroxylated version of serine. However, the CYA amino acid residues in the N-terminal sequence of LysM of PrChi-A was not found in the translated sequence deriving from the nucleotide sequence of the PCR-amplified product; this CYA was also not found in the LysM motif sequence of E. faecium DO; however the pink shaded amino acid stretch ISQ matched with the LysM motif. LysM is a protein domain of about 45 amino acids found initially in several bacterial autolysin proteins (Joris et al., 1992). The domains are also known to bind N-acetylglucosamine (GlcNAc)-containing glycan molecules including peptidoglycan from several bacteria and chitin from fungi (Bateman and Bycroft, 2000;Buist et al., 2008;Iizasa et al., 2010;Petutschnig et al., 2010). In the plant kingdom, LysM domains are found in receptors for chitooligosaccharide and related compounds. The PrChi-A was reported to bind to chitin in the fungal cell wall mainly through LysM domains and then it degraded the chitin by hydrolytic action. This led to disruption of the fungal cell wall and fungal growth inhibition (Onaga and Tiara, 2008).
The LC-ESI-MS/MS generated the spectrum of separated peaks (Figures 6A-C) showing three abundant peaks, consistent with the molecular form of the purified protein. The resulting Frontiers in Microbiology | www.frontiersin.org protein sequence was blasted against the NCBI database with the best overall match showing 30 % identity with the NIpC/P60 family (Anantharaman and Aravind, 2003). In mass spectrometry analysis, the protein sequence matched upto 30% with NlpC/P60 family protein present in the MASCOT database (Figures 6A-C, 7A-C). The Figures 7A-C, depict the dendrogram presentations of the multiple alignments of the deduced amino acid sequences of three peptides of NipC/P60 of E.faecium and other closely related strains. NlpC/P60 is a large family of cell-wall related cysteine peptidases that are broadly distributed in bacteria, archaea and eukaryotes (Anantharaman and Aravind, 2003). While their biochemical function seems to be conserved, the physiological roles of NlpC/P60 proteins are diverse, including cell separation, expansion, differentiation, cell-wall turnover, cell lysis, protein secretion and virus infection (Xu et al., 2010). Multiple proteins with NlpC/P60 domains are found in individual Gram-positive bacteria (Humann and Lenz, 2009). SagA of E. faecium belongs to this NIpC/P60 and is a secreted antigen which binds to the extracellular matrix proteins (Teng et al., 2003). Teng et al. (2003) reported the Nterminal sequencing of the fibrinogen-binding protein revealing a 20-amino-acid sequence, DFDSQIQQQDQKIADLKNQQ, identical to the predicted N-terminal sequence of the mature SagA (Teng et al., 2003). In our study, the significant peptides NQQADAQSQIDALESQVSEINTQAQDLLAK, DIADLQER, VQAMTTMVK, and TSLAAEQATAEDKK detected were matched with secreted antigen Sag A/SagBb proteins produced by the E. faecium strain. NlpC/P60 proteins are often fused to auxiliary domains, many of which are known cell-wall binding modules (e.g., LysM domain) (Xu et al., 2010). These auxiliary domains may be thought to function as targeting domains which localize their proteins to the cell wall (Xu et al., 2010). The species designation of E. faecium isolates was confirmed by amplification of specific DNA sequences by PCR. The results obtained in this study reveal a so far not described function for enterococcal LysM domain protein and taken together our findings clearly indicate the presence of this auxiliary domain in the form of LysM domain and NIpC/P60. However, the functional synergy between the NlpC/P60 domains and their auxiliary domains (Xu et al., 2010) needs to be investigated further to establish the rationale of our findings in the present study. Extracellular E. faecium Sag A/Bb protein that is antigenic in nature is apparently essential for growth and shows broadspectrum binding to extracellular matrix (ECM) proteins forming oligomers (Humann and Lenz, 2009) whereas the secreted protein sspA or sspB produced by Streptoccoccus sp. was reported to adhere to collagen type I and C. albicans (Teng et al., 2003). Insertion inactivation experiments have shown that both sspA and sspB genes are necessary for the binding of S. gordonii cells to C. albicans (Holmes et al., 1996). The SspB protein, when expressed on the surface of E. faecalis, confers upon the enterococcal cells the ability to bind C. albicans (Teng et al., 2003). In the same study conducted by Teng et al. (2003) the gene in clones d1-27 and d2-29 named Sag A for major secreted antigen was found in all 11 E. faecium strains from different communities and/or from different geographic sources (Teng et al., 2003).
The results obtained in the present work are in partial agreement with the earlier works. The identified peptide sequences derived from the LC-ESI-MS/MS match partly with the secreted antigen A/Bb that might impart binding ability toward the extracellular matrix present on the cell surface of C. albicans and the bacteriocin secretion accessory proteins might play an important role in the antimicrobial activity of the producer strain. The SagA (often called P60), P54 from E. faecium and SagBb secreted by Enterococcus hirae (Teng et al., unpublished data;Muller et al., 2006) have been reported to be associated with the cell wall biosynthesis; however these secreted proteins have the likelihood in mediating cell wall hydrolysis to indicate that apart from the binding to the cell surface of C. albicans, Sag A/Bb might play a role in antimicrobial activity indirectly.

Bacterial Strains, Growth Conditions, and Media
The producer test strain was routinely propagated in TGYE (tryptone, 5 g l −1 ; glucose, 1 g l −1 ; yeast extract, 3 g l −1 , pH 7.2 ± 0.4) medium and was grown at 14 ± 0.5 • C. The producer strain E. faecium and three indicator strains (C. albicans MTCC 3958, MTCC 183, and SC 5314) used in the present study were maintained in glycerol stock at −70 • C (Shekh et al., 2011) and subcultured as and when required.

Gel-Filtration of ACP
The anti-Candida protein (ACP) was partially purified from supernatant of cultures of E. faecium using ammonium sulfate fractionation and ion exchange chromatography (Shekh and Roy, 2012) which was followed by gel filtration chromatography. The dialyzed sample was loaded onto a DEAE-Sepharose Fast flow column (GE Healthcare) equilibrated with 20 mmol sodium phosphate buffer, pH 8.0. The fractions were eluted using a linear gradient of 0-0.30 M NaCl (Shekh and Roy, 2012). The ion exchange fractions exhibiting the anti-Candida activity were pooled and further purified by gel permeation chromatography. Three grams of Sephadex G-75 were soaked in 200 ml of sterile distilled water and washed three times for removing fine particles, and then dissolved in 20 ml of sterile distilled water and poured in a 1 × 50 cm column. Void volume was determined by passing blue dextran (2000 kDa) through the column. The pooled ACP fractions (2.0 ml) were loaded onto the gel filtration column. The above mentioned buffer was used to elute the fractions each of 1.5 ml; those fractions were collected at a flow rate of 65 ml h −1 and read at 280 nm using UV-Visible spectrophotometer (Shimadzu). Antimicrobial assay for all fractions was performed against the freshly grown C. albicans MTCC 3958, MTCC 183 and SC 5314 by using cut-well agar assay (Shekh et al., 2011). The active fractions were concentrated by U-tube concentrator. That facilitated the removal of small molecules and also any salts present. The concentrated samples (100 µL) were added into a well of diameter 7.0 mm made in a freshly prepared MGYP agar plate which was seeded with freshly grown (diluted to 10 6 -10 7 cells/ml) C. albicans. After 48 h of luxurious growth of C. albicans at 37 • C, the plates were inspected for the zone of inhibition and the zone diameter was measured in terms of millimeter.

Tricine SDS-PAGE
Samples were separated on a one dimensional SDS-PAGE. Slabs of 10% polyacrylamide (acrylamide/bisacrylamide, 30:0.8) with a 5% stacking gel were electrophoresed at 100 mV until the bromophenol dye reached the bottom of the gel. The gel was viewed after silver staining.

Direct Detection of Biological Activity on Tricine PAGE
Tricine Native-PAGE (10%) (Schagger and Von Jagow, 1987) followed by a gel overlay was performed with active pooled fractions from gel filtration. After electrophoresis for 2 h at 20 mA, 2 duplicate gels were cut. One of the gels was silver stained. The other gel was fixed in 20% (v/v) isopropanol and 10% (v/v) acetic acid for 30 min, rinsed with 500 ml of MilliQ water for 1 h, and placed aseptically on an MGYP plate. To identify the active peptide band, the Tricine gel containing pooled active fraction was overlaid by freshly grown C. albicans MTCC 3958. After the agar solidified, the plate was incubated at 37 • C for 48-72 h until C. albicans grew uniformly over the plate or an inhibition zone was observed.

N-Terminal Amino Acid Sequence Analyses
The protein band that showed inhibition in the in-gel assay in Tricine-Native PAGE was further subjected to N-terminal amino acid sequencing using an ABI Procise 494 protein sequencer (Applied Biosystems), Iowa State University, US (Shekh and Roy, 2012).

PCR Amplification and Nucleotide Sequencing
Based on the N-terminal sequence (Joris et al., 1992) of the LysM domain of the E. faecium, forward and reverse primers were designed. The primers used were: forward primer 5 ′ ACT TTT GCT GCT GGT GC 3 ′ and reverse primer 5 ′ TTA GTA CCA GCC GTT TGC 3 ′ ; a 25 µl of reaction consisted of 100 ng of purified genomic DNA of the producer strain, forward and reverse primers (30 picomoles each) and 200 µM of dNTPs each, and 1.5 units of Taq polymerase. The PCR conditions consisted of an initial denaturation step at 94 • C for 90 s, followed by 35 cycles of denaturation 60 s at 94 • C, annealing at 50 s at 48 • C, and 50 s at 72 • C. The final extension step was at 72 • C for 3 min. The nucleotide sequence was determined on both strands by using Big Dye Terminator chemistry and ABI 3500 × L Genetic Analyzer. The sequence data was evaluated on the basis of sequence homology to GenBank entities using BLASTN and was analyzed using the open reading frame finder of NCBI.

Mass Spectrometry of the Gel-Filtration Purified Protein
The gel filtration purified fractions that contained anti-Candida principle were pooled and resolved using the SDS-PAGE. The band in the stained gel was precisely cut and used for LC-ESI-MS/MS (Proteomics International, Australia). Protein samples were trypsin digested and peptides extracted according to standard techniques (Xu et al., 2010). Peptides were analyzed by electrospray ionization mass spectrometry using the Ultimate 3000 nano HPLC system [Dionex] coupled to a 4000 Q TRAP mass spectrometer [Applied Biosystems]. Tryptic peptides were loaded onto a C18 PepMap100, 3 µm [LC Packings] and separated with a linear gradient of water/acetonitrile/0.1% formic acid (v/v). Spectra were analyzed to identify proteins of interest using Mascot sequence matching software [Matrix Science] with Ludwig NR database. Spectra were obtained for the major peptide ions in MS mode and sequence data obtained when the spectrometers automatically reverted to MS/MS mode. These spectra were then compared with databases (MASCOT and Lud-wigNR) to provide hits that could identify matching or similar sequences. The amino acid sequences were identified using the tool provided by the National Center for Biotechnology Information (NCBI) and ORF Finder tool (www.bioinformatics.org/ sms/orf=find.html) for the sequences obtained. The translated ORFs were compared to known sequences deposited in the nonredundant protein databases (www.ncbi.nlm.nih.gov) using the BLAST program (Shekh and Roy, 2012). Multiple alignments were performed with the CLUSTAL W proGram (Altschul et al., 1993) Supplementary Material Images 2-4.


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Intracellular Immunohistochemical Detection of Tetrodotoxin in Pleurobranchaea maculata (Gastropoda) and Stylochoplana sp. (Turbellaria)

Tetrodotoxin (TTX), is a potent neurotoxin targeting sodium channels that has been identified in multiple marine and terrestrial organisms. It was recently detected in the Opisthobranch Pleurobranchaea maculata and a Platyhelminthes Stylochoplana sp. from New Zealand. Knowledge on the distribution of TTX within these organisms is important to assist in elucidating the origin and ecological role of this toxin. Intracellular micro-distribution of TTX was investigated using a monoclonal antibody-based immunoenzymatic technique. Tetrodotoxin was strongly localized in neutral mucin cells and the basement membrane of the mantle, the oocytes and follicles of the gonad tissue, and in the digestive tissue of P. maculata. The ova and pharynx were the only two structures to contain TTX in Stylochoplana sp. Using liquid chromatography-mass spectrometry, TTX was identified in the larvae and eggs, but not the gelatinous egg cases of P. maculata. Tetrodotoxin was present in egg masses of Stylochoplana sp. These data suggest that TTX has a defensive function in adult P. maculata, who then invest this in their progeny for protection. Localization in the digestive tissue of P. maculata potentially indicates a dietary source of TTX. Stylochoplana sp. may use TTX in prey capture and for the protection of offspring.

Introduction
Tetrodotoxin (TTX) is a potent non-protein neurotoxin that selectively targets and blocks voltage-gated sodium channels. It is most notably found in the tissues of pufferfish species from the Tetraodontidae family [1,2]. Tetrodotoxin is fatal to humans (wt. 50 kg) at levels of just 1-2 mg [3]. Tetrodotoxin was initially thought to only occur in pufferfish, but has since been discovered in a growing number of organisms including frogs, newts, gastropods, crabs, an algae species, arrow worms, and land planarians [2,[4][5][6].
Numerous researchers have suggested that the incidence of TTX in so many genetically unrelated organisms is due to an exogenous source such as symbiotic bacterial production or bioaccumulation through diet [2,[7][8][9]. Bioaccumulation of TTX has been implicated in several instances in which prey animals have also been shown to contain TTX, or during captive studies where organisms removed from their natural environments lose their toxicity [1,[10][11][12]. In contrast, bacterial production of TTX has also been reported in marine organisms including the gastropod Niotha clathrata [13], the blue-ringed octopus Octopus maculosus (i.e., Hapalochlaena maculosa) [14], and the pufferfish Fugu vermicularis vermicularis [15]. However, concentrations of toxin produced by isolated bacterial strains are generally orders of magnitude lower than host organisms, suggesting bacterial production is unlikely to be the sole source of toxin [7,8,16]. In contrast, studies on terrestrial newts (Taricha granulosa) showed an increase in toxin concentrations when kept in captivity and the ability to regenerate TTX after the release of toxin through the skin, suggesting an endogenous source [17,18]. Currently the definitive origin of TTX remains debated, and strategies for acquisition most likely vary among species.
Studies using chemical methods to detect TTX have shown sequestration of toxin varies among tissue types in many organisms [2]. For example, in the pufferfish Takifugu niphobles high concentrations of TTX were present in the liver, ovaries, and intestines, while skin and muscle tissues only had low concentrations [1]. Micro-distribution of TTX has been demonstrated using TTX specific monoclonal antibody (mAB) immunoenzymatic techniques in newts [19,20], ribbon and flat worms [21], pufferfish [22][23][24], and octopuses [25]. Understanding the accumulation and sequestration of TTX at the cellular level provides additional information regarding the ecological functions of TTX. For example, in predator-prey trials conducted by Williams et al. [26] using the rough-skinned newts, Taricha granulosa and their natural predator the garter snake Thamnophis sirtalis, it was shown that rejected newts possessed significantly higher concentrations of TTX in the skin compared to those that were consumed.
In 2009, the opisthobranch Pleurobranchaea maculata was found to contain high concentrations of TTX when a number of dogs became ill after consuming beach-cast individuals in New Zealand [27]. Subsequent studies using liquid chromatography-mass spectrometry (LC-MS) revealed that only the TTX variant was present. The highest concentrations of TTX were in the mantle, gonad, and digestive tissue, with total TTX concentrations (highest average (ave.) 369 mg· kg −1 ) varying significantly between individuals and season [12,28]. Using a series of aquaria based studies, the egg-laying season was shown to coincide with seasonal peaks in TTX concentrations (June-August) [28]. The high concentrations of TTX detected in egg masses, and the subsequent depuration of TTX from adults after spawning, suggest that TTX plays a protective role in offspring of P. maculata. In 2013, high concentrations (ave. 376 mg· kg −1 ) of TTX were detected in Stylochoplana sp. (a marine flatworm), collected from Tauranga, New Zealand [29]. Concentrations of TTX were less variable than in P. maculata, but also decreased from winter (June-August) to spring (September to November). The small size of Stylochoplana sp. (ca. 60 mg) prohibited dissection and LC-MS analysis of TTX concentrations in various tissues, thus to date there is no information on how TTX is distributed within this organism. In this study immunohistological techniques, in conjunction with the T20G10 anti-TTX monoclonal antibody (mAB) [30], were used to investigate the micro-distributions of TTX within each organism at the cellular level. These data may provide insights on ecological function and the source of TTX in these organisms.

Mantle
Species from the group Opisthobranchia have extremely reduced, or in some cases have completely lost, their protective shell, resulting in a diverse range of alternative defensive strategies [31][32][33]. These include the acidification of the mantle [33][34][35], incorporation of nematocysts from cnidarian prey [31], development of spicules [36], secretion of ink [37], and acquisition of secondary metabolites [32,38]. The mantle, or dorsal body wall, of P. maculata consists of multiple folds or puckering of the epidermis which has previously been reported to be extremely acidic (pH = 1 to 2) [39]. In the immunostained section of the mantle TTX, visualized as brown color deposits, was most strongly localized in the basement membrane layer as well as in tear-shaped membrane bound cells ( Figure 1A). This is similar to immunohistochemical studies on the pufferfish Tetraodon nigroviridis [22], Tetraodon steindachneri [24], and Takifugu niphobles [40] where TTX was shown to be sequestered in both basal cells and succiform cells of the epidermis. The pink color of the tear-shaped cells in both the Hematoxylin & Eosin (H&E) and the Alcian Blue-Periodic Acid Schiff (AB-PAS) stained sections reveal that these erythrophil cells secrete neutral mucin, suggesting that cells responsible for the acidity of the mantle and sequestration of TTX are separate. Sequestration of TTX in the skin has been reported in a number of other organisms including the pufferfish Takifugu vermicularis and Chelonodon patoca, [23], the California newt Taricha torosa [41], red-spotted newt, Notophthalmus viridescens [19], the Japanese newt Cynops pyrrhogaster [20], and the frog Brachycephalus ephippium [42], and a possible defensive mechanism is suggested.

Reproductive and Digestive Tissue
Analysis of the mAB incubation of the gonad and digestive tissue from P. maculata showed the strongest antigen-antibody reaction was in the oocytes and their surrounding follicles ( Figure 2A). Positive staining occurred to a lesser degree in the digestive gland ( Figure 2A). Localization of TTX in the reproductive organs/tissues has been observed in other organisms including the oocytes of pufferfish Takifugu niphobles [40], Takifugu vermicularis and Chelonodon patoca [23], the ovaries, oviduct, and testis of the short-tailed newt Cynops ensicauda [43], and the ovaries of the blue ringed octopuses Hapalochlaena lunulata and Hapalochlaena fasciata [44]. Researchers have suggested that TTX plays a protective role in host organisms, and the localization of TTX in reproductive organs imparts the toxin onto offspring to increase their survival rates [44,45].
Previous reports addressing the origin of TTX in P. maculata have provided evidence suggesting that a dietary source is probable [12,29,46]. A dietary source was suggested by Wood et al. [12] due to the depuration of TTX in P. maculata when kept in captivity and fed a non-toxic diet. Khor et al. [46] demonstrated that non-toxic P. maculata have the ability to sequester TTX into their tissues when fed an artificial toxic food source. Further evidence was provided when real-time PCR assays revealed P. maculata ingested the co-existing TTX-containing Stylochoplana sp. [29]. Collectively these studies suggest that TTX in P. maculata is most likely obtained from a dietary source. The localization of TTX in the digestive gland tissue of P. maculata supports this proposed scenario (Figure 2A).

Stylochoplana sp.
Stylochoplana sp. were fixed flat and sectioned dorsoventrally. Tetrodotoxin, identified by the brown color deposits, is contained in the ova as well as portions of the pharynx (Figure 3). The localization of TTX in the reproductive and digestive tissues has also been reported in other flatworm species. Tanu et al. [21] used immunohistological techniques to show that TTX was contained in the ovum of the flatworm Planocera reticulata. Miyazawa et al. [47] showed the oviduct and digestive organs to be the most toxic tissues in the flatworm Planocera multitentaculata via mouse bioassay. A study on a planocerid species found on a reef in Guam demonstrated the highest concentrations of TTX were in the pharynx and through a series of feeding studies the researchers suggested that TTX was utilized in prey capture [48]. The sequestration of TTX in the pharynx of Stylochoplana sp. suggests this species may also use it to capture prey. The detection of TTX in the ova corroborates the observation of TTX in reproductive structures of Pleurobranchaea maculata, as well as many TTX-containing organisms, and most likely acts as a protective mechanism in offspring (see earlier discussion).

Offspring
The high water and mucopolysaccharide content of the gelatinous matrix of the egg masses prevented histological studies on these samples. However, the egg masses of both P. maculata and Stylochoplana sp., and hatched larvae of P. maculata, were tested for TTX utilizing liquid chromatography-mass spectrometry (LC-MS; Table 1). To ascertain where TTX is localized in the egg masses of P. maculata, the outer gelatinous matrix, with and without the eggs, was tested. No TTX was detected in the gelatinous matrix with the eggs removed, thus it is assumed that TTX is invested only into the eggs. While this would be beneficial for the larvae once hatched, the lack of TTX in the gelatinous coating surrounding the eggs is at odds with the suggestion that TTX provides a protective function for the egg masses [12]. This, in conjunction with the observation of the sea star Patiriella regularis consuming Pleurobranchaea maculata egg masses (L. Salvitti pers. obs.), suggests that TTX does not act as a predator deterrent at this life stage.
Tetrodotoxin concentrations of egg masses from a Stylochoplana sp. kept in aquaria were higher than those P. maculata egg masses tested in this study, but still in the range of previously recorded TTX concentrations of P. maculata egg masses (max. 100 mg· kg −1 ) [12]. It was not possible to localize TTX in egg masses of Stylochoplana sp. as these were only ca. 75 mg making separation of the gelatinous matrix and eggs difficult. Tetrodotoxin has been shown to be sequestered in eggs and egg masses of other species including flatworms Planocera multitentaculata [47], California newts T. torosa [41], rough skin newts T. granulosa [49], horseshoe crabs Carcinoscorpius rotundicauda [50,51], blue ring octopuses H. maculosa [52] and H. lunulata [44], and frogs A. chiriquiensis [53]. The foremost ecological role suggested for TTX residing in the egg masses and offspring is for protection. A recent study by Itoi et al. [45] demonstrates this possibility by showing that several predatory fish species ingested toxic pufferfish (T. rubripes and T. niphobles) larvae, but promptly spat them out. Immunohistological techniques revealed the localization of TTX in the outer layer of larvae of both pufferfish species. Several other studies have also shown the presence of TTX in pufferfish eggs and gonads signaling a potential role for the toxin in progeny protection [54,55].
Egg masses from P. maculata were collected on 9 September 2013 from Pilot Bay and kept in an aerated aquarium for 3 days before freezing (−20 °C ) sections for TTX analysis. Egg masses were tested in their entirety and small sections were carefully scraped clean of egg capsules leaving only the gelatinous casing for testing. The remaining egg masses were left in the aquarium until hatching (day 7). A sample of larvae (50 mL) was centrifuged (3000× g, 10 min), seawater removed, and frozen (−20 °C) for TTX analysis. Additional subsamples (50 mL) of larvae were collected, fixed with ethanol, and used for enumeration to determine the number of individuals in each sample. Counts were conducted using a 5 mL chamber and a dissecting microscope (Olympus SZ60).
Twenty-three Stylochoplana sp. specimens were collected from Pilot Bay (7 June 2012) and transported to laboratory aquaria in individual small plastic containers with 50 mL of seawater. Specimens were maintained in aerated aquariums (19 L) with 14 L of filtered seawater (0.22 μm). One individual laid two egg masses fourteen days after collection. Egg masses were removed from tanks and frozen (−20 °C) for TTX analysis.

Histochemistry
Pleurobranchea maculata were aseptically dissected. Small sections of the mantle tissue from P. maculata were removed, while the gonad and digestive tissues were kept intact due to the fragility of these tissues. Stylochoplana sp. specimens were left whole because of their small size (ave. 60 mg) and fixed flat using the techniques described in Newman and Cannon [56]. Briefly, Stylochoplana sp. specimens were transferred using a small artist brush to a piece of filter paper dampened with ambient seawater in order to encourage them to lie flat. Filter paper was then transferred into a container with frozen fixative (2% glutaraldehyde/4% paraformaldehyde), which was left to melt.
Step Some sections of Stylochoplana sp. and P. maculata mantle tissue were also stained with Gill's II hematoxylin (Surgipath ® ; Leica Biosystems, Wetzlar, Germany) and eosin (Surgipath ® ; Leica Biosystems, Wetzlar, Germany), and the Alcian Blue-Periodic Acid-Schiff (AB-PAS) method to differentiate between neutral and acidic mucins [57]. For AB-PAS staining, paraffin sections were rehydrated, stained with alcian blue (5 min), rinsed with distilled water, flooded with 1% periodic acid (2 min) and rinsed again. Slides were then immersed in Schiff's reagent (8 min) and washed in running water (10 min). Sections were lightly counterstained with Mayer's hematoxylin (2 min) before a final rinse with water. Sections were then dehydrated in an ascending ethanol series, cleared in xylene, mounted in D.P.X (Merck Millipore, Billerica, MA, USA), and observed under a light microscope (Leica DMRE with plan fluorite lenses, Leica Biosystems, Wetzlar, Germany).

Tetrodotoxin Analysis
Entire egg masses of Stylochoplana sp., specimens and sub-samples of egg masses from P. maculata, both with and without eggs, and hatched larvae were extracted for TTX. Samples (ca. 0.1 g) were first diluted 1:10 (w:v) with Milli-Q containing 0.1% v/v acetic acid. Each sample was manually homogenized with a glass pestle and vortexed to ensure complete disruption of tissues. Samples were centrifuged (3000× g, 10 min) and an aliquot of the supernatant was removed. This was diluted 1:10 with 100% methanol containing 0.1% v/v acetic acid and frozen (−20 °C) for at least 1 h. Samples were then centrifuged (3000× g, 10 min) and diluted 1:4 with 100% methanol containing 0.1% v/v acetic acid and analyzed for TTX using LC-MS as described in McNabb et al. [27].

Conclusions
Tetrodotoxin was found to be sequestered in the mantle, reproductive tissues, eggs, and larvae of P. maculata. Definitive characterization of the type of TTX-containing cells in the mantle of P. maculata is difficult to ascertain with paraffin techniques, and electron microscopy would greatly aid in identifying cell types and elucidating their potential functions. Tetrodotoxin localization in the digestive tissue could be indicative of a dietary source of TTX in this species. The de novo synthesis or sequestration of secondary metabolites from prey for use as a defense mechanism in Opisthobranchs is a well-known phenomenon (reviewed in [31]). The sequestration of TTX in the mantle, eggs, and larvae may be suggestive of a defensive role in P. maculata.
Localization of TTX in the pharynx, ova, and egg masses of Stylochoplana sp. could be indicative of ecological roles including aiding in capturing prey and protection of offspring, and further studies are required.
The methods by which P. maculata and Stylochoplana sp. sequester TTX are unknown. Immunohistologically-stained sections of P. maculata show that low concentrations of TTX are present throughout most tissues, while TTX is exclusively localized in the ova and pharynx of the Stylochoplana sp. This could be a product of differing anatomy (coelomate verses acoelomate) or that sequestration techniques differ between the two species. Tetrodotoxin-binding proteins have been isolated from a number of invertebrates including horseshoe crabs [58], xanthid crabs [59], and several gastropods [60]. Determining if these proteins are present in P. maculata or Stylochoplana sp. would assist in understanding the transfer and transport of TTX in these organisms.