extfieldsofstudy: Physics Medicine

s2fieldsofstudy: Biology Psychology

text:
Dissociable Influences of Skewness and Valence on Economic Choice and Neural Activity

Asymmetry in distributions of potential outcomes (i.e. skewness), and whether those potential outcomes reflect gains or losses (i.e. their valence), both exert a powerful influence on value-based choice. How valence affects the impact of skewness on choice is unknown. Here by orthogonally manipulating the skewness and valence of economic stimuli we show that both have an influence on choice. We show that the influence of skewness on choice is independent of valence, both across and within subjects. fMRI data revealed skew-related activity in bilateral anterior insula and dorsomedial prefrontal cortex, which shows no interaction with valence. Further, the expression of skew-related activity depends on an individual’s preference for skewness, and this was again independent of valence-related preference. Our findings highlight the importance of skewness in choice and show that its influence, both behaviourally and neurally, is distinct from an influence of valence.

Introduction
Value-based decision-making is influenced by risk in potential outcomes, and also by whether those potential outcomes reflect gains or losses (i.e. their valence). Recent evidence suggests that risk and valence exert independent influences on choice [1]. However, in those paradigms risk was defined as the variance in potential outcomes, ignoring that a crucial aspect is asymmetry in probabilities of possible outcomes. Such skewness powerfully influences choice in foraging animals [2] and in humans performing laboratory based economic tasks [3]. Indeed, attitudes to negative skew (i.e. a small chance of a particularly bad outcome) are important in performance measurement of financial investment [4], and attitudes to positive skew (i.e. a small chance of a particularly good outcome) help explain gambling [5]. How the impact of skewness on choice is affected by outcome valence is unknown, and here we examine this relationship behaviourally and neurally.
A behavioural influence of skewness has been highlighted in recent work using options involving only gain amounts [3,6]. Neurally, in both studies, and also when passively viewing skewed mixed gambles containing both gains and losses [7], skewness in value-based stimuli was reflected in activity within anterior insula, a region implicated in processing of uncertainty [8][9][10]. In contrast, there is a dearth of behavioural data on the relationship between skewness and valence, although the attractiveness ratings of a skewed gamble with gains can be markedly reduced by adding a loss outcome [11]. As far as we are aware studies of neural encoding of skewness have relied on either gains or mixed gambles [3,6,7].
We orthogonally manipulated skewness, variance and valence of value-based stimuli during economic choice. Expected Value (EV) was kept constant within these choices. Behaviourally, we tested whether skewness influenced choice, and also asked how this influence of skewness may relate to the influences of both variance and valence. Using fMRI, we asked how the influence of skewness was reflected neurally, and how this may differ according to valence, with a particular focus on anterior insula.

Participants and ethics statement
All participants provided written, informed consent. The University College London (UCL) Ethics Committee approved the study. All participants were healthy and were recruited using institutional mailing lists. 27 right-handed participants took part (age mean 24 years, range 18-33; 13 male; one further participant excluded who used a fixed strategy of choosing the sure option). No participants had taken part our previous studies with related paradigms [1,3].

Task
In this ''accept/reject'' task ( Fig. 1) there were 200 trials presented in a random order, of which 100 were ''gain trials'' (all possible outcomes $0) and 100 were ''loss trials'' (all outcomes #0). In each trial participants chose to accept or reject a lottery (four possible outcomes) compared to a sure option (£12 in ''gain trials''; £-12 in loss trials). Each trial began with a fixation cross presented for 1-2secs (mean 1.5secs), followed by viewing the options for 4020msec; and finally a black square appeared to indicate participants had 1500msec to input their choice by button press (the black square turned white when they chose). If participants did not respond, they received £0 on a ''gain trial'' and the maximum loss possible on a ''loss trial'' (£-24).
Our decision-variables of interest were skewness, variance and valence. We controlled for the Expected Value (EV) of the lottery, which was always equal to the sure option (i.e. £12 or £-12). We manipulated the two aspects of risk by using a set of 100 lotteries (four possible outcomes, all $0; Fig. 1b) in which we parametrically and orthogonally manipulated the degree of skewness (10 levels, with half positively skewed and half negatively skewed) and variance (10 levels). We presented each lottery in this set once to give 100 ''gain trials''. To manipulate valence, we multiplied all amounts by -1 to give 100 ''loss trials'' (i.e. all outcomes #0, and a sure option of £-12). This created a set of ''gain trials'' and a set of ''loss trials'' that were matched in their parametric modulations of skewness and variance.
Participants began the day with an endowment of £24. After the experiment, one ''gain trial'' and one ''loss trial'' were picked at random and their outcomes were added to the endowment to determine payment. Participants could receive between £0-48.

Stimulus sets
We generated the set of 100 ''gain trials'' in two stages. First, we generated a list of every possible trial within the following constraints: lottery EV was £12 (i.e. equal to the sure option); each lottery had four outcomes (i.e. four pie chart segments); outcomes were between £0-£24; the smallest allowable probability was 0.1; and the smallest allowable probability increment was 0.05.
Second, from within this very large number of potential trials, we selected our set of 100 trials that were the closest match to our desired 10 levels of skewness and 10 levels variance. Variance ranged from 7 to 124, skewness ranged from -317 to 317 (absolute skewness ranged from 30 to 317).
Calculation of EV, Variance and Skewness. For a given lottery with N potential outcomes (m 1 , m 2 ,… m N ), with probabilities p = p 1 , p 2 , …p N , we define the EV, variance (Var) and skewness (Skw) of the outcome distribution as follows:

Statistical analysis
In our behavioural analyses, statistical tests were carried out using paired or independent-samples t-tests, or mixed analyses of variance (ANOVA) in SPSS; reported p-values are two-tailed.

Reaction time analysis
We normalised each individual's RTs by taking the natural logarithm, mean-correcting and dividing by the standard deviation. However, we note that our findings were the same irrespective of having used ''raw'' or normalised RTs.

Behavioural modelling
We modelled choice using utility functions described previously [1]. Additionally we assessed utility functions explicitly including an influence of skewness on choice. We fit data on an individual participant basis, modelling behaviour by estimating model parameters using maximum likelihood analysis implemented in Matlab. We compared models with different utility functions using Group Bayes Factors, where the Bayesian Information Criterion (BIC) penalisies model complexity [12]. In all our models, on each trial the subjective values, or utilities (U), of both options were computed using one of the utility functions below. These values were then compared to generate a trial-by-trial probability of each choice, using a softmax function with a free parameter b (constrained between 0 and 20) that allows for noise in action selection.
The behavioural modelling enables us to confirm that our manipulations of skew, variance and valence consistently influenced choice. Therefore, to illustrate the influences of variance and valence on choice we used the same two models we used previously to illustrate these effects with unskewed lotteries [1]. First, to test for an influence of variance we used a Mean-Variance model (U = EV + Var*r), in which r is a free parameter (a riskneutral individual has r = 0, risk-averse r,0, and risk-seeking r.0). Second, to test an additional influence of valence we used the winning Mean-Variance-Valence (MVV) model from our previous datasets with unskewed lotteries [1], with a r gain parameter reflecting risk preference in gain trials and a r loss Figure 1. Experimental design. Our design orthogonally manipulates variance, skewness and valence in a modification of our ''accept/ reject'' task (previously in this task we parametrically manipulated EV and lotteries were unskewed). a) In each ''gain trial'' individuals chose to accept a lottery (4 possible outcomes, all $0; EV £12) or reject and so receive £12 for certain. b) We created a set of 100 ''gain trials'' that parametrically and orthogonally manipulated the degree of skewness (10 levels) and variance (10 levels) of the lotteries. In this set of gain trials half had positive skew (e.g. panel c), and half had negative skew (e.g. panel d) (the two example lotteries in panels c and d are parameter reflecting risk preference in loss trials. Third, in the current study we added the influence of skewness in an MVVskewness model (U = Mean + r*Variance + y*skew) with the free parameter y reflecting skew-preference in addition to those for r gain , and r loss . Note that in this study lottery EV does not contribute as it always equals the sure option. We also implemented Expected Utility, Prospect Theory and Cumulative Prospect Theory models, as previously detailed in [1].

fMRI data acquisition
This was identical to that previously reported in [1]. Using a 3T Allegra scanner (Siemens) each participant underwent one functional run (515 volumes), acquired using a gradient-echo EPI sequence (46 transverse slices; TR, 2.76 secs; TE, 30 ms; 363 mm in-plane resolution; 2 mm slice thickness; 1 mm gap between adjacent slices; z-shim -0.4 mT/m; positive phase encoding direction; slice tilt -30 degrees) optimised for OFC and amygdala. We acquired a T1-weighted anatomical scan and local field maps.

fMRI data analysis
Functional data were analysed using standard procedures in SPM8 (Statistical Parametric Mapping; www.fil.ion.ucl.ac.uk/ spm). fMRI timeseries were regressed onto a composite general linear model (GLM). The GLM contained boxcars for the length of time the lottery was displayed (5.5 seconds) to examine the decision-making process. Delta functions were also included for button presses, lottery onset to account for visual stimulus presentation, and for trials in which subjects failed to respond. We modelled our neuroimaging data using a 2 valence (gain, loss) by 2 choice (risky, sure) design, as in [1]. Additional parametric modulators were included, with the height of the boxcar modulated by the skewness and variance of the lottery on each trial. Additional GLMs using modifications of this design (e.g. alternative parametric regressors) are described in the Results. The delta functions and boxcars were convolved with the canonical haemodynamic response function.
We report all activations at P,0.05 that survive whole brain correction using family-wise error at the cluster level [13], unless otherwise stated. Clusters were defined using a threshold of P ,0.005. For presentation, images are displayed at P,0.001 uncorrected. Unless otherwise stated, small volume correction (P,0.05) was for a sphere of 8 mm radius around stated coordinates. Conjunction analyses were performed using the SPM 8 conjunction null function [14].
Skewness also influenced choice (Fig. 2b). Half the lotteries were positively skewed and half negatively skewed enabling us to assess the impact of skewness on choice (ImpSkew = PropRisk negSkew -PropRisk posSkew ). Individuals were sensitive to skewness (ImpSkew 0.326s.d.0.20; one-sample t-test versus no effect of skew, t (26)  For variance there was no simple categorical division between trial types (e.g. comparable to gain v. loss, or positive v. negative skew). However, for illustration we split trials into the half with the higher and the half with the lower variance and then assessed its impact on choice (ImpVariance = PropRisk lowVar -PropRisk highVar ). Variance influenced choice (ImpVariance 0.056s.d.0.11; onesample t-test versus no effect of variance, t (26) = 2.5, P = 0.018) and subjects made more risky choices when the variance was low (PropRisk LowVar 0.476s.d. 0.09), than when it was high (PropRisk HighVar 0.426s.d. 0.11; t (26) = 2.5, P = 0.018; Fig. 2b). There was no interaction with valence (see ANOVA below) but was an interaction with skewness in both gains and losses such that variance influenced choice with negatively but not positively skewed gambles (Fig. 2b).
Individuals' skew preferences were independent of those for valence or variance We next examined inter-individual differences, asking if an individual's sensitivity to one decision variable predicted their sensitivity to another. The influence of skewness on an individual's choices (ImpSkew) did not predict the influence of valence (ImpValence v ImpSkew: r = 0.24, P = 0.2; Fig. 3) nor variance (ImpVariance v. ImpSkew: r = -0.17, P = 0.4) nor the proportion of risky choices they made overall (PropRisk all v. ImpSkew: r = -0.17, The proportion of risky choices an individual made overall (PropRisk all ) did not predict the influence of valence (PropRisk all v. ImpValence: r = -0.02, P = 0.9) nor variance (PropRisk all v. ImpVariance: r = -0.25, P = 0.2). However, we noted a significant correlation between individuals' preferences for variance and valence (ImpVariance v. ImpValence: r = -0.45, P = 0.02; i.e. more gambling for gains than losses was associated with more gambling for higher than lower variance options).

Behavioural modelling confirmed that skew, variance and valence influenced choice
The purpose of the behavioural modelling was to confirm that our manipulations of skew, variance and valence consistently influenced choice, as shown by comparing our ''summary statistic'' models. To illustrate the influences of variance and valence on choice we used the same two models used previously to illustrate effects with unskewed lotteries [1]. As before, the influence of variance was captured by a Mean-Variance model that correctly predicted 59%6s.d.7% of choices (summed BIC = 7094). The fit of the model was improved by adding valence, shown in the winning Mean-Variance-Valence (MVV) model in our previous datasets with unskewed lotteries [1] (BIC = 7003). In turn, that model was improved by also accounting for skew in our MVV-Skew model (MVVS BIC = 6102). Notably, this MVVS model also outperformed more standard Expected Utility, simple Prospect Theory and more complex Cumulative Prospect Theory models (with the reference point as zero or as a free parameter), and a modified MVVS model with separate skew parameters for positive and negative skew. In absolute terms the MVVS model correctly predicted 73%6s.d.7% of choices.

Reaction times
Reaction times (RTs) also showed influences of skew, variance and valence (Fig. 4). Further, these data were consistent with each acting as appetitive or aversive stimulus features, where it is known that individuals are slower to approach aversive stimuli and faster to approach appetitive stimuli [15]. We have further examined and discussed such RT effects previously [1]. Regarding valence, individuals were slower to approach (choose) options entailing loss compared to gains (gains RT 5946s.d.90msec; losses 643694; t (26) = -3.8, P = 0.001).
Regarding risk, this can be aversive, neutral or appetitive depending on an individual's risk preference. We found that individuals' risk preference with gains (PropRisk gain ) strongly predicted RT differences when approaching (choosing) the riskier relative to the sure option (i.e. RT riskier -RT surer ) with gains (r = -0.58, P = 0.001); and risk preference with losses (PropRisk loss ) strongly predicted the RT difference with losses (r = -0.48, P = 0.01). Furthermore, the pattern was exactly as predicted where risk-averse individuals were slower to approach risk; riskneutral showed no RT difference; and risk-seeking subjects were faster to approach risk.   Both aspects of risk (skewness and variance) showed the same pattern. Individuals' risk preference with negatively skewed gambles (PropRisk negSkew ) strongly predicted the RT difference when approaching (choosing) the riskier relative to the surer option (RT riskier -RT surer ) with negatively skewed gambles (r = -0.52, P = 0.005); and risk preference with positively skewed gambles (PropRisk posSkew ) strongly predicted the RT difference with positively skewed gambles (r = -0.74, P = 9610 26 ; Fig. 4). Individuals' risk preference with high variance gambles (PropRisk highVar ) predicted the RT difference with high variance gambles (r = -0.58, P = 0.001); and risk preference with low variance gambles (PropRisk lowVar ) strongly predicted the RT difference with positively low variance gambles (r = -0.38, P = 0.048).

Neural data
We first tested for a representation of skewness in the stimuli. Initially, we used the same 2 valence (gain, loss) by 2 choice (sure, risky) factorial design as in our previous study with unskewed lotteries [1], but here with lottery skewness and variance as trialby-trial parametric regressors. Activity is whole-brain cluster-level corrected (P,0.05) unless otherwise stated. We replicated the main effects of valence (greater activity for gains than losses in bilateral striatum and obitofrontal cortex (OFC); greater activity for losses than gains in pre-SMA/dmPFC and SVC in bilateral anterior insula) and choice (risky.sure in posterior parietal cortex) in the factorial analysis. Again parietal activity correlated with the parametric manipulation of lottery variance (Table S1). However, no activity correlated with skewness in that GLM, nor in an alternative GLM using the modulus of the skewness (i.e. the unsigned magnitude of the skewness in each trial).
However, robust activity was seen for skewness as it interacted with choice ( Fig. 5; Table 1). This was shown in a new GLM with a 2 valence (gain, loss) by 2 skew type (posSkew, negSkew) by 2 choice (risky, sure) factorial design corresponding to the categorical distinctions in our design, and with variance as a parametric modulator. An interaction of skew type by choice in bilateral anterior insula and pre-SMA/dmPFC (Fig. 5a) was driven by increased activity for choosing a risky option with positive skew (Fig. 5b), which was the specific action to which individuals were least disposed behaviourally (Fig. 2b). Further, this pattern of skewrelated activity did not interact with outcome valence (Fig. 5b; no suprathreshold voxels for an interaction with valence within 10 mm of the peak in each cluster) and conjunction analysis between this activity in gains and losses revealed activity regardless of valence in these same areas (pre-SMA/dmPFC and right anterior insula). We also note that, as above, activity in this GLM corresponded to the manipulations of valence (gain.loss in OFC and striatum; and loss.gain SVC in dmPFC/pre-SMA) and variance (positive correlation in parietal cortex; Table 1).
Next we asked if inter-individual differences in sensitivity to skewness were also reflected neurally. Applying our impact of skew metric (ImpSkew) to the contrast that revealed skew-related activity above (i.e. the interaction of skew type and choice), demonstrated a negative correlation with activity in regions including hippocampus and OFC (Table 2). This indicated that the less susceptible an individual was to skewness (i.e. lower ImpSkew), the more significant the interaction of choice and skew, driven both by greater activity for risky.sure choices in posSkew trials, and lower activity for risky.sure choices in negSkew trials. This correlation was specific to ImpSkew, as skew-related activity did not correlate with PropRisk all , ImpValence or ImpVariance. Further whilst activity for risky.sure choices correlated with overall risk preference (i.e. PropRisk all ) in anterior insula (Table 2), this was again specific and did not correlate with ImpSkew, ImpValence or ImpVariance. Finally, for completeness we note there was no correlation between variance-related activity and the ImpVariance, nor between activity for gains versus losses and ImpValence.
Finally, we asked if neural activity correlated with unified subjective value (SV) derived from the winning behavioural model. In contrast to the robust findings above, No such correlation was seen (whole brain corrected or using SVC in vmPFC, OFC and striatum as regions of interest specified in the PickAtlas toolbox [16] in a GLM as above but with lottery SV as the parametric modulator, nor when using chosen minus unchosen SV, nor using the difference in SV between options.

Discussion
Here we demonstrate that the skewness of outcome distributions influenced choice behaviour. Furthermore, this influence of skewness on choice was independent of valence both across (Fig.  2b) and between (Fig. 3a) subjects. Neurally, we observed skewrelated activity across subjects in anterior insula, a region implicated in aversive representations [17,18], and between subjects correlating with individuals' skew preference in hippocampus and vmPFC/OFC. Mirroring our behavioural findings these patterns of skew-related activity were seen for gains and losses (Fig. 4b). These data support the idea that risk is not monolithic, either in terms of its behavioural effects or neurally, and instead that distinct aspects of risk including variance and skewness can powerfully influence choice.
The observation that skewness influenced choice behaviour is consistent with human [3,6] and animal data [2]. Our data help characterise this influence of skewness in two further ways. Firstly, we dissociate this influence of skewness from other aspects of risk. Secondly, individuals' preferred negative to positive skew, which replicates a recent study using a similar format [3] and previous work showing negative skew preference on average [19]. We note that other studies have shown a mixture of participants with Figure 5. Choosing skewed lotteries alters anterior insula activity. Skewness may affect action-selection by influencing the disposition to approach economic stimuli. We examined neural activity in a GLM with a 2 valence (gain, loss) by 2 skew type (posSkew, negSkew) by 2 choice (risky, sure) factorial design corresponding to the categorical distinctions in our design, and with variance as a parametric modulator. a) There was an interaction of skew type by choice in bilateral anterior insula. As shown in panel b this was driven by increased activity for choosing (approaching) the risky option with positive skew, which was the specific action to which individuals were least disposed behaviourally. Further, as shown in panel b this pattern of skew-related activity did not interact with outcome valence. Parameter estimates plotted for the peak of the interaction in right anterior insula. Error bars indicate s.e.m.. *,0.05, **,0.005, ***,0.001 doi:10.1371/journal.pone.0083454.g005 positive or negative skew-seeking [20] or positive skew seeking behaviour [21,22].
Neurally, we showed that skew-related activity was distinct from the activity related to other aspects of risk. Skew-related activity here was found in anterior insula, a region implicated in two previous studies of skewness in gains [3,6] and when passively viewing skewed mixed gambles [7]. Anterior insula is a region also implicated in processing of uncertainty more generally [8][9][10]. In line with other recent work with unskewed gambles [1], this skewrelated activity here was contingent on choice, and specifically there was increased activity in anterior insula when choosing the least preferred option (i.e. choosing the risky option when it contained positive skew), where anterior insula is known to be implicated in aversive representations [18].
We note that if anterior insula plays such a role we would also expect increased activity here for choosing the lottery with losses, as we have shown previously [1]. However, in this experiment skew-related preference dominated behaviour (Fig. 2b) which may have reduced sensitivity and we do not see strong evidence for such valence-related activity. Tentative evidence for this here is seen in the simpler GLM that collapsed across skewness type and that showed greater activity for losses than gains (Results above  and Table S1), as well as in our main GLM where Fig. 5b shows a tendency towards a greater effect of risky than sure choices in losses than in gains. This could be usefully examined in future work.
In addition to such skew-related activity across subjects, between subjects we noted brain regions that integrate this skewrelated activity with individuals' preferences for skewness. This was seen in hippocampus, a region identified with reward in a metaanalysis of value-based choice [23] and implicated in goal-directed behaviour [24]. It was also seen in OFC, a region implicated in an integration of preference and reward related activity [25]. In these regions, the less susceptible an individual was to skewness (i.e. lower ImpSkew), the greater the interaction of choice and skewness. This closely parallels previous findings between subjects, where for example in a framing task participants who showed less framing exhibited greater OFC activity associated with the interaction of choice and frame [26]. As those authors speculate, individuals less susceptible to the frame may be better at representing their own affective influences, which enables them to modify their behaviour.
The influence of skewness on choice behaviour was independent of outcome valence both across (Fig. 2b) and between (Fig. 3a)  This table shows all activity surviving cluster level correction across the whole brain (P,0.05 FWE corrected; threshold of P,0.005 used to define the clusters) for our GLM with a 2 valence (gain, loss) by 2 skew type (posSkew, negSkew) by 2 choice (risky, sure) factorial design corresponding to the categorical distinctions in our design, and with variance as a parametric modulator. We report all activity for all main effects and contrasts in the factorial design, and for positive and negative correlations with variance and for the interaction of variance in gains versus losses. Activity for loss.gain survived SVC in dmPFC/ pre-SMA (9 17  subjects. The relationship between this aspect of risk (skewness), and valence is important as the prevailing view in psychologicallyinformed economics is that risk and valence have a specific relationship, in which individuals are risk-averse with gains and risk-seeking with loss outcomes [27,28]. Contrary to this we observed that individuals chose the risky option more often with gains than with losses, which precisely replicates our previous findings in a similar paradigm with unskewed gambles [1].
Specifically regarding the relationship between the influences of skewness and valence on choice behaviour, there has not to our knowledge been a previous characterisation of how the influence of skewness (e.g. positive and negative skew) is affected by an orthogonal manipulation of valence. Supporting the idea that these may be distinct influences, however, we note that a study assaying attractiveness ratings of gambles showed such ratings were dramatically altered by adding a loss to a skewed gamble with gains (i.e. it became a mixed gamble), suggesting such a gamble may be rendered qualitatively different [11]. Skew-related activity was distinct from that related to valence (Fig. 5b). Previous studies of skewness have used either only gains or mixed gambles [3,6,7]. That risk and valence may exert their influence on choice independently is consistent with mounting evidence that choice is the product of multiple interacting value and decision systems, which may for example reflected in both our current and previous data [1] by distinct behavioural effects and neural substrates associated with risk and valence.
Finally, we note that our data also speak to a debate in the literature between two main competing accounts for recent neuroscientific studies examining the neural basis of risky economic choice: that ''summary statistics'' describe the distribution of possible outcomes from a risky choice [9,29]; or that subjective value (SV) is determined by the shape of a utility function, with risk-preference emerging as a by-product of that shape [30]. Here consistent with a ''summary statistic'' account we find neural activity related to skew and variance, whilst in contrast to these robust data, there was not equally clear evidence for encoding of SV. However, we note that absence of evidence is not evidence of absence. Further, an important potential reason is provided in a recent study showing that SV representations of more complex multi-attribute stimuli are distributed, and so detectable using multivariate but not standard mass univariate analysis [31].
In conclusion, risk exerts a powerful influence on value-based decision-making and our data help parse the different dimensions of risk. The influence of skewness on choice can be dissociated from other aspects of risk both in terms choice behaviour and its neural representation. Furthermore, our data show that the influence of skewness can be dissociated from the influence of valence on choice. These data provide evidence that distinct aspects of value-based stimuli exert their influence through a choice architecture wherein multiple interacting systems contribute to choice. Table S1 fMRI results for 2 valence (gain, loss) by 2 choice (risky.sure) GLM. Replicating the 262 analysis used in our previous study that used unskewed lotteries (Wright et al., 2012) revealed similar results across and between subjects. Panel a) across subjects: shows all activity surviving cluster level correction across the whole brain (P,0.05 FWE corrected; threshold of P,0.005 used to define the clusters) for our GLM with a 2 valence (gain, loss) by 2 choice (risky, sure) factorial design, with variance and skewness as parametric modulators. We report all activity for all main effects and contrasts in the factorial design, and for positive and negative correlations with the parametric modulators and for the interaction of the parametric modulators in gains versus losses. Note also activity for the contrast of loss.gain survived SVC in left AI (236 23 1 Z = 4.01, 64vox) and right AI (36 23 7 Z = 3.78, 45vox). Panel b) between subjects: shows activity in this GLM for the second level covariate for risk (PropRisk all ) on activity for risky.sure; and the This table shows all activity surviving cluster level correction across the whole brain (P,0.05 FWE corrected; threshold of P,0.005 used to define the clusters) in the 2 valence by 2 skew type by 2 choice model, for contrasts involving: the second level covariate for risk overall (PropRisk all ) on activity for accept.reject; the second level covariate for skew (ImpSkew) on the skew related activity seen across subjects (interaction of skew type and choice). For each cluster is shown: the three constituent peaks with the highest Z-scores; the number of voxels at P,0.005 (uncorrected); and the P-value of the cluster after FWE correction across the whole brain. doi:10.1371/journal.pone.0083454.t002 second level covariate for valence (ImpValence) on activity for gain.loss. For each cluster is shown: the three constituent peaks with the highest Z-scores; the number of voxels at P,0.005 (uncorrected); and the P-value of the cluster after FWE correction across the whole brain.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
A1180V of Cardiac Sodium Channel Gene (SCN5A): Is It a Risk Factor for Dilated Cardiomyopathy or Just a Common Variant in Han Chinese?

Our previous study of a Chinese family with dilated cardiomyopathy (DCM) suggested that A1180V of the cardiac sodium channel gene (SCN5A) was associated with the disease within this family. According to data deposited in dbSNP, however, A1180V has been found in some small samples of the Asian population. In this study, we followed up the affected pedigree and expanded the investigation of the prevalence of A1180V in 460 unrelated healthy Han Chinese. Besides, we searched and analyzed it in other database as well. During the follow-up period, 1 A1180V carrier's condition deteriorated alot, and another 4 carriers progressed to DCM or atrioventricular block (AVB). We also found that the A1180V was absent among the 460 individuals (0%, 0/460), and the carrier frequency of A1180V among Chinese was about 0.51% obtained from the 1000 genome project. In conclusion, our finding suggests that A1180V is a potential risk factor for DCM, and it is extremely rare among Healthy Han Chinese.

Introduction
Dilated cardiomyopathy (DCM) is the most common form of primary cardiomyopathy and is a leading cause for heart transplantations [1] approximately 30%-50% of affected individuals have a familial form of DCM [2]. To date, at least 40 disease genes have been reported to be associated with familial DCM [3], in which; mutations of SCN5A gene which encodes the alpha subunit of the major sodium channel in the heart have been demonstrated to result in myocardial damage and DCM [4,5]. Previously, SCN5A has been implicated in conduction disturbances and several electrophysiological phenotypes [6,7], including atrioventricular block (AVB), long QT syndrome, sick sinus syndrome, sudden infant death syndrome, and atrial fibrillation [8,9]. Meanwhile, more and more SCN5A mutations were discovered to be related with DCM, such as A1180V [10], D1275N [11], W156X, R225W [12], and R222Q [13].
We first reported finding A1180V, a single nucleotide mutation, c.3539C > T, that occurred in exon 20 of the SCN5A gene in a three-generation Chinese family with agerelated DCM and progressive AVB in 2008 [10]. In that study, A1180V was detected in all studied patients and in six younger unaffected members of the pedigree. The mutation, however, was not found in 200 healthy Han Chinese controls. Experimental data revealed that A1180V channels exhibited a negative transform of voltage-dependent inactivation of the cardiac sodium channels, which lead to a rate-dependent sodium current reduction, and a moderate increase in late sodium current. Correspondingly, our clinical study revealed QRS widening at high heart rates and QTc prolongation at rest in unaffected carriers. These findings suggest that A1180V is a functional mutation that can contribute to the development of DCM [10].
However, data deposited in the dbSNP database [14] show that A1180V has been found in two small sample 532 Disease Markers groups of the Asian population. In the first sample group, A1180V (dbSNP refSNP number, rs41310765) was present in a cohort of 47 Han people (R31 HANLA) who were part of the Los Angeles Panel at a carrier frequency of 6.4%. In another sample group (HER ASIAN-PANEL) of 16 individual Asians selected from the Coriell Cell Repository, genetic sequencing yielded a carrier frequency of 6.2% (http://www.ncbi.nlm.nih.gov/projects/SNP/snp ref.cgi?rs =41310765). To date, the data obtained from the dbSNP database show that A1180V is virtually absent in other ethnic groups. Thus, these unpublished data suggest that A1180V might be a common variant in Asians, which is inconsistent with our previous observations.
In order to further evaluate the pathogenic role of A1180V in familial DCM and make sure the frequency of A1180V among Healthy Han Chinese, we followed up the threegeneration Chinese family and expanded the control size to screen for A1180V. Besides, we analyzed its frequency in other database as well.

Study Population.
We followed up all the A1180V carriers and noncarriers of the three-generation Chinese family for 6 years. All individuals were interviewed with the symptoms of cardiovascular disease, such as typical chest pain, palpitation, cyanosis, and syncope. 12-lead electrocardiogram (ECG) and echocardiography were performed on the family members to make a diagnosis. 460 unrelated Healthy Han Chinese living in Shanghai, ranging from 20 to 70 years old, were enrolled to the control group from a National High Technology Research and Development Program (the National 863 Program) of China. Individuals with known cardiovascular risk factors other than age, gender, and smoking history were excluded. The enrolled participants did not show any abnormalities under comprehensive clinical evaluation, including review of medical history, physical examination, 12-lead ECG, submaximal exercise ECG, and blood biochemical examinations.
Each of the participants and individuals whose data are included here signed an informed consent form after a complete and clear explanation of the nature and purpose of the study. The study was approved by the local Ethics Committee of Zhongshan Hospital, Fudan University.

Genotype
Determination. DNA was extracted from 2 mL venous blood according to kit procedure (Sigma, USA) and stored at −80 ∘ C. The exon 20 of SCN5A was amplified by polymerase chain reaction (PCR). A 20 uL mixture was prepared for each reaction and included 1x HotStarTaq buffer, 2.0 mM Mg 2+ , 0.2 mM dNTP, 0.2 M of each primer, 1 U Hot-StarTaq polymerase (Qiagen Inc.) and 1 L template DNA. The DNA was amplified by cycling at 95 ∘ C for 15 min; 11 cycles of 94 ∘ C for 15 s, 62 ∘ C-0.5 ∘ C per cycle for 40 s, 72 ∘ C for 1 mins; 24 cycles of 94 ∘ C for 15 s, 57 ∘ C for 30 s, 72 ∘ C for 1 mins; 72 ∘ C for 2 min. The oligonucleotide sequences of the primers were as follows: 5 -ACCCCCATCATCGTAGCTCTT-3 and 5 -TCCTGCTCTGGCCTCCATAC-3 . The PCR products were purified by 1 U SAP and 6 U Exo I, and they were added into 8 L PCR products. The mixture was incubated at 37 ∘ C for 60 mins, followed by incubation at 70 ∘ C for 10 mins. Reaction mixture included 2 L BigDye3.1 mix, 2 L sequencing primer (0.4 M, 5 -ACCCCCATCATCGTAGCTCTT3 ) and 1-2 L purified PCR product. The cycling program was 96 ∘ C for 1 min; 28 cycles of 96 ∘ C for 10 s, 50 ∘ C for 5 s, 60 ∘ C for 4 mins. The sequencing products were analyzed by an ABI3730XL DNA Analyzer. The results were analyzed by the Polyphred software and revised manually.

Database Retrieval.
We searched A1180V of SCN5A in 1000 genomes project [15]. Besides, we have excluded some familial data to make sure that the frequency of A1180V from 1000 genomes project was calculated from unrelated individuals.

Statistical Analyses.
All statistical analyses were performed with the SAS statistical program. Comparison of the A1180V carrier frequency between our studies and the results of dbSNP database was tested by using the Fisher's exact test. Statistical significance was determined at < 0.05.

Results
Combined with our former study and the 6 years of clinical followup of the affected pedigree, we discovered that A1180V related DCM was preceded by progressive AVB which mostly occurred in the third decade of life. During the follow-up period, the condition of an A1180V carrier (II-8 in Figure 1) who had been diagnosed with DCM before aggravated gradually and she now was diagnosed with severe congestive heart failure. Besides, III-1 with A1180V, developed to DCM and complete atrioventricular block (AVB) and another 3 carriers (III-2, III-3, III-13) progressed to different degree of AVB. Figure 2 displays changes of echocardiograms of III-1 and ECG recordings of III-2 in the follow-up period. None of the individuals in the pedigree showed any sign of long-QT or Brugada syndrome and other diseases.
After screening A1180V in 460 individuals, we found that A1180V was absent in this group (0/460, 0%). Figure 3(a) gives an example of the sequencing results, and Figure 3(b) is a sequencing result of a A1180V carrier in 2007. The combined carrier frequency, 0% (i.e., 0 out of 660 cases), of A1180V from our studies remarkably suggests that A1180V is not a common variant among Healthy Chinese, and the data significantly differs from that in the dbSNP database ( = 5.3 × 10 −5 ).
Data from the 1000 genomes project revealed a carrier frequency of 0.51% (i.e., 1 out of 197 cases) for A1180V among Chinese participants. The data was obtained by analyzing the sequencing results of 97 northern and 100 southern Chinese adults. Since the southern Chinese group was familial related, the data from known offspring were excluded from the calculation of the carrier frequency to make sure the value was calculated from unrelated individuals (http://browser .1000genomes.org/Homo sapiens/Variation/Population?db= core;r=3:38616415-38617415;v=rs41310765;vdb=variation;vf= 12532571). The data also dramatically differs from those

Discussion
We have presented the followup and sequencing data identifying that A1180V of SCN5A was a potential risk factor for DCM, and it is absent among healthy Han Chinese. Compared with the former results in 2008, 5 A1180V carriers exhibited age-related progressive AVB and DCM, and the A1180V carrier frequency among Han Chinese in our study is 0% out of 660 control cases.
Evidence is now being accumulated that SCN5A mutations are associated with development of cardiac dilation [13,[16][17][18]. McNair et al. have reported that mutations in SCN5A were detected in 1.7% of DCM families with a shared mechanism of disruption of the voltage-sensing mechanism of this channel leading to DCM [19]. Moreover, some SCN5A mutations which have been found to be a primary factor of leading DCM were characterized by an age-related phenotype of cardiac dilation with different types of arrhythmia [11,20]. And the pathogenic characteristic of A1180V conformed to the pattern as well. Since the cardiac sodium channel is principally in charge of the fast depolarization of the myocardium [21], some suggested that the cardiac dilation may result from long-term arrhythmia [22,23]. However, our experimental evidence indicated that A1180V could alter calcium homeostasis as a consequence of alterations in intracellular sodium concentrations, and the former condition might damage myocardium directly and subsequently lead to cardiac dilation [10].
Our data have revealed that A1180V specifically appeared in the affected pedigree and was absent among healthy controls, however, why do such significant discrepancies exist for A1180V among different studies of the same ethnic population? Possible explanations for the discrepancies  [25]. At present, lots of gene mutations were found playing pathogenic roles in both familial and sporadic diseases [26,27], then another question is that whether A1180V would be a risk factor among sporadic DCM patients as well. In our recent studies of the distributions of candidate mutations in DCM-related genes in sporadic DCM patients conducted using high-throughput sequencing technology, we identified A1180V in 1 out of 68 patients with DCM. The patient, a 41year-old male, was diagnosed as having DCM with NYHA class I, LVEDD 78 mm, and LVEF 24%, but no other diseases. However, the patient carrying A1180V also carries other mutations in several DCM-related genes, including G1117S, P1119R, Y498H of LAMA4, W768S of RBM20, P398S of VCL, and R659W of SCN5A. The last two mutations have never been reported previously. Although we found that A1180V might also be a risk factor in sporadic DCM patients, we cannot exclude the possibility that A1180V could work together with other gene mutations.
In addition to genetic factors, our previous study of the DCM pedigree carrying A1180V demonstrated that the A1180V channel lost current only at high heart rates, which suggested that physical activity and lifestyle that increased average daily heart rate might enhance the phenotype for A1180V carriers [10]. Therefore, environmental factors may also play a role in expression of the pathogenic mechanism of A1180V. Further studies of genetic and environmental factors and followup of A1180V carriers will provide more information about the relationship between the mutation and the risk of DCM. Studies of transgenic mice may also provide a step to test the hypothesis and to explore the underlying mechanism. T

Conclusions
In conclusion, herein we provide evidence that the A1180V is a potential cause for DCM rather than a common variant in Han Chinese, although additional genetic or environmental factors may potentiate the risk for DCM in A1180V carriers.


s2fieldsofstudy: Biology Chemistry

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Ampelopsis Radix Protects Dopaminergic Neurons against 1-Methyl-4-phenylpyridinium/1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-Induced Toxicity in Parkinson's Disease Models In Vitro and In Vivo

Ampelopsis Radix, the root of Ampelopsis japonica (Thunb.) Makino (Vitaceae), is a herbal medicine which has been widely used in East Asia. The present study was done to explore whether the standardized extract of Ampelopsis Radix (AJW) protects dopaminergic neurons via antioxidant mechanisms in Parkinson's disease (PD) models. The effects of AJW on primary mesencephalic cultures stressed with 1-methyl-4-phenylpyridinium were investigated using tyrosine hydroxylase (TH) immunohistochemistry and reactive oxygen species measurement. The eliminative effects of AJW on the 2,2-diphenyl-1-picrylhydrazyl and 2,2′-azino-bis-(3-ethylbenzthiazoline-6-sulphonic acid) radicals were explored using colorimetric methods. The effects of AJW on the mice treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) were determined by pole test as well as TH and 8-hydroxydeoxyguanosine immunohistochemistry. AJW protected dopaminergic neurons by inhibiting reactive oxygen species generation in vitro. Moreover, AJW showed potent radical scavenging activities in vitro. In the mouse PD model, AJW protected the dopaminergic neurons in the brain, leading to motor improvements. AJW inhibited the MPTP-evoked accumulation of 8-hydroxydeoxyguanosine in the brain. These data suggest that AJW has neuroprotective effects with antioxidant mechanisms in PD models.

Introduction
Oxidative stress occurs as a result of imbalance of the free radical generation and antioxidant defense system [1]. Even under natural physiological states, however, the oxygen consumption by aerobes, which is necessary for their immediate survival, leads to the potentially detrimental reactive oxygen metabolites generation [2]. The brain is deemed to be vulnerable to oxidative damage because of its high metabolic rate and relatively low cellular regeneration ability compared with other organs [3]. In addition, dopaminergic neurons in the substantia nigra pars compacta (SNc) are thought to be more prone to oxidative stress because the self-metabolism of dopamine generates reactive oxygen species (ROS), including various peroxides and radicals [4]. Therefore, oxidative stress might contribute to the pathogenesis of Parkinson's disease (PD), a common degenerative brain disease, which is featured by selective dopaminergic neurodegeneration from the SNc to the striatum and by clinical symptoms of bradykinesia, resting tremor, and rigidity [5]. Supporting this oxidative stress hypothesis, considerable levels of oxidative damaged macromolecules such as proteins, lipids, and DNA have been reported in PD brains [6]. Evidence-Based Complementary and Alternative Medicine As a very common neurotoxin for PD models, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) causes clinical and chemical alterations in humans and rodents similar to those that occur in PD [7,8]. After administration, MPTP rapidly passes through the brain and is converted into its active form, 1-methyl-4-phenylpyridinium (MPP + ), by an oxidizing enzyme in astrocytes [9]. MPP + is transferred to dopaminergic neurons through the dopamine transporter, where it inhibits the respiratory chain by disrupting mitochondrial complex I, and leads to oxidative stress via mitochondria-dependent or -independent pathways [10]. The oxidative stress generated by MPP + subsequently results in the peroxidation of macromolecules, ultimately resulting in cell death [10].
Ampelopsis Radix, the root of Ampelopsis japonica (Thunb.) Makino, belongs to the grape family (Vitaceae) and has been widely used as a traditional medicine to treat pain and fever in East Asia [11]. It contains well-known bioactive components, including resveratrol, schizandriside, catechins, and other polyphenol compounds [11,12]. Researches on (+)catechin and (−)-epicatechin demonstrated that they have the strong free radical eliminating activities and protective effects against 6-hydroxydopamine (6-OHDA) neurotoxicities [13,14]. (+)-Catechin further exerted neuroprotective effects via antioxidant mechanisms against 6-OHDA-or MPP + -induced toxicities in various cellular systems [15]. In addition, resveratrol protects neurons in an injured-spinalcord rat model with its antioxidant properties, which increase superoxide dismutase activity and decrease malondialdehyde levels [16]. Nevertheless, few studies have examined the pharmacological properties of Ampelopsis Radix, and particularly the possible protective effects in PD models have not been demonstrated. Therefore, we explored the effects of an aqueous extract of Ampelopsis Radix on mesencephalic dopaminergic cells stressed with MPP + and C57BL/6 mice stressed with MPTP. We also examined the possible antioxidant mechanisms of this protection.

Preparation of Extract and Standardization.
A dried root of A. japonica was obtained from Jung Do Hrbal Drug Co. (Seoul, Republic of Korea). The voucher specimen (KHUOPS-MH023) was deposited in the herbarium of the College of Pharmacy, Kyung Hee University (Seoul, Republic of Korea). It was extracted with distilled water at 100 ∘ C for 2 h. The powder (AJW) was made from the extracts by filtering and lyophilization. The yield of AJW was 16.10% and it was kept at 4 ∘ C. AJW was standardized with (+)-catechin and (−)epicatechin known as constituents in A. japonica [11], using reverse-phase high-performance liquid chromatography system consisting of a Waters (Miliford, MA, USA) model 515 pump, a 717 autosampler, and a 996 photodiode array detector. Separation was conducted using Atlantis C18 column (150 × 4.6 mm, 3 m; Waters, Miliford, MA, USA) at 25 ∘ C. Mobile phases included aqueous solution of 0.2% acetonitrile (eluent M1) and methanol (eluent M2). The gradient elution was carried in the way that the eluent M2 was linearly increased from 0 to 50% for 0 to 24 min and from 50 to 100% for 24 to 40 min at a flow rate of 1.2 mL/min. The injection volume was 10 L, and the detector wavelength was set at 280 nm. AJW and reference to the calibration curve obtained with (+)-catechin and (−)-epicatechin were analyzed in triplicates. (+)-Catechin and (−)-epicatechin were found in AJW at a mean level of 1.81 ± 0.16 mg/g and 1.40 ± 0.04 mg/g, respectively.

Primary Cultures of Rat Mesencephalic Cells.
Mesencephalic cell cultures were derived from 14-day embryos of Sprague-Dawley rat (Daehan Biolink Co., Eumseong, Republic of Korea). Mesencephalons were dissected, collected, dissociated, and seeded in PLL precoated 24-well plates at a density of 1.0 × 10 5 cells/well. Cultures were maintained in a humidified incubator of 5% CO 2 at 37 ∘ C in minimal essential medium with 6 g/L glucose, 2 mM glutamine, and 10% FBS. On the 6th day in vitro, cells were treated with AJW for 1 h and stressed with 12 M MPP + for a further 23 h. An equal volume of vehicles was given to the control and toxin groups. Then, cells were fixed with 4% PFA at room temperature for 30 min. The cells were stored in PBS at 4 ∘ C for immunohistochemistry.

Determination of Intracellular ROS Level.
Intracellular ROS was measured using a fluorescent probe, H 2 DCF-DA. Intracellular H 2 O 2 or low-molecular weight peroxides oxidize H 2 DCF-DA to the highly fluorescent compound dichlorofluorescein (DCF). Cells were seeded in PLL precoated 24-well plates at a density of 1.0 × 10 5 cells/well, and on the 6th day in vitro, cells were treated with AJW for 1 h and stressed with 12 M MPP + for a further 23 h. An equal volume of vehicles was given to the control and toxin groups. Cells were incubated with 20 M H 2 DCF-DA at 37 ∘ C for 30 min, the cells on cover slips were mounted on gelatin-coated glass slides; they were photographed with a fluorescence microscope (BX51T-32F01; Olympus Co., Tokyo, Japan). The fluorescence intensity of DCF was measured by a fluorescence microscope, and was expressed as a percentage of the value in the vehicle-treated control group.

Measurement of DPPH Radical Scavenging Activity.
AJW at various concentrations of 1-1000 g/mL was mixed with 0.2 mM DPPH ethanol solution (1 : 1). After incubation at room temperature in the dark for 30 min; the absorbance of the mixture was determined at 517 nm using spectrophotometer (VersaMax microplate reader; Molecular Device, Sunnyvale, CA, USA). Also, the activity of AJW was expressed as half maximal inhibiting concentration (IC 50 ) which is defined as the concentration of AJW required to scavenge 50% of DPPH radicals. IC 50 values were estimated by a nonlinear regression using the GraphPad Prism software (GraphPad Software Inc., San Diego, CA, USA).

Measurement of ABTS Radical Cation Scavenging Activity.
ABTS solution of 7.4 mM was added to 2.6 mM potassium persulfate for 1 day before starting the experiment in the dark. AJW at various concentrations of 1-1000 g/mL was mixed with 7.4 mM ABTS solution with 2.6 mM potassium persulfate. After incubation at room temperature for 5 min, the absorbance of the mixture was determined at 732 nm using spectrophotometer. Also, the activity of AJW was expressed as IC 50 which is defined as the concentration of AJW required to scavenge 50% of ABTS radical cations. IC 50 values were estimated by a nonlinear regression using the GraphPad Prism software.

Animals and Treatment.
Animal maintenance and treatment were carried out in accordance with the Principles of Laboratory Animal Care (NIH publication number 85-23, revised 1985) and the Animal Care and Use guidelines of Kyung Hee University, Seoul, Republic of Korea. Male C57BL/6 mice (8 weeks old) were purchased from Daehan Biolink. Animals were assigned to four groups: (1) Group 1 (vehicle-treated group), (2) Group 2 (MPTP-only treated group), (3) Group 3 (MPTP + AJW 10 mg/kg/day treated group), and (4) Group 4 (MPTP + AJW 100 mg/kg/day treated group). The mice were housed at an ambient temperature of 23 ± 1 ∘ C and a relative humidity of 60 ± 10% under a 12-h light/dark cycle and were allowed free access to water and food. AJW dissolved in saline was administered orally at 10 or 100 mg/kg/day for 7 days. MPTP (MPTP base form; 20 mg/kg) in saline was injected intraperitoneally four times at 2 h intervals on the last day of AJW treatment.

Behavioral Test and Brain Tissue Preparation.
We performed the pole test, on the seventh day after the last MPTP injection. The mice were placed head upward near the top of a vertical rough-surfaced pole (diameter 8 mm, height 55 cm). The time taken for the mice to turn completely downward (time to turn, T-turn) and the time taken to reach the floor (locomotion activity time, T-LA) were recorded, with a cut-off limit of 30 sec. After the pole test, the mice were anesthetized with 50 mg/kg Zoletil (intramuscularly) and were rapidly perfused transcardially with PBS, followed by 4% PFA in 0.1 M phosphate buffer (PB). Then, brains were rapidly taken out, postfixed in 4% PFA, and processed for cryoprotection in 30% sucrose at 4 ∘ C. Frozen brains were cut into 30 m coronal sections using a cryostat microtome (CM3000; Leica, Wetzlar, Germany). Then, the tissues were stored in storing solution containing glycerin, ethylene glycol, and PB at 4 ∘ C for immunohistochemistry.
2.9. Immunohistochemistry. Fixed mesencephalic cells on cover slips and free-floating brain sections were rinsed in PBS at room temperature before immunostaining. They were pretreated with 1% H 2 O 2 in PBS for 15 min. For dopaminergic neuron detection, they were incubated with a rabbit anti-TH antibody (1 : 2000 dilution) overnight at 4 ∘ C. For detection of oxidative DNA damage, the treated brain sections were heated in citric acid (0.1 M, 90 ∘ C) for 5 min for antigen retrieval, incubated in blocking solution (10% goat serum + 1% BSA in PBS) for 1 h, and incubated with mouse anti-8-OHdG antibody (1 : 50 dilution in blocking solution) overnight at 4 ∘ C. They were then incubated with a biotinylated anti-mouse and anti-rabbit IgG, respectively, for 1 h followed by incubation in ABC solution for 1 h at room temperature. The activity was visualized with DAB for 3 min. After every incubation step, the cells and tissues were washed three times with PBS. Finally, the mesencephalic cells on cover slips were mounted on gelatin-coated glass slides and airdried. The free-floating brain tissues were mounted on gelatin-coated slides, dehydrated, cleared with xylene, and cover-slipped using histomount medium. All of them were photographed with an optical light microscope (BX51T-32F01; Olympus Co.). For quantification of the effect of AJW in the mesencephalic dopaminergic cells, TH-positive cells were counted on at least four cover slips from independent experiments for each condition. Quantification of the effect of AJW in brain tissues was performed by counting the THpositive cell numbers and the 8-OHdG-positive cell numbers in the SNc at ×100 magnification under a microscope and by measuring the optical density of TH-positive fibers in the striatum at ×40 magnification using an ImageJ software (Bethesda, MD, USA). Data were expressed as a percentage of the value in the vehicle-treated control group.

Statistical
Analysis. The data were expressed as mean ± standard error of the mean (SEM). Statistical significance was determined by one-way analysis of variance, followed by Tukey's multiple comparison test, using GraphPad Prism software. In all analyses, < 0.05 was deemed to indicate statistical significance.

Inhibitory Effects of AJW on ROS Production by MPP + in Primary Mesencephalic Cells.
To evaluate the inhibitory effects of AJW on ROS generation by MPP + in primary mesencephalic cells, we used H 2 DCF-DA. Exposure to 12 M MPP + exhibited significantly increased intensity of DCF by 171.15 ± 5.37% relative to the control group, while the treatment with AJW at 0.1 and 1 g/mL showed markedly decreased intensity by 144.87 ± 5.51 and 129.51 ± 5.79% relative to the control group, respectively ( Figure 2).

Radical Scavenging Activities of AJW.
To examine the antioxidant capacities of AJW, we performed DPPH free radical and ABTS radical cation scavenging activity assays. In both assays, AJW exhibited dose-dependently increased activities similar to the water extract of Scutellariae Radix (SBE), used as a positive control. AJW at 1000 g/mL exhibited 96.12 ± 0.52% and 93.64 ± 0.04% of scavenging activities in DPPH (Figure 3(a)) and ABTS (Figure 3(b)) assays, respectively. DPPH IC 50 s for AJW and SBE were 36.12 ± 2.79 and 39.28 ± 1.02 g/mL, whereas ABTS IC 50 s for AJW and SBE were 64.26 ± 0.06 and 54.30 ± 0.33 g/mL, respectively.  (Figure 4). However, the times of AJW 10 mg/kg/day treated group were shortened as 19.20 ± 6.14 sec and 21.49 ± 5.28 sec, respectively, and the AJW 100 mg/kg/day treated group showed more shortened T-turn as 4.06 ± 0.87 sec and T-LA as 9.39 ± 0.94 sec (Figure 4).

Inhibitory Effects of AJW on Dopaminergic Neuronal Loss by MPTP in Mice.
In the mouse brain, MPTP induced severe dopaminergic cell death in the SNc and the striatum. In the MPTP-only treated mice, the number of TH-positive cells in the SNc and the optical density in the striatum were decreased by 37.46±3.46 and 39.53±1.38% relative to the control group, respectively ( Figure 5). However, these values are significantly increased by AJW 10 mg/kg/day treatment as 53.83 ± 9.54 and 45.34 ± 4.41% relative to the control group ( Figure 5). Also, the values of AJW 100 mg/kg/day treated group more largely increased as 60.48 ± 4.68 and 58.48 ± 3.09% relative to the control group.

Inhibitory Effects of AJW on 8-OHdG Accumulation by MPTP in Mice.
To evaluate the inhibitory effects of AJW on 8-OHdG accumulation by MPTP in mice, we used anti-8-OHdG antibody. In the SNc of MPTP-only treated mice, large accumulation of 8-OHdG occurred, whereas in the control group, there is nothing shown (Figure 6). This accumulation  is effectively prevented by treatment with AJW at 10 and 100 mg/kg/day as 85.48 ± 6.31 and 35.26 ± 13.92% of the value in the MPTP-only treated group ( Figure 6).

Discussion
In this study, we demonstrated that AJW protects dopaminergic neurons against MPP + toxicities in a primary culture system and against MPTP toxicities in mice through antioxidant mechanisms.
First, to investigate whether AJW protects dopaminergic neurons from MPP + toxicity, we performed an immunohistochemistry of TH, the crucial enzyme in dopamine biosynthesis [17], in primary cultured mesencephalic cells. MPP + , the active metabolite of MPTP, is known to result in selective dopaminergic neuronal degeneration via inhibiting mitochondrial complex I and increasing calcium permeability of the mitochondrial membrane generating ROS in cellular PD models [18]. In this study, AJW remarkably protected dopaminergic neurons against MPP + toxicity, leading to an increase in the TH-positive cell numbers and preservation of TH-positive cell morphologies.
Next, to explore the protective mechanisms of AJW from MPP + -induced neurotoxicity, we measured intracellular ROS level in primary cultured mesencephalic cells treated with MPP + and/or AJW. ROS, generated mostly during the incomplete metabolic reduction of oxygen to water in aerobes, include superoxide, nitric oxide, hydroxyl radical, H 2 O 2 , and peroxynitrite [3]. All of these species are redox-active and can interact with nearby cellular components, resulting in wide-ranging damage [3]. Moreover, as one of the toxic events induced by MPP + , the overproduction of ROS readily damages dopaminergic neurons, which are especially vulnerable to oxidative stress [1,19,20]. In this study, AJW remarkably inhibited the intracellular ROS production triggered by MPP + in primary mesencephalic cells. In addition, AJW showed potent radical scavenging activity comparable to SBE, a positive control that has strong antioxidant and neuroprotective effects [21], in DPPH and ABTS assays, suggesting that AJW scavenges the free radicals overproduced by MPP + .
Next, to evaluate the effects of AJW in vivo, we performed behavioral tests and brain tissue stereology after treating C57BL/6 mice with MPTP and/or AJW. The MPTP-treated mouse has been widely used as an in vivo PD model because MPTP induces PD-like motor deficits such as bradykinesia and rigidity and selective dopaminergic neuronal loss in the SNc [22]. In the pole test, which measures bradykinesia [23], AJW-treated mice showed significant improvement in the Tturn and T-LA to the level of the control group, compared with the MPTP-only treated group. We confirmed these effects of AJW in TH immunohistochemistry, showing that the AJW treatment protects both dopaminergic neurons in the SNc and its fibers in the striatum compared with the MPTP-only treated group. During MPTP-induced neurotoxicities, oxidative stress increases markedly and leads to the peroxidation of macromolecules [10]. Therefore, we examined the inhibitory effects of AJW on 8-OHdG accumulation in the SNc. 8-OHdG is the major DNA oxidative adduct, and it increases in the SNc and in the urine of PD patients [5]. Moreover, 8-OHdG levels in urine have been suggested to be a potential biomarker for investigating the progress of PD [5]. In our study, the MPTP-only treated mice showed significantly increased 8-OHdG accumulation in the SNc, whereas the control group showed nothing in the same region. However, AJW treatment prevented this accumulation, suggesting that AJW has inhibitory effects on the oxidative stress induced by MPTP. From these results, we confirmed that AJW protects dopaminergic neurons against MPTP-induced neurotoxicities through antioxidant mechanisms in PD models both in vitro and in vivo.
Most neurodegenerative diseases are heterogeneous and genetically complex. In PD, more than 90% of the cases are not linked to a single mutation [24]. Due to this multiplicity in the pathological processes of neurodegenerative diseases, materials that target more than one pathophysiological event  Figure 5: Protective effects of AJW on dopaminergic neuronal loss by MPTP in mice. Saline or AJW at 10 or 100 mg/kg/day was administered orally for 7 days, and MPTP 20 mg/kg was injected intraperitoneally four times at 2 h intervals on the last day of AJW treatment. After the behavioral test, dopaminergic neurons were determined using TH-immunohistochemistry. The numbers of TH-positive neurons in the SNc were counted (a) and the optical density of TH positive fibers in the striatum was measured (b). Representative images are shown of the SNc ((c)-(f)) and the striatum ((g)-(j)) of each group. Control group ((c) and (g)), MPTP-only treated group ((d) and (h)), MPTP + AJW 10 mg/kg/day treated group ((e) and (i)), and MPTP + AJW 100 mg/kg/day treated group ((f) and (j)). Scale bar = 100 m ((c)-(f)) and 400 m ((g)-(j)). Values are indicated as the mean ± SEM. * * * < 0.001 compared with the control group, ### < 0.001 compared with the MPTP-only treated group.
in cell death cascades have been investigated [25]. And many drugs with single target often fail to show the expected effect in further studies [24]. However, because herbal medicines contain multiple compounds, they potentially could have multiple functions and produce stronger effects than a single compound via the synergism of their constituents [26]. Therefore, candidate herbal medicines have been evaluated recently [27]. For example, Cassia obtusifolia L. seed extract showed protective effects in experimental models of memory impairment, hippocampal cell death, ischemia, and PD [22]. Similarly, the root extract of Polygala tenuifolia Willd was reported to confer neuroprotection in various experimental models, such as memory impairment, cerebral ischemia, chronic stress, anxiety, and PD [17]. In addition, Scutellaria baicalensis Georgi root extract, another herbal medicine, protected neurons against ischemia, PD, Alzheimer's disease, and spinal cord injury in various experimental models [22]. Chemical profile of Ampelopsis Radix is rarely known, but Kim discovered that it contains resveratrol, (+)-catechin, (−)-epicatechin, schizandriside, (+)-gallocatechin, and (−)epicatechin gallate, showing that (+)-catechin is the highest constituent in Ampelopsis Radix [11] and the content is similar to our result. Although the content of (+)-catechin in AJW, 0.18%, is not high, it could be enough to show activities in that (+)-catechin has been demonstrated to have strong neuroprotective activities in MPTP-treated mice even at 0.3 mg/kg [28] and to have good bioavailability [29]. In addition to (+)-catechin, (−)-epicatechin and resveratrol, which are known to be neuroprotective in various PD models [13,15,30], have been demonstrated to cross blood-brain barrier [31,32]. Furthermore, the mixture of (+)-catechin and (−)epicatechin was reported to have synergistic neuroprotective effects against amyloid toxicities in vitro [33]. Taken together, the beneficial effects of AJW might be due to (+)catechin mainly, and also other unknown compounds in AJW might synergistically contribute to the effects. Furthermore, those compounds could make AJW have potential to be applied to various neurodegenerative disease models, as the other herbal medicines mentioned above do. Nevertheless, the possible multimodal neuroprotective effects of AJW and the active compounds in AJW need to be further studied.

Conclusions
AJW protected dopaminergic cells from MPP + toxicities in primary mesencephalic cultures and against MPTP toxicities in C57BL/6 mice. The neuroprotective effects of AJW are due to the antioxidant activities including inhibition of ROS, free radicals, and 8-OHdGs. These results indicated that AJW is a potential candidate as a neuroprotective agent in PD, although the major active ingredients in AJW and the possible protective effects of AJW in other PD models need to be further studied.


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Identification of Three Genes Encoding for the Late Acyltransferases of Lipid A in Cronobacter sakazakii

Lipid A, the hydrophobic anchor of lipopolysaccharide, is an essential component in the outer membrane of most Gram-negative bacteria. Food-borne pathogen Cronobacter sakazakii synthesizes two lipid A species, differing by the length of the secondary acyl chain. In this work, we identified three genes ESA02293, ESA02951 and ESA01386 encoding for the late acyltransferases of lipid A biosynthesis pathway in C. sakazakii. Based on the sequence alignment, proteins YP_001438378.1 encoded by ESA02293, YP_001439016.1 encoded by ESA02951, and YP_001437482.1 encoded by ESA01386 are homologous to E. coli LpxL, LpxP and LpxM, respectively. Functions of the three acyltransferases were confirmed by overexpressing the genes in E. coli, isolating lipid As and analyzing their structures using an ESI/MS. C. sakazakii LpxL and LpxM transfer a C14:0 secondary acyl chain to the 2′- and 3′-position of lipid A, respectively. C. sakazakii LpxP can transfer either a C16:1 or a C14:0 secondary acyl chains to the 2′-position of lipid A.

Introduction
Lipid Ais the hydrophobic anchor of lipopolysaccharide in the outer membrane of Gram-negative bacteria [1]. It is recognized by the TLR4/MD2 receptor of the innate immune system, which triggers an inflammatory response accompanied by massive over-production of cytokines and leads to Gram-negative septic shock [2][3][4]. Cronobacter sakazakii is a Gram-negative bacterial pathogen associated with contaminated, temperature-abused, powdered infant formula, which can cause meningitis and enterocolitis in neonates. Cronobacter sakazakii synthesizes two lipid A species, but the biosynthesis of lipid A in C. sakazakii has not been well characterized [5,6].
In this work, we identified three genes encoding the late acyltransferases of lipid A in C. sakazakii BAA894. Three enzymes encoded by genes ESA02293, ESA02952 and ESA01386 are homologous to E. coli LpxL, LpxP and LpxM, respectively. Like E. coli LpxM, C. sakazakii LpxM transfers a C14:0 secondary acyl chain to the 3′-position of lipid A, C. sakazakii LpxL and LpxP, however, functions differently from E. coli LpxL and LpxP. C. sakazakii LpxL transfers a C14:0 secondary acyl chain to the 2′-position of lipid A, while C. sakazakii LpxP can transfer either a C16:1 or a C14:0 secondary acyl chain to the 2′-position of lipid A even at ambient temperatures. These three acyltransferases might play important roles in biosynthesis of lipid A in C. sakazakii.

Three Genes Encoding Late Acyltransferases Were Found in C. sakazakii BAA894
Lipid A samples were extracted from E. coli W3110 and C. sakazakii BAA894, respectively, and analyzed using ESI/MS in the negative ion mode (Figure 1). Two major ions at m/z 1796.1 and 1716.2 were observed in the spectrum of E. coli Lipid A as expected ( Figure 1A). The peak at m/z 1796.1 was created by the molecular ion [M − H] − of lipid A; the peak at m/z 1716.2 arisen by loss of a phosphate from the lipid A during the extraction procedure [5]. The spectrum of C. sakazakii lipid A showed four major peaks at m/z 1716.1, 1744.1, 1796.1 and 1824.1, respectively ( Figure 1B). Two of the peaks (m/z 1716.1 and 1796.1) are the same to that of E. coli lipid A, while the other two peaks (m/z 1744.1 and 1824.1) differ from the first two by 28 Da, suggesting that C. sakazakii BAA894 synthesizes two different species of lipid A which differ at the length of secondary fatty acyl chains at 2′ and 3′-position [6]. To explore the nature of diversity of the secondary fatty acyl chains of lipid A in C. sakazakii, BLASTp search against the genome of C. sakazakii BAA894 was performed, using E. coli LpxL, LpxP and LpxM as queries. Three late acyltransferases YP_001438378.1, YP_001439016.1, and YP_001437482.1 were revealed and the sequence alignments of all these proteins are shown in Figure 2. YP_001438378.1 is encoded by ESA02293, contains 306 amino acids, and shares 79%, 51% and 31% identity to E. coli LpxL, LpxP and LpxM, respectively. YP_001439016.1 is encoded by ESA02951, contains 307 amino acids, and shares 55%, 71% and 28% identity to E. coli LpxL, LpxP and LpxM, respectively. YP_001437482.1 is encoded by ESA01386, contains 322 amino acids, and shares 30%, 28% and 75% identity to E. coli LpxL, LpxP and LpxM, respectively. Based on the sequence similarity, YP_001438378.1 is proposed as C. sakazakii LpxL, YP_001439016.1 as C. sakazakii LpxP and YP_001437482.1 as C. sakazakii LpxM.
To confirm the function of the three putative acyltransferases, genes ESA02293, ESA02951 and ESA01386 were cloned into pWSK29, and transformed into E. coli mutant MLK1067 and MKV15b. MLK1067 was constructed by inactivating lpxM in E. coli and can only synthesize pentaacylated lipid A [16]; MKV15b is a Lpxl, LpxM, LpxP triple E. coli mutant and could only synthesize tetraacylated lipid A [17].
Two major ions at m/z 1403.9 and 1323.9 were observed in the spectrum of lipid A from the control MKV15b/pWSK29 ( Figure 3A). The peak at m/z 1403.9 is created by the molecular ion [M − H] − of lipid A containing two phosphate groups and four primary fatty acid chains; the peak at m/z 1323.9 arises by loss of a phosphate from the molecular ion during the extraction procedure. In the spectrum of lipid A sample from MKV15b/pWSK29-ESA02293, two major ions were also observed ( Figure 3B). The molecular ion at m/z 1614.1 differs from that of the lipid A from MKV15b/pWSK29 by 210 amu, that is, a C14:0 fatty acid unit. The peak at m/z 1534.1 arises by loss of a phosphate from the molecular ion. The results indicate that the enzyme encoded by ESA02293 transfers a C14:0 secondary acyl chain to the 2′-position of lipid A. Six peaks were observed in the spectrum of lipid A sample from MKV15b/pWSK29-ESA02951 ( Figure 3C). Peaks at m/z 1323.9 and 1403.9 are the same to that found in spectrum of lipid A from both MKV15b/pWSK29. Peaks at m/z 1534.1 and 1614.1 are the same to that found in spectrum of lipid A from MKV15b/pWSK29-ESA02293. This indicates that the enzyme encoded by ESA02951 can transfer a C14:0 secondary acyl chain to the 2′-position of lipid A, but its efficiency is not as high as the enzyme encoded by ESA02293. In addition, there were two new peaks at m/z 1560.1 and 1640.1 in the spectrum of lipid A from MKV15b/pWSK29-ESA02951; the former might arise by loss of a phosphate form the later. The molecular ion at m/z 1640.1 differs from that of the molecular ion of lipid A from MKV15b/pWSK29 by 236 amu moiety, that is, a C16:1 fatty acid unit. The results indicate that the enzyme encoded by ESA02951 could transfer a C14:0 or a C16:1 secondary acyl chain to the 2′-position of lipid A.
The spectrum of lipid A sample from MK15b/pWSK29-ESA01386 only showed a peak at m/z 1323.9 created by the molecular ion losing a phosphate, which is exactly the same as that of the control strain MKV15b/pWSK29 ( Figure 3D), suggesting the enzyme encoded by the gene ESA01386 does not function on the substrate lipid A with only four primary fatty acyl chains. Based on the analysis of sequence and function, the acyltransferase encoded by ESA02293 should be the LpxL in C. sakazakii, and the acyltransferase encoded by ESA_02952 should be the LpxP in C. sakazakii.
Because there is a second molecular ion at m/z 1796.2 in the C. sakazakii lipid A mass spectrum ( Figure 1B), there might be another acyltransferase in C. sakazakii which transfers a C12:0 secondary acyl chain to lipid A. This acyltransferase may have no sequence similarity to LpxL. This situation was observed for LpxH, a key enzyme in the lipid A biosynthesis of E. coli. A new enzyme LpxI which has no sequence similarity to LpxH but generates the same products by a different route was recently found in Caulobacter crescentus [18]. Unlike E. coli LpxP which transfers a C16:1 acyl chain only at low temperature (12 °C), C. sakazakii LpxP can function at ambient temperature and can transfer either a C14:0 or a C16:1 secondary acyl chains. It would be interesting to study the structural difference of these two LpxP proteins to figure out the machnism for their different substrate specificity and temperature regulation.

Acyltransferase Encoded by ESA01386 Is the Homologue of E. coli LpxM
Lipid A and its derivatives were isolated from MLK1067/PWSK29, MLK1067/pWSK29-ESA02293, MLK1067/pWSK29-ESA02951, and MLK1067/pWSK29-ESA01386, respectively, and analyzed by ESI/MS (Figure 4). Six peaks were observed in the spectrum of lipid A sample from MLK1067/pWSK29-ESA02951 ( Figure 4D). Peaks at m/z 1506.0 and 1586.0 are the same to that found in spectrum of lipid A from MLK1067/pWSK29. Peaks at m/z 1534.1 and 1614.1 are the same to that found in spectrum of lipid A from MKV15b/pWSK29-ESA02293 or MLK1067/pWSK29-ESA02293. Peaks at m/z 1560.1 and 1640.1 are the same to that found in the spectrum of lipid A from MKV15b/pWSK29-ESA02951 ( Figure 3C). These data further confirm that C. sakazakii LpxP can transfer either a C16:1 or a C14:0 secondary acyl chain to the 2′-position of lipid A.

DNA Preparation and PCR Techniques
Plasmid DNA was prepared by using the EZ-10 spin column plasmid mini-preps kit from Bio Basic Inc. (Markham, Canada). Fifty microliters of PCR reaction mixture was used in this study, which contains 5 μL 10× Extaq buffer, 4 μL dNTP (2.5 mM each), 1 μL plasmid template, 1 μL forward primer (20 μM), 1 μL reverse primer (20 μM), and 0.5 μL Extaq polymerase. The PCR reaction was started at 94 °C for 5 min, followed by 35 cycles of denaturation (30 s at 94 °C), annealing, and extension. PCR products were purified using the TIAN gel purification kit from Tiangen (Beijing, China). Primers synthesis and DNA sequencing were performed by Sangon (Shanghai, China).

Lipid A Isolation from Different E. coli Strains
Lipid A was isolated using the Bligh-Dyer method [19]. Briefly, 200 mL cultures were grown in LB broth at 37 °C to OD 600 of 1.0. Cells were harvested by centrifugation at 8000 rpm and washed twice with ddH 2 O. The cell pellet was resuspended in 76 mL of a single-phase Bligh-Dyer mixture containing chloroform/methanol/H 2 O (1:2:0.8, v/v/v), incubated with stirring at room temperature for 1 h. The insoluble debris was collected by centrifugation at 2000 rpm for 20 min and washed three times with single-phase Bligh-Dyer mixture. The debris was resuspended in 27 mL of 12.5 mM sodium acetate (pH 4.5), heated at 100 °C for 30 min, and cooled to room temperature. The suspension was mixed with 30 mL chloroform and 30 mL methanol, and the two phases were separated by centrifugation. The lower phase contains lipid A, and was dried under a stream of nitrogen.

Mass Spectrometry Analysis
All the mass spectra of lipid A samples were acquired on a Waters SYNAPT quadrupole time-of-flight mass spectrometer equipped with an electrospray ionization (ESI) source. Lipid A samples were dissolved in chloroform/methanol (2:1, v/v) and subjected to ESI/MS in the negative ion mode. Data acquisition and analysis were performed using Mass Lynx V4.1 software [20].

Conclusions
Unlike E. coli which usually synthesizes only one lipid A species, C. sakazakii BAA894 synthesizes two different species of lipid A, differing on the length of the secondary acyl chain at 2′-position. Here, we identified three late acyltransferases in C. sakazakii BAA894. The enzyme encoded by the gene ESA02293 is homologous to E. coli LpxL, and transfers a C14:0 acyl chain to the 2′-position of lipid A. The enzyme encoded by the gene ESA_02953 is homologous to E. coli LpxP, and transfers either a C16:1 or a C14:0 acyl chains to the 2′-position of lipid A. The enzyme encoded by ESA01386 is homologous to E. coli LpxM, and transfer a C14:0 acyl chain to the lipid A at 3′-position. Since the number and length of secondary acyl chains of lipid A are important for the recognition of TLR4/MD2 to LPS, these late acyltransferases found in this study might be useful to construct various lipid A moieties for studying the immune responses.