Parental problem drinking, parenting, and adolescent alcohol use

The present study examined whether parental problem drinking affected parenting (i.e., behavioral control, support, rule-setting, alcohol-specific behavioral control), and whether parental problem drinking and parenting affected subsequent adolescent alcohol use over time. A total of 428 families, consisting of both parents and two adolescents (mean age 13.4 and 15.2 years at Time 1) participated in a three-wave longitudinal study with annual waves. A series of path analyses were conducted using a structural equation modeling program (Mplus). Results demonstrated that, unexpectedly, parental problem drinking was in general not associated with parenting. For the younger adolescents, higher levels of both parenting and parental problem drinking were related to lower engagement in drinking over time. This implies that shared environment factors (parenting and modeling effects) influence the development of alcohol use in young adolescents. When adolescents grow older, and move out of the initiation phase, their drinking behavior may be more affected by other factors, such as genetic susceptibility, and peer drinking.
Introduction
Time trends in Dutch epidemiological research show a significant increase in frequency and intensity of alcohol consumption among 12–15 year olds (Poelen et al. 2005). Alarming high numbers of Dutch adolescents (75%) also report problem drinking behaviors such as binge drinking (consuming more than 5 amounts of alcohol on one occasion), when compared to their American counterparts (19%; Newes-Adeyi et al. 2005; Van Dorsselaer et al. 2007). In addition, previous studies show that high levels of alcohol-related problems such as social consequences of alcohol use (e.g., family problems) and dependence symptoms (e.g., loss of control) occur frequently in Western societies, with approximately 10% of both American and Dutch populations reporting 3 or more alcohol-related problems (Cornel et al. 1994; NIAAA 1997; Van Dijck and Knibbe 2005; Wallitzer and Connors 1999).
Parental problem drinking increases risk for alcohol use in children (e.g., Chassin et al. 1996; Hawkins et al. 1992; Sher et al. 1991). Children of alcoholics are not only at a higher risk for early alcohol initiation (Hill et al. 2000), they also show a greater increase in alcohol consumption over time than adolescents without alcoholic parents (Chassin and Barrera 1993). In addition, children with a family history of alcoholism show more escalation of alcohol use (Lieb et al. 2002), and more often develop alcohol disorders and dependence (Hill et al. 2000) than children without a family history of alcoholic parents.
In an attempt to explain these associations, social theorists suggested a modeling effect (Bandura 1977) that causes youngsters to imitate their parents. Others have proposed that parental substance abuse may impair parenting (Sher 1991; Van der Vorst et al. 2006; Van Zundert et al. 2006), which subsequently may affect adolescent alcohol consumption. As Mayes and Truman (2002) pointed out, personality characteristics, disabilities, or impairments accompanying an addiction may affect the ability to raise a child. In addition, substance use alters the state of consciousness, memory, affect, and impulse control, each of which may impair the adult’s parenting capacities. Indeed, empirical studies have shown that children of alcoholic parents receive less discipline (King and Chassin 2004) and less emotional support from their parents (Rutherford et al. 1997). In addition, Chassin et al. (1993) found that parental alcoholism decreased the amount of parental monitoring. This is all the more problematic, since discipline and rule setting, in turn, reduce the likelihood of youngsters’ drunkenness (Engels and Van der Vorst 2003), and more parental monitoring is related to less heavy drinking in adolescents (Kerr and Stattin 2000; Van der Vorst et al. 2006). In addition, parental support appears to prevent early onset of alcohol use, as well as frequent and heavy alcohol use among adolescents (Barnes et al. 1994). Thus, numerous cross-sectional studies have demonstrated associations between parental alcohol use, parenting, and adolescent alcohol consumption (e.g., Chassin et al. 1993; Kerr and Stattin 2000). However, with the exception of two prospective studies that showed that monitoring by fathers and parental discipline mediated between parental alcoholism and adolescents’ alcohol use (Chassin et al. 1996; King and Chassin 2004), longitudinal studies are lacking. In addition, to allow generalization of findings and to examine effects in potentially less severe cases, it is necessary to investigate community-based samples (Russell et al. 1990). Accordingly, the central aim of the present study was to longitudinally examine the nature of the relations between parental problem drinking, parenting, and adolescent alcohol use in a three-wave community-based sample.
Alcohol-specific parenting
Although studies on parenting and adolescent alcohol use have been informative, two important issues have hardly been addressed. First, most studies on the link between parenting and adolescent alcohol use have focused on general parenting. However, alcohol-specific socialization, which refers to the actions parents undertake to discourage or prevent their offspring from drinking (Jackson et al. 1999; Van der Vorst et al. 2005), has received less attention in relation to parental drinking and adolescent alcohol use. Wood et al. (2004) found that late adolescents drank less alcohol when their parents disapproved of drinking. In addition, imposing strict rules prevented youngsters from heavy drinking (Jackson et al. 1999; Van der Vorst et al. 2005; Yu 2003). However, whether parental problem drinking affects alcohol-specific parenting has not yet been examined. From studies on smoking we know that parents who smoke are less frequently engaged in anti-smoking socialization practices than parents who do not smoke (Harakeh et al. 2005). A similar process might be at work regarding alcohol-specific socialization, suggesting that parents with alcohol problems may engage less frequently in alcohol-specific socialization, and as such provide fewer alcohol-specific rules, are more permissive towards alcohol use and exert less alcohol-specific control.
Second, it is crucial to acknowledge that the association between parenting and adolescent problem behavior may be bidirectional: Parents do not only influence their children, but children’s behavior also exerts an effect on parents. Indeed, recent longitudinal studies showed a bidirectional relation between parenting and adolescent substance use. Adolescent drinking, smoking or deviant behavior decreased the level of parental monitoring and rule setting (Huver et al. 2006; Stice and Barrera 1995; Van der Vorst et al. 2006). This implies that when these child effects are not taken into account, this may lead to an overestimation of parental influences (Kerr and Stattin 2003; Van der Vorst et al. 2006).
Current study and expectancies
We longitudinally investigated the direct effect of parental problem drinking on adolescent alcohol use, the role of alcohol-specific and general parenting practices in this relationship, and the reciprocal effects of adolescent alcohol use on parenting (see Fig. 1). It was expected that parental problem drinking would have a direct positive effect on adolescent alcohol use, with more parental alcohol-related problems leading to more adolescent alcohol use. In addition, an indirect relationship was expected via parenting; more specifically, higher levels of parental problem drinking were thought to have a negative effect on both general and alcohol-specific parenting practices, which in turn would lead to more adolescent alcohol use. Moreover, the drinking behaviors of the adolescents were expected to influence parenting, with more adolescent drinking resulting in less parental discipline and monitoring.
Fig 1Longitudinal Model of Parental Problem Drinking, Parenting, and Adolescent Alcohol Use
Methods
Participants and recruitment
The data were derived from an ongoing Dutch longitudinal survey called ‘Family and Health’, which examines different socialization processes in relation to various health behaviors in adolescence (see Harakeh et al. 2005; Van der Vorst et al. 2005). A total of 428 Dutch families, consisting of mother, father, and two adolescent children, participated in our study in the first wave (2002–2003). Families were included when the parents were married or living together, and when all family members were biologically related. Families with twins, or with mentally or physically disabled offspring were excluded. Numbers of drop-outs were extremely low in the second (2003–2004) and third wave (2004–2005), with 416 (97%) and 404 (94%) participating families, respectively.
The majority of the families were of Dutch origin (>95%). The mean age of the participants at Time 1 was 15.2 years (SD = .60) for the older adolescents, 13.4 years (SD = .50) for the younger adolescents, 46.2 years (SD = 4.00) for the fathers, and 43.8 years (SD = 3.57) for the mothers. Of the older adolescents, 47% were girls, compared to 52% in the younger group. Concerning educational level, an equal distribution was realized, with about one third of the adolescents following low education, one third following intermediate general education, and one third following the highest level of secondary school. The different levels of the Dutch secondary school system are comparable with the different tracks within a middle class public high school in the USA, although they may not be completely interchangeable. In our sample, when compared to national Dutch figures, the intermediate general education is slightly underrepresented, while the low and high levels are slightly overrepresented (CBS 2007).
Procedure
The families were visited at home by a trained interviewer. In his or her presence all four family members individually filled out an extensive questionnaire, which took about 2 h to complete. The participants were not allowed to consult each other or to discuss the answers. When all family members had completed the questionnaire, each family received 30 € (39 $). In addition, after completion of the first three waves of the project, 5 traveler cheques of 1,000 € (1,300 $) each were raffled among all participating families. Approval was obtained from the Central Committee on Research Involving Human Subjects on collecting the data.
Measure
Self-reports were used to measure parental problem drinking and adolescent alcohol use. The four parenting practices were based on adolescents’ reports, reflecting how they perceived their parents’ behaviors. The questions regarding the parenting variables were asked in such a way that the adolescents were able to discriminate between the parenting practices of their mothers and fathers.
Problem drinking
To measure the severity of fathers’ and mothers’ alcohol-related problems, both parents completed the problem drinking list of Cornel et al. (1994). The original scale was based on three commonly used instruments to measure problem drinking: CAGE (Cut down, Annoyed, Guilty, Eye-opener, CAGE is an acronym formed by taking the first letter of key words from each of the following questions; Mayfield et al. 1974), Short Michigan Alcohol Screening Test (SMAST; Selzer et al. 1975), and a shortened version of the Self-Administered Alcohol Screening Test (SAAST; Davis et al. 1987). Seriousness of problem drinking was developed as a Rasch scale with items arranged in order of increasing severity. The more severe the items, the less frequently they are scored positively. Since all requirements of the Rasch model were met, the items form a reliable and unidimensional scale (Cornel et al. 1994). Examples of items were ‘Do you ever drink alcohol to forget your concerns?’ (item 2) and ‘Have you ever lost your job because of your drinking’ (item 18). Respondents could respond 0 ‘no’, or 1, ‘yes’. Severity of problem drinking was reflected by the aggregated score with a maximum score of 18. Because of the skewness of the summed variable’s distribution, scores were categorized into 3 meaningful groups: 1 = never had problems due to alcohol; 2 = has had problems due to alcohol a couple of times, 3 = problem drinkers (see Cornel et al. 1994).
General parenting
To measure parental behavioral control, we used a Dutch translation of the scale developed by Kerr and Stattin (2000). The scale consisted of 5 items with response categories ranging from 1 ‘no, never’ to 5 ‘yes, always’. Examples of items were: ‘Do you need to have your mother’s permission to stay out late on a weekday evening?’ and ‘Before you go out on a Saturday night, does your father require you to tell them where you are going and with whom?’. Internal consistencies as measured with Cronbach’s alphas ranged from .71 to .90 for the reports of both adolescents, about their mothers and fathers over the three waves.
To measure parental support, we used the Relationship Support Inventory (RSI; Scholte et al. 2001) tapping several aspects of emotional and instrumental support. Examples of items were ‘My mother shows me that she loves me’ and ‘My father supports me in what I do’. The adolescents had to answer 12 items on a scale from 1 ‘absolutely untrue’ to 5 ‘absolutely true’. The amount of support was the mean score on 12 items. Cronbach’s alpha coefficients were between .76 and .88 across the three waves.
Alcohol-specific parenting
Van der Vorst et al. (2005) developed a 10-item scale to measure the degree to which parents permit their children to consume alcohol. Examples of items were: ‘I am allowed to drink alcoholic consumptions when my mother/father is at home’ and ‘I am allowed to drink alcohol on weekdays’. Participants had to respond on a 5-point scale that ranged from 1 ‘completely applicable’ to 5 ‘not applicable at all’. The internal consistency was high, with Cronbach’s alphas between .89 and .92 over the three waves.
In addition, the general behavioral control scale of Kerr and Stattin (2000) was adapted to measure behavioral control aimed at affecting adolescents’ alcohol consumption. Examples of the 5 items were ‘Do you need your mother’s permission to drink alcohol on weekdays?’ and ‘Does your mother want to know whether your friends drink alcohol?’. As in the original scale, the response categories ranged from 1 ‘never’ to 5 ‘always’. Cronbach’s alpha coefficients were between .74 and .88 across the three waves.
Adolescent alcohol use
Intensity of drinking was assessed by questions that asked about the number of glasses consumed in the previous week, during weekdays and weekends, both outside and inside the house (Engels et al. 1999). The aggregated score on these four questions was used as an indication of the adolescents’ intensity of alcohol use (Van der Vorst et al. 2005). Because of the skewness in the distribution of this variable, total scores were categorized into 7 groups (0 = 0 glasses, 1 = 1–2 glasses, 2 = 3–5 glasses, 3 = 6–10 glasses, 4 = 11–20 glasses, 5 = 21–30 glasses, 6 = 31 glasses and above).
Strategy of analyses
For the descriptive part of the analyses we applied t tests, Pearson correlations and general linear modeling with repeated measures (the latter to test changes over time in alcohol-related problems and alcohol use). We performed cross-lagged path analyses (see Fig. 1), using version 4.1 of the Mplus statistical package (Muthén and Muthén 1998–2006), to test (a) to which degree parental alcohol-related problems, parental practices and alcohol use of adolescents were stable over time, (b) whether parental problem drinking was related to parental practices and alcohol use of the adolescent over time and (c) whether parental practices and alcohol use of the adolescent were cross-related over time (Finkel 1995).
Cross relations over time allow to test causal predominance: Are specific parenting practices the ‘cause’ of adolescent alcohol use, or does adolescent alcohol use provoke specific parenting practices (Byrne 1998)? Structural regression models are generally somewhat more sophisticated than the path models used in our study because they correct for measurement error (Kline 1998, p. 211). This controlling for error variance by means of latent variables that are measured by multiple manifest indicators plus their error variance, however, also increases the number of parameters to be estimated. In addition, more complex models, i.e., models with more parameters, require larger sample sizes than do more parsimonious models in order for the estimates to be comparably stable (Kline 1998, p. 111). Kline (1998) recommends a parameter—subjects ratio of 1:10. As such, we used path models in which one manifest parameter represented all the individual items of one scale by means of the mean or sum score. The model depicted in Fig. 1 was tested for each of the four parenting variables separately. A total of 4 (parenting variables) × 2 (fathers and mothers) = 8 models were tested. The variables at T1 and the disturbance terms of the variables at T2 and T3 were free to correlate. Because adolescent alcohol consumption and parental problem drinking were relatively skewed and the measurement level was ordered more categorical (ordinal) than interval, maximum likelihood estimation methods (demanding multivariate normal distributed variables) were less suited. We used the weighted least square method with adjusted mean- and variance chi-square (WLSMV) estimator, an estimation method specifically developed for ordered categorical dependent variables (Muthén and Muthén 1998–2006). To test model fit, standard chi-square tests as well as the number of degrees of freedom (df) were replaced by robust chi-square tests (mean- and variance-adjusted chi-squares) and estimates of df (Muthén and Muthén 1998–2004, pp. 19–20). The latter estimates are dependent on sample information and this explains why df with identical models can vary across different groups.
Together with the robust chi-square tests we used two fit measures: the Root Mean Square Error of Approximation (RMSEA; Byrne 1998; Steiger and Lind 1980), and the Comparative Fit Index (CFI) of Bentler (Bentler 1990). RMSEA is utilized to assess approximate fit preferably with values less than or equal to .05, but values between .05 and .08 are indicative of fair fit (Browne and Cudeck 1993). CFI is a comparative fit index, values above .95 are preferred (Kaplan 2000), but should not be lower than .90 (Kline 1998, see also Hu and Bentler 1999, and, for commentary on existing guidelines Marsh et al. 2004). Mplus has several possibilities to handle missing values depending on the estimation method used. In our case (using the WLSMV-estimator) all available information in the data was used by means of pair-wise information of each combination of two variables.
Results
Descriptives on alcohol consumption and problem drinking
Table 1 shows the means, standard deviations, and percentages of parental problem drinking. Fathers reported higher levels of problem drinking than mothers at all three waves, as was tested with separate t tests. (T1: t(424) = 8.11, p < .001; T2: t(426) = 8.13, p < .001; T3: t(424) = 7.93, p < .001). With general linear modeling repeated measures we tested whether maternal and paternal problem drinking differed over time (within factor). Both paternal and maternal problem drinking showed significant differences over time (for fathers: F(2, 421) = 23.18, p < .001, partial eta squared (PES) = .10, and for mothers: F(2, 424) = 59.10, p < .001, and PES = .22).1 Subsequently carried out repeated contrasts revealed significant differences over time only from T1 to T2, for both parents (p < .001), but not from T2 to T3. At T1, the older adolescents consumed on average 4.36 glasses in the past week (SD = 6.81; T2: M = 7.78, SD = 10.86; T3: M = 9.75, SD = 12.35), compared to 1.23 glasses (SD = 3.41) consumed in the past week by the younger siblings (T2: M = 3.70, SD = 8.99; T3: M = 6.22, SD = 10.32). Older adolescents reported significantly higher levels of alcohol consumption than younger adolescents at all three waves (T1: t(417) = 9.30, p < .001; T2: t(414) = 6.85, p < .001; T3: t(405) = 5.09, p < .001). Repeated measures showed a significant increase in alcohol consumption over time for both adolescents, for T1–T2, and T2–T3 (for older adolescents: F(2, 394) = 34.15, p < .001, PES = .15, and for younger adolescents: F(2, 414) = 42.64, p < .001, PES = .17).
Table 1Means, standard deviations and percentages of parental problem drinking (PD) and adolescent alcohol use (A) at Time 1 (T1), Time 2 (T2), and Time 3 (T3)T1T2T3M*SD%**M*SD%**M*SD%**PD father1.84a2.1819.42.33b1.9925.52.21b2.0022.7PD mother.87a1.575.61.44b1.518.41.42b1.49 9.1A younger adolescent1.22a3.41n.a.3.11b8.35n.a.5.27c9.76n.a.A older adolescent4.37a6.80n.a.7.15b10.62n.a.8.79c12.08n.a.Note: *M represents the mean score calculated from the aggregated scores of all 18 items (maximum score = 18) of which the problem drinking scale consists (Cornel and Knibbe 1994). **Percentages of problem drinkers are computed with a cut-off score > 3. n.a. = not applicable. Values for adolescent alcohol use (A) represent the intensity of alcohol use, i.e., the number of glasses of alcohol consumed in the past week. Fathers had significantly more alcohol-related problems than mothers at all three time points, with p < .01. Older adolescents reported significantly more alcohol than younger adolescents at all three time points. Means in the same row that do not share superscripts (a, b, c) are significantly different (p < .001)
Correlations between cross-sectional and longitudinal variables
Maternal and paternal problem drinking correlated positively, but marginally with adolescents’ alcohol consumption (.02 ≤ r ≤ .19). Parental problem drinking correlated negatively with support (−.18 ≤ r ≤ −.02), and alcohol-specific behavioral control (−.22 ≤ r ≤ −.01), and positively with permissiveness (.03 ≤ r ≤ .24), while both positive and negative correlations were found between parental problem drinking and general behavioral control (−.13 ≤ r ≤ .15). Adolescent alcohol use correlated low to moderately with general behavioral control (−.24 ≤ r ≤ −.05), support (−.15 ≤ r ≤ −.02) and alcohol-specific behavioral control (−.29 ≤ r ≤ −.03), and positively with permissiveness (.18 ≤ r ≤ .46). General behavioral control correlated positively with support (.11 ≤ r ≤ .40) and negatively with permissiveness (−.29 ≤ r ≤ −.02). Moderate correlations existed between the general behavioral control scale and the alcohol-specific behavioral control scale (.19 ≤ r ≤ .55), indicating that they share the same basis, but can be seen as separate constructs. Correlation tables are available upon request.
Structural equation models
All models showed an acceptable fit (Table 2), with all Comparative Fit Indices (CFI) at least above .90 and all Root Mean Square Errors of Approximation (RMSEAs) below .08.
Table 2Fit indices for all modelsFatherMotherPMASBCSUPMASBCSUdf2630302627302928χ²36.1358.7376.0541.7847.9256.8078.4942.95p0.090.000.000.030.010.000.000.04CFI0.980.960.930.980.960.960.920.98RMSEA0.030.050.060.040.040.050.060.04Note: PM = Permissiveness, AS = Alcohol-specific behavioral control, BC = Behavioral control, SU = Support. Each column represents one model with a specific parenting variable, separately for mothers and fathers
Alcohol-specific parenting: permissiveness and alcohol-specific behavioral control
Standardized regression weights (β) of parental problem drinking, permissiveness, alcohol-specific behavioral control, and adolescent alcohol consumption showed a strong stability over time, with values between .36 and .88 (see Table 3).
Table 3Structural parameters estimates of the alcohol-specific parenting practices, parental problem drinking (PD) and adolescent alcohol use (standardized beta weight)PermissivenessAlcohol-specific behavioral controlFathersMothersFathersMothersStability paths1. PD T1–PD T2.79.76.80.762. PD T2–PD T3.82.88.82.823. Parenting OA T1–Parenting OA T2 .68.67.55.554. Parenting OA T2–Parenting OA T3.73.73.64.645. Alcohol use OA T1–Alcohol use OA T2.46.47.55.546. Alcohol use OA T2–Alcohol OA T3.66.66.67.677. Parenting YA T1–Parenting YA T2.75.76.50.498. Parenting YA T2–Parenting YA T3 .76.76.49.489. Alcohol use YA T1–Alcohol use YA T2.36.37.42.4210. Alcohol use YA T2–Alcohol use YA T3.55.54.59.57Paths from PD11. PD T1–Parenting OA T2.11**.12**.07−.0812. PD T2–Parenting OA T3.05.03−.11**−.0713. PD T1–Alcohol use OA T2.08.16**.09.18***14. PD T2–Alcohol use OA T3.02.02.03.0315. PD T1–Parenting YA T2.07.03.05−.0216. PD T2–Parenting YA T3.05.04−.14**−.15**17. PD T1–Alcohol use YA T2.00−.02.00−.0318. PD T2–Alcohol use YA T3.19***.14**.20***.15**Cross-lagged paths19. Parenting OA T1–Alcohol use OA T2.15**.12*.05.0420. Parenting OA T2–Alcohol use OA T3.02.03−.02−.0721. Alcohol use OA T1–Parenting OA T2−.01.01−.07−.0522. Alcohol use OA T2–Parenting OA T3−.04−.04−.10*−.0923. Parenting YA T1–Alcohol use YA T2.17**.17**−.23***−.24***24. Parenting YA T2–Alcohol use YA T3.04.05.05−.0325. Alcohol use YA T1–Parenting YA T2.01.02−.02−.10*26. Alcohol use YA T2–Parenting YA T3.01.01−.12*−.10*27. Alcohol use OA T1–Alcohol use YA T2.08.09.09.0928. Alcohol use OA T2–Alcohol use YA T3.11*.12*.09.11*Note: PD = Parental Problem Drinking, OA = Older Adolescent, YA = Younger Adolescent. All stability paths are significant at p < .001. The numbered paths in the table correspond to the arrowed paths depicted in Fig. 1* p < .05, ** p < .01, *** p < .001
Regarding both the older and younger adolescents, no significant associations were found between parental problem drinking and parental permissiveness, with the exception of problem drinking of both fathers and mothers at T1 which was significantly and positively related to permissiveness towards the older adolescent at T2 (for fathers: β = .11, p < .01, for mothers: β = .12, p < .01). More problem drinking of the mother at T1 and T2 was significantly associated with more alcohol use of the older adolescent at T2 and drinking of the younger adolescent at T3, respectively (β = .16, p < .01; β = .14, p < .01). Problem drinking of the father at T2 was significantly and positively associated to alcohol use of the youngest adolescent at T3 (β = .19, p < .001). More parental permissiveness at T1 led to more alcohol use at T2 in both younger and older adolescents (.12 ≤ β ≤ .17, p < .05). This significant relation was not found between T2 and T3. We did not find that parents adapt their levels of permissiveness in response to adolescent alcohol use.
Paths from parental problem drinking at T2 on alcohol-specific behavioral control at T3 were significant for the father regarding both the older and younger adolescents (respectively β = −.11 and β = −.14, p < .01), and for the mother regarding the younger adolescent (β = −.15, p < .01). Problem drinking of the father at T2 related substantially to alcohol use of the younger adolescent at T3 (β = .20, p < .001). Problem drinking of the mothers affected both the older (β = .18, p < .001) and the younger adolescents’ alcohol consumption (β = .15, p < .01). For the younger, but not for the older adolescents, more alcohol-specific behavioral control at T1 was related to lower levels of adolescent alcohol use at T2 (β = −.23, p < .001 for fathers, β = −.24, p < .001 for mothers). Adolescent alcohol use negatively affected alcohol-specific behavioral control of both parents (.10 ≤ β ≤ .12, p < .05)
General parenting: behavioral control and support
The standardized regression weights of parental problem drinking, support, behavioral control, and adolescent alcohol consumption showed a strong stability over time, with values between .42 and .85 (see Table 4).
Table 4Structural parameters estimates of general parenting practices, parental problem drinking (PD) and adolescent alcohol use (standardized beta weight)Behavioral controlSupportFathersMothersFathersMothersStability paths1. PD T1–PD T2.80.73.81.772. PD T2–PD T3.83.82.85.803. Parenting OA T1–Parenting OA T2 .61.64.76.734. Parenting OA T2–Parenting OA T3.70.70.76.725. Alcohol use OA T1–Alcohol use OA T2.54.53.53.536. Alcohol use OA T2–Alcohol OA T3.66.66.67.677. Parenting YA T1–Parenting YA T2.66.63.70.678. Parenting YA T2–Parenting YA T3 .63.73.69.709. Alcohol use YA T1–Alcohol use YA T2.43.42.43.4210. Alcohol use YA T2–Alcohol use YA T3.58.57.59.57Paths from PD11. PD T1–Parenting OA T2.03−.06.00.0712. PD T2–Parenting OA T3−.05−.04−.06−.0113. PD T1–Alcohol use OA T2.09.18***.10.18***14. PD T2–Alcohol use OA T3.03.00.03.0215. PD T1–Parenting YA T2.06.00.08.0316. PD T2–Parenting YA T3−.04−.12**−.03−.0617. PD T1–Alcohol use YA T2.00.00.00−.0118. PD T2–Alcohol use YA T3.20***.15**.20***.14**Cross-lagged paths19. Parenting OA T1–Alcohol use OA T2.04.03−.04−.0720. Parenting OA T2–Alcohol use OA T3−.11*−.18***−.02−.0221. Alcohol use OA T1–Parenting OA T2−.07−.05−.01−.0222. Alcohol use OA T2–Parenting OA T3−.12**−.14**−.06.0123. Parenting YA T1–Alcohol use YA T2−.15**−.14**−.10*−.15**24. Parenting YA T2–Alcohol use YA T3−.05−.08.07−.0125. Alcohol use YA T1–Parenting YA T2−.04−.09*−.11**−.08*26. Alcohol use YA T2–Parenting YA T3−.05−.07.02−.0227. Alcohol use OA T1–Alcohol use YA T2.10*.09.09.1028. Alcohol use OA T2–Alcohol use YA T3.10*.10*.11*.11*Note: PD = Parental Problem Drinking, OA = Older Adolescent, YA = Younger Adolescent. All stability paths are significant at p < .001. The numbered paths in the table correspond to the arrowed paths depicted in Fig. 1* p < .05, ** p < .01, *** p < .001
Regarding both the older and younger adolescents, paths of both maternal and paternal problem drinking with behavioral control were generally not significant. Only maternal problem drinking at T2 showed a significant association with behavioral control towards the youngest adolescent at T3 (β = −.12, p < .01). Problem drinking of the mother at T1 and T2 was significantly associated with alcohol use of the older adolescent at T2 and drinking of the younger adolescent at T3, respectively (β = .18, p < .001, β = .15, p < .01). Problem drinking of the father at T2 related substantially to alcohol use of the younger adolescent at T3 (β = .20, p < .001). Considering the cross-lagged paths, more parental behavioral control at T2 was associated with less alcohol consumption of the older adolescents at T3 (β = −.11, p < .05 for fathers, β = −.18, p < .001 for mothers), but not from T1 to T2. Younger adolescents also consumed less alcohol at T2 when their parents exerted more behavioral control at T1 (β = −.15, p < .01 for fathers, β = −.14, p < .01 for mothers). In addition, older adolescents’ drinking at T2 negatively affected parental behavioral control at T3 (β = −.12, p < .01 for fathers, β = −.14, p < .01 for mothers), indicating that when older adolescents drank more, parents exerted less general behavioral control. This result was not found for the younger adolescent.
No significant associations were found between parental problem drinking and the support parents provide to both the younger and older adolescent. Paternal problem drinking at T2 directly affected alcohol use of the younger adolescent at T3 (β = .20, p < .001). Mothers’ problem drinking at T1 and T2 affected alcohol use of the older adolescent at T2, and drinking of the younger adolescent at T3, respectively (β = .18, p < .001; β = .14, p < .01). More parental support at T1 was related to less alcohol use of the younger adolescents at T2 (β = −.10, p < .05 for fathers, β = −.15, p < .01 for mothers). These associations were not found between T2 and T3, nor for the older adolescents. In addition, more alcohol use of the older adolescents at T1 was associated with less parental support at T2 (β = −.11, p < .01 for father, β = −.08, p < .05 for mothers).
Additional analyses
We also tested whether older adolescents influenced their younger siblings in drinking behavior. Results showed that alcohol consumption of the older adolescents tended to directly affect alcohol use of the younger adolescent (.10 ≤ β ≤ .12, p < .05).
Discussion
The aim of the present study was to gain insight into the associations between parental problem drinking, parenting, and adolescent alcohol use in a sample of Dutch families. The first main finding shows that, except for alcohol-specific behavioral control, parental problem drinking does not structurally affect parenting over time. More alcohol-related problems did not result in less behavioral control, less general support, or higher permissiveness. Our results differ from those of other studies in which significant relations between parental problem drinking and parenting were found (Chassin et al. 1993; King and Chassin 2004; Rutherford et al. 1997). These differences could be due to methodological issues, as problem drinking or alcoholism in the latter studies were often diagnosed in conformity with the DSM-IV criteria, whereas we concentrated on a broader range of alcohol-related problems in a community sample. Thus, because of our assessment of problem drinking,2 instead of alcohol dependence or abuse, and because of our focus on a community sample instead of a clinical sample, the present study reflects the situation in the general population, and as such enhances the generalizability of the findings. Another explanation for the non-significant relation between parental problem drinking and general parenting comes from the buffering hypothesis, which states that “support protects persons from the potentially pathogenic influence of stressful events” (Cohen and Wills 1985, p. 310). Accordingly, children of one problem-drinking parent may be protected from the parent’s inadequate parenting by the support and adequate parenting of the other (non-problem drinking) parent. A buffering mechanism has been reported in the literature. For example, Van Aken and Asendorpf (1997) found that low support from one parent could be compensated by support from the other parent in affecting adolescent self-esteem. In addition, peer friendships, positive peer relations and family cohesion have each shown to be a protective factor against children’s externalizing problems in family conflict situations (Criss et al. 2002; Farrell et al. 1995). With respect to problem drinking, the possibility of enhancing resiliency in children and adolescents, by protecting against possible harmful influences from one parent by a strong relationship with the other parent, siblings, or peers, should be a topic of examination in future research. In addition, with regard to the persons in our sample, being part of a stable, nuclear family that consists of two biological parents with two or more children living together may be protective in itself.
Our findings do not imply that problem drinking has no effect on personal cognition or functioning, but suggest that parents are able to regulate their problem behavior with regard to their children and parenting practices. However, parental problem drinking may affect the way in which parents handle alcohol use within the family. Parents with more alcohol-related problems are not more permissive than parents who do not have these problems. Maintaining the set rules by means of alcohol-specific behavioral control, however, does appear to be a problem.
Our second main finding is that higher levels of behavioral control, support, rules, and alcohol-specific behavioral control account for less alcohol consumption in mainly the younger adolescents, which corresponds with the literature on this topic (e.g., Kerr and Stattin 2000; Van der Vorst et al. 2005, 2006). Alcohol-specific rule setting played an important role in drinking of both the younger and older adolescents. When parents were more permissive toward alcohol use, adolescents reported higher levels of drinking, which is in accordance with other studies on rule enforcement and adolescent alcohol use (Jackson et al. 1999; Van der Vorst et al. 2005; Van Zundert et al. 2006; Yu 2003). However, support and both general and alcohol-specific behavioral control were only associated with less alcohol use of the younger adolescent, up to the age of about 14 years. For older adolescents the effect of parenting disappeared, and parental problem drinking was found to directly affect adolescent alcohol use. In explaining this pattern, it might be that parenting exerts influence before and during the initiation phase of alcohol use, which in Dutch adolescents takes place around the age of 14 years (Poelen et al. 2005), but that parenting is no longer important once the habitual drinking pattern has been established (DeCourville 1995). Further, it has been suggested that genetic effects increase in importance over time during late adolescence, whereas environmental factors decrease in importance. Twin studies have indeed shown that shared environmental factors, such as parenting, play a profound role in the initiation of alcohol use, but that genetic factors are more important in frequency of alcohol use and problem drinking in young adulthood (see Hopfer et al. 2003; Pagan et al. 2006). Moreover, during adolescence, parental factors decrease in importance, whereas the influence of peers increases, making the latter a strong predictor for adolescent drinking (Fergusson et al. 1995). As such, shared environmental factors, such as parenting and parental modeling, affect alcohol consumption in young adolescence. Drinking in later adolescence may be related to other, non-shared factors, such as genes and peer drinking. Accordingly, future research should apply a longitudinal, behavioral genetic design, preferably examining the different stages of adolescent drinking (initiation, frequency of consumption, drinking to intoxication) in order to partial out the different effects of genetic and environmental factors.
Our third main finding considers the reciprocal associations between adolescent alcohol use and parental behaviors. More adolescent alcohol use made parents decrease their levels of general and alcohol-specific behavioral control over time. Levels of support were also negatively adjusted in response to adolescent alcohol use, but only in the younger adolescents. Our findings concur with recent studies that also reported bidirectional findings between parenting practices and adolescent substance use (Huver et al. 2006; Stice and Barrera 1995; Van der Vorst et al. 2006). However, parents did not adjust their levels of permissiveness in response to adolescent alcohol use. Perhaps rule-setting is more stable over time, and is not affected by fluctuations in adolescent alcohol use. The application of those rules, however, by means of exerting control, was influenced by the levels of alcohol that adolescents consumed. Since our study is one of the first to examine reciprocal effects between adolescent drinking and parental factors, more research on this topic is warranted.
Regarding differences between fathers and mothers, we found that maternal, but not paternal problem drinking was directly associated with alcohol use of the oldest adolescent. This is a remarkable finding, considering the fact that in most parenting studies with a focus on alcoholism or problem drinking, women are underrepresented (e.g., Chassin et al. 1993). Future research should specifically include mothers in studies regarding alcoholic or problem-drinking parents.
Limitations
Despite the advantages of our study, such as multi-informant data, longitudinal design and the testing of reciprocal associations in path analyses, some limitations should be addressed. First, we did not further examine relationships in subgroups (for example, sex differences) because of a lack of statistical power and, subsequently, the risk of making Type II errors. Nonetheless it should be stressed that in this type of longitudinal study with a full-family design, the sample size was substantial and the low attrition rates over the three waves were remarkable. Second, parental factors explained only a small part of the variance in adolescent drinking. However, finding small effects does not imply that parental factors are unimportant. The finding that parenting influences adolescent alcohol consumption can have large practical implications (see Abelson 1985). Third, parents may have under-reported their alcohol-related problems because of social desirability and adolescents may have under-reported their alcohol use because of the presence of their parents at home while filling in the questionnaires. In an attempt to anticipate these biases, and to ensure confidentiality, the questionnaires had to be completed individually and separately, without the possibility for family members to discuss the answers. In addition, studies have shown that self-reports concerning alcohol use are a reliable source of information (Engels et al. 2007). Fourth, although the sample was carefully selected, the results cannot be generalized to the whole Dutch population, because of the lack of for example, single-parent families and step-families. Fifth, in the Netherlands the legal age to drink beer and wine is 16, and the legal age to drink liquor is 18. This may make it difficult to compare previous research from the USA, where the legal age to drink is 21, with our results. Sixth, it might be that the initial measurements triggered follow-up discussions about alcohol use within families, which might have acted as an intervention. However, since our study is longitudinal and ongoing, we did not want to encourage any speculations or thoughts on the content of the questionnaires which might affect the following measurements, and as such we did not consult the families on these matters. Seventh, in our questionnaires, the definition of “glasses” of alcohol was left up to the respondents to interpret. This may have lead to a reporter bias in the exact amount of alcohol consumed because of different (non-standard) glass sizes. However, measurement of the precise amount of alcohol consumed is rather difficult to realize in our current study design. Experimental designs or diary studies will be able to more accurately measure and control the precise quantity of consumed alcohol. See Kerr et al. (2005) for an elaborated discussion on this topic.
Taking these limitations into account, this study is the first to disentangle the prospective relations between parental alcohol-related problems, parenting, and adolescent alcohol use in a community sample using multi-informant data. The results show that parental problem drinking does not substantially and systematically affect parenting, and that parenting influences adolescent alcohol use, but only up to the age of about 14 years. This implies that shared environment factors (e.g., parenting and modeling effects) influence the development of alcohol use in young adolescents. When adolescents grow older, and move out of the initiation phase, their drinking behavior may be more affected by other factors, such as genetic susceptibility and peer drinking.

parenting
problem drinking
adolescent
alcohol


Database of neurodegenerative disorders

A neurological disorder is a disorder caused by the deterioration of certain nerve cells called neurons. Changes in these cells cause them to function abnormally, eventually bringing about their death. In this paper we present a comprehensive database for neurodegenerative diseases, a first-of-its kind covering all known or suspected genes, proteins, pathways related to neurodegenerative diseases. This dynamically compiled database allows researchers to link neurological disorders to the candidate genes & proteins. It serves as a tool to navigate potential gene-protein-pathway relationships in the context of neurodegenerative diseases. The neurodegenerative disorder database covers more then 100 disease concepts including synonyms and research topics. The current version of the database provides links to 728 abstracts and over 203 unique genes/proteins with 137 drugs. Also it is integrated well with other related databases. The aim of this database is to provide the researcher with a quick overview of potential links between genes and proteins with related neurodegenerative diseases. Thus DND providing a user-friendly interface is designed as a source to enhance research on neurodegenerative disorders.
Background
Neurodegenerative disease leads to neurodegeneration and disabilities as a result of deterioration of neurons. [1] Progressive loss of motor, sensory neurons and the ability of the mind to refer
sensory information to an external object is affected in different kinds of neurological disorders. [2] Mutations in the genes identified for these disorders leads to accumulation of misfolded protein
resulting in protein aggregation and intracellular inclusions. [3] The knowledge gained from the genetic studies of many neurodegenerative disorders explains the disease mechanism that includes few pathways
leading to neuronal death of cells. [4] Amyloid precursor proteins (APP), SNCA, Parkin, UCHL1, NR4A2, DJ1, PINK1 and LRRK2 are the genetic causes of some of the primary neurodegenerative disorders like
Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis and prion diseases. [5] The treatment strategies for these disorders include therapies and drugs. Because of the
complexity involved in the neurodegenerative disease mechanism and treatment strategies, it is of interest to collate different Neurodegenerative Disorders in the form of a database. Hence we developed DND
(Database of Neurodegenerative Disorders), an on-line web based database that contains more than 100 neuro related disease concepts and provides with a covering of all related genes, proteins, pathways and drug information.
Methodology
Database model and content
Database of Neurodegenerative Disorders (DND) is developed as an open source software system using Mysql - 5.0.18 - Win32 [6] and PHP - 5.2.0.
[7] DND uses the relational data model. A schema diagram describing
the DND is shown in the [figure 1]. MySQL tables are shown as rectangles. Mandatory attributes are in bold, optional are in italics. The relationships between tables in the database are shown as connecting
lines. The public databases NCBI, SwissProt, Kegg and DrugBank are used for populating the database. The gene sequences related to various neurological disorders are obtained from NCBI. Both gene sequence and coding
sequence can be obtained in Fasta format. Also HGNC links for each entry is provided. All protein sequences are extracted from UniProt, and fields that mapped to PDB, Pfam, Interpro, Prints and Smart are parsed and
stored. Links from DND to these source databases are also provided for each entry. Fasta format of protein sequences too can be obtained. Pathway information for Neurodegenerative diseases as well as known neuro
related drug compounds in KEGG is searched and the results obtained are incorporated in to the database. The articles are chosen to represent a diverse selection of reports on major Neurodegenerative diseases and
corresponding Pubmed ID is provided for all the entries. Neurological Associations and Organizations interested in neuroscience-related fields are tabulated with links to their corresponding web pages.
Searching DND
There are three ways by which the user can query the database. The first one is the keyword search in home page that can be performed by giving keywords like Disease name, Gene name and Drug name to retrieve the
required data. The second one is the advanced search option for specific requirements with two main entry points namely gene/protein and drug/drug properties. Gene/protein include search options like Gene name, Gene id,
Omim, Hgnc, Pubmed, Swissprot id, Pdb, Interpro, Pfam, Smart and Prints. The second entry point Drug/drug properties include Drug name, Kegg id, DrugBank id, Pubchem, and IUPAC name and Molecular formula. Alternatively,
the users can also browse the database via the seven entry points namely browse by Diseases, Genes, Proteins, Drugs, Pathways, Related Articles and Organizations.
Utility
The current version of the DND is comprehensive with enormous data related to every aspect of neurological disorders, providing public access to sequence, genetics, structural, and bibliographic information. Structures
of drugs can be viewed using the browser plug in Chime [8] that allows chemical structures to rotate, reformat, and save in various file formats. A glossary describing the terms used in database is also provided to help
the end users. We believe that DND will assist the intended neurological researchers in understanding of fundamental molecular and genetic processes that control various neurodegenerative diseases.

neurodegenerative disorder
neurological disorders
web database


Ambulatory blood pressure monitoring and renal functions in children with a solitary kidney

The aim of this study is to investigate the blood pressure (BP) profile, microalbuminuria, renal functions, and relations with remaining normal kidney size in children with unilateral functioning solitary kidney (UFSK). Sixty-six children with UFSK were equally divided into three groups: unilateral renal agenesis (URA), unilateral atrophic kidney (UAK), and unilateral nephrectomy (UNP). Twenty-two age-, weight-, and height-matched healthy children were considered as a control group. The serum creatinine level and first-morning urine microalbumin and creatinine concentrations were determined by the standard methods. Also, the BP profile was determined by ambulatory blood pressure monitoring (ABPM). We found that the serum creatinine level was higher and creatinine clearance was lower in each patient groups compared to those of the control group (p < 0.05). Compared with the controls, each group of patients had mean office, 24-h, daytime, and night-time systolic and diastolic BP values similar to those of the controls (p > 0.05). An inverse correlation was found between the renal size standard deviation scores (SDS) of normal kidneys and 24-h systolic and diastolic BP load SDS in all of the patients (p < 0.05; r = −0.372, r = −0.295, respectively). The observed relationship between renal size SDS and 24-h mean arterial pressure (MAP), systolic and diastolic BP load SDS suggests that children with UFSK should be evaluated by using ABPM for the risk of hypertension.
Introduction
Arterial hypertension, proteinuria, and impaired renal functions are potential complications of unilateral functioning solitary kidney (UFSK), including unilateral renal agenesis (URA), unilateral atrophic kidney (UAK), and unilateral nephrectomy (UNP). The reduction of renal mass leads to compensatory hypertrophy of the remaining renal tissue in these patients [1–3]. Functionally, the remnant kidney will partly compensate for the lost function by increasing its workload, which is accompanied by increased glomerular blood flow and blood pressure (BP), so-called hyperfiltration [4]. It has been shown in animal models of unilateral renal ablation that the remaining kidney undergoes accelerated structural renal damage, generally in the form of glomerulosclerosis, with varying degrees of renal insufficiency, partly amenable to a variety of therapeutic interventions [5, 6]. A relatively small number of long-term follow-up studies in humans has documented that the surgical loss of renal functional mass in the presence of a normal remnant kidney rarely leads to renal insufficiency, although the incidence of mild to moderate proteinuria and hypertension has increased [7–10].
Kasiske et al. [11] reported that UNP does not cause progressive renal dysfunction, but it may be associated with a small increase in BP. On the other hand, Wikstad et al. [8] showed that adults born with URA or UNP in childhood did not have a marked increase in arterial BP or renal insufficiency. Some reports, however, indicate that patients with URA or with an UFSK left after nephrectomy may have proteinuria and focal glomerular sclerosis [5, 12]. Janda et al. [13] recorded marginal diastolic hypertension in one third of their subjects in a study of 40 children and/or adolescents (23 URA, 17 UNP). Although some investigators have reported an increase in the prevalence of hypertension and/or proteinuria, others have failed to document these abnormalities [11]. The discrepancy among the results of these studies may be caused due to a difference in the amount of renal mass removed, the age at the time of renal mass reduction, or unsuspected damage in the remaining kidney.
In the past few years, ABPM has become an accepted method for investigating the BP profile in children with renal disorders [14–16]. Over the years, ambulatory blood pressure monitoring (ABPM) has been increasingly used to investigate hypertension in different pediatric populations [17–20]. A careful review of the literature shows that there are few reports on ABPM, microalbuminuria, and renal functions in children with UFSK [21]. Furthermore, the utility of BP in children with UFSK is not well established. Therefore, the aim of this study was to investigate the BP profile by ABPM, microalbuminuria, renal functions, and relations with remaining normal kidney size in children with UFSK.
Materials and methods
Sixty-six children with a mean age of 8.32 ± 4.23 and range 0.5 and 18 years with UFSK were equally divided into three groups: URA group (n = 22), UAK group (n = 22), and UNP group (n = 22). Twenty-two Age-, weight-, and height-matched healthy children were considered as a control group (Table 1). Renal ultrasound was normal in all controls. Informed consent was obtained from the children and their parents prior to the testing, and the study was approved by the local ethics committee of the School of Medicine, Çukurova University, Adana, Turkey.
Table 1Demographic data, anthropometric data, and renal functional parameters of the patients and control groupsParametersURA (n = 22)UAK (n = 22)UNP (n = 22)Controls (n = 22)Gender (M/F)11/1112/1010/1210/12Age (years)8.63 ± 4.508.03 ± 4.308.31 ± 4.048.78 ± 3.49Weight (kg)28.44 ± 18.0727.01 ± 17.1426.57 ± 10.4829.19 ± 10.92Height (cm)124.55 ± 27.62126.05 ± 25.22123.96 ± 21.21128.36 ± 26.44BMI (kg/m2)16.60 ± 2.9517.96 ± 3.1416.77 ± 2.0516.96 ± 1.91UMA (mg/L)5.46 ± 3.914.80 ± 2.034.56 ± 8.115.18 ± 4.09UMA/UCr (mg/mg)0.23 ± 0.720.17 ± 0.300.07 ± 0.130.06 ± 0.06GFR (ml/dk/1.73 m2)1121.95 ± 34.19123.66 ± 43.67108.57 ± 26.71155.85 ± 41.27Serum Cr (mg/dl)20.59 ± 0.130.60 ± 0.150.65 ± 0.100.47 ± 0.13Renal size (mm)100.32 ± 18.4291.32 ± 17.5697.23 ± 17.4992.36 ± 16.00Values are expressed as mean±SD, M: male, F: female, BMI: body mass index, UMA: urine microalbumin, UCr: urine creatinine, GFR: glomerular filtration rate1p < 0.05 URA vs. controls, UAK vs. controls, and UNP vs. controls2p < 0.05 URA vs. controls, UAK vs. controls, and UNP vs. controls
Both renal agenesis and atrophic kidney were established through renal ultrasound and scintigraphy. A non-functioning kidney is caused due to unilateral vesicoureteral reflux (in seven cases), ureteropelvic junction obstruction (in 10 cases), and multicystic dysplastic kidney (in five cases) in the UAK group. In contrast, indications for nephrectomy contained unilateral Wilms’ tumor (in six cases), a non-functioning kidney is caused due to unilateral vesicoureteral reflux (in 10 cases) and ureteropelvic junction stenosis (in two cases), multicystic dysplastic kidney (in two cases), and nephrolithiasis (in two cases) in the UNP group. Patients with Wilms’ tumor have favorable type and they did not receive chemotherapy. The mean time due to nephrectomy was 4.06 ± 3.87 years (range 0.5–17 years) in this group.
All patients had a normal renal function defined as a glomerular filtration rate (GFR) of over 90 ml/min 1.73 m2 (as determined by Schwartz et al.’s formula [22]) and normal urinary sediment. The remaining kidney has findings of normal ultrasound and normal scintigraphy in all patient groups. Only children without any anatomical abnormalities of the remaining kidney and with normal GFR were included in the study. No patient was treated with antihypertensive drugs or drugs interfering with BP. Neither the study patients nor children in the control groups had a positive (first-degree-relative) family history of hypertension. Patients with a renal scar in their solitary kidney or who have other urinary tract anomaly were withdrawn from the study. In all of the study group participants, urinalysis, serum uric acid, creatinine, cholesterol, protein and albumin levels, and first-morning urine microalbumin and creatinine concentrations were determined by the standard methods.
In all subjects, ABPM was performed over 24 h using the SpaceLabs 90217 oscillometric device (Redmond, Washington). The appropriately sized cuff, chosen based on arm width, was placed on the non-dominant arm. The BP was automatically recorded in every 15 min during the day and every 30 min at night. Only ABPM profiles with at least 30 recordings, including at least eight readings between midnight and 06.00 h, were accepted. Daytime was defined as 08.00–20.00 h and night-time as 00.00–06.00 h, according to the study of Soergel et al. [23]. For each subject, the BP values corresponding to the 95th percentile, according to sex and height, was determined [23, 24]. Hypertension was defined as a systolic BP and/or diastolic BP>95th percentile. The percentage of systolic BP and diastolic BP readings above this value constituted a measurement of 24-h systolic and diastolic BP load, respectively. The dipping status was calculated by subtracting the mean night-time BP from the mean daytime BP, and then dividing this value by the mean daytime BP. Dipping was defined as a ≥10% drop in mean systolic BP or diastolic BP between daytime and night-time. Renal size and BP standard deviation scores (SDS) was calculated according the left main stem (LMS) method [24, 25].
The renal size of the normal kidney was evaluated by performing abdominal ultrasound and measuring the lower-to-upper-pole length in the supine longitudinal view by the same observer. Renal size SDS values were correlated to the BP SDS values for each patient. Renal size percentiles were determined and evaluated according to standard length-against-height-nomogram defined by Dinkel et al. [26]. Compensatory hypertrophy was accepted when the measured renal size was found to be greater than the 95th percentile, and its effect on the BP parameters was evaluated.
All data were stored and analyzed using the SPSS statistical package (SPSS Inc., Chicago, Illinois). The normality Kolmogorov-Smirnov test was performed to determine whether continuous variables were normally distributed or not. Differences between groups were analyzed using the non-parametric Kruskal-Wallis test and the Mann-Whitney U test. All values are expressed as mean±SD. The prevalence of hypertension was determined using Fisher’s exact test. The relationships between variables were analyzed with the non-parametric Spearman’s correlation test in each group. A value of p < 0.05 was considered to be statistically significant.
Results
The demographic data and renal function parameters of all of the study groups and controls are shown in Table 1. Age, height, weight, BMI, urine microalbumin, urine microalbumin to urine creatinine ratio, and renal size values are not different between each patient group and the controls (p > 0.05). The mean serum creatinine level was higher in all patient groups compared to that of the control group (p = 0.006 between URA and control, p = 0.004 between UAK and control, p < 0.001 between UNP and control) and the GFR was lower in all patient groups compared to that of the control group (p = 0.005 between URA and control, p = 0.016 between UAK and control, p < 0.001 between UNP and control).
Mean heart rates (24 h, daytime, night-time), mean systolic and diastolic BP parameters (office BP, 24 h, daytime, night-time), mean systolic and diastolic BP dipping, and 24-h systolic and diastolic BP loads are all given in Table 2. When compared to the controls, each group with UFSK had mean office, 24-h, daytime, and night-time systolic and diastolic BP, and heart rate values similar to those of the controls (p > 0.05). Only the mean 24-h diastolic BP was higher in the URA group than that of the control group (p = 0.039). In addition, the diastolic load was higher in the UAK group than that of the control group (p = 0.012). There was no significant difference in the mean office, 24-h, daytime, and night-time systolic and diastolic BP, and heart rate between all of the patient groups (p > 0.05). The mean office systolic and diastolic BP, respectively, were higher than the mean daytime systolic and diastolic BP in all groups. The prevalence of hypertension in the URA group was 23% (5/22), in the UAK group was 23% (5/22), in the UNP group was 32% (7/22) [in all patients, it was 26% (17/66)], and in the controls it was 5% (1/22). The prevalence of hypertension in patients was higher compared to that in the controls (p = 0.035). The prevalence of non-dipping phenomenon in the URA group was 23% (5/22), in the UAK group was 36% (8/22), in the UNP group was 32% (7/22) [in all patients, it was 30% (20/66)], in the controls it was 9% (2/22). There was a statistically significant difference between patients and the controls (p = 0.049).
Table 2Average pulse rate and blood pressure (BP) of the patients and the control groupsParametersURA (n = 22)UAK (n = 22)UNP (n = 22)Controls (n = 22)Av. 24-h HR (beats/min)89.36 ± 15.7994.64 ± 17.7488.18 ± 12.9590.82 ± 14.79Av. daytime HR (beats/min)92.64 ± 15.5197.14 ± 17.2793.09 ± 12.9697.41 ± 15.55Av. night-time HR (beats/min)80.23 ± 15.9986.46 ± 18.0177.86 ± 12.4382.50 ± 16.65Av. office SBP (mmHg)113.96 ± 16.00116.96 ± 11.24114.55 ± 14.83114.68 ± 12.66Av. office DBP (mmHg)74.73 ± 10.4871.50 ± 11.0671.05 ± 13.0072.00 ± 9.67Av. 24-h MAP (mmHg)80.00 ± 7.9278.82 ± 7.1678.41 ± 7.0678.27 ± 3.89Av. 24-h SBP (mmHg)106.64 ± 10.87105.59 ± 9.31104.22 ± 8.04105.05 ± 6.90Av. 24-h DBP (mmHg)165.91 ± 6.8264.55 ± 6.5964.09 ± 6.8763.41 ± 3.63Av. daytime MAP (mmHg)81.14 ± 9.1480.00 ± 8.0479.68 ± 7.3380.68 ± 4.86Av. daytime SBP (mmHg)109.59 ± 11.75107.18 ± 10.01106.55 ± 7.95108.14 ± 6.83Av. daytime DBP (mmHg)68.36 ± 8.3666.46 ± 7.1766.18 ± 7.2867.41 ± 4.36Av. night-time MAP (mmHg)72.86 ± 6.5571.86 ± 7.7072.09 ± 6.7673.64 ± 4.99Av. night-time SBP (mmHg)99.82 ± 9.3699.32 ± 9.9999.00 ± 7.7898.55 ± 6.60Av. night-time DBP (mmHg)59.00 ± 5.4658.23 ± 7.1858.09 ± 6.7357.59 ± 3.75Av. SBP dipping (%)8.26 ± 4.297.31 ± 4.686.58 ± 3.428.83 ± 3.74Av. DBP dipping (%)13.12 ± 6.5412.24 ± 7.6412.03 ± 7.1914.87 ± 6.32Av. 24-h SBP load (%)19.71 ± 25.7526.31 ± 25.1421.88 ± 20.0814.45 ± 13.81Av. 24-h DBP load (%)221.22 ± 17.1233.0.2 ± 28.9926.24 ± 24.5613.54 ± 13.75Values are expressed as mean±SD, Av.: average, HR: heart rate, MAP: mean arterial pressure, SBP: systolic blood pressure, DBP: diastolic blood pressure1p = 0.039 between URA and control groups2p = 0.012 between UAK and control groups
Compensatory hypertrophy (renal size percentile >95%) was seen in 59% (39/66) of patients. Age, height, weight, renal size, and GFR were statistically significantly higher in patients with compensatory hypertrophy than in patients without compensatory hypertrophy. In contrast, the mean heart rates were lower in patients with >95th percentile of renal size (Table 3).
Table 3Some characteristics of the patients with unilateral functioning solitary kidney (USFK) according the percentile of renal sizeParametersRenal size <95th percentile (n = 27)Renal size >95th percentile (n = 39)pAge (year)5.91 ± 3.169.99 ± 4.09<0.001Height (cm)113.19 ± 21.24132.92 ± 23.47<0.001Weight (kg)19.80 ± 9.8932.56 ± 16.41<0.001Renal size (mm)80.37 ± 11.13107.31 ± 12.71<0.001GFR (ml/dk/1.73 m2)107.82 ± 37.31125.15 ± 33.09<0.05Av. 24-h HR (beat/min)99.19 ± 15.4984.87 ± 12.96<0.001Av. daytime HR (beat/min)102.00 ± 13.8188.95 ± 13.99<0.001Av. night-time HR (beat/min)88.59 ± 17.0576.62 ± 13.02<0.05Values are expressed as mean±SD, Av.: average, GFR: glomerular filtration rate, HR: heart rate
Renal size SDS was correlated with some BP SDS parameters in the patients and the controls. Statistically significant correlations are shown in Table 4. There was no correlation between the renal size SDS and BP parameters, but an inverse correlation was found between the renal size SDS and 24-h MAP SDS, 24-h systolic and diastolic BP load SDS in all of the patients, but these correlations were not found in the controls.
Table 4Correlation between renal size standard deviation scores (SDS) and some parameters SDS in patients with UFSK and the controlsGroupsParametersrpPatients (n = 66)Av. 24-h MAP SDS−0.2840.021Av. 24-h SBP SDS−0.1750.159Av. 24-h DBP SDS−0.0340.788Av. 24-h SBP load SDS−0.3720.002Av. 24-h DBP load SDS−0.2950.016Controls (n = 22)Av. 24-h MAP SDS−0.0110.962Av. 24-h SBP SDS−0.0670.767Av. 24-h DBP SDS−0.3120.158Av. 24-h SBP load SDS−0.1480.510Av. 24-h DBP load SDS−0.0190.934SDS: standard deviation scores, Av.: average, MAP: mean arterial pressure, SBP: systolic blood pressure, DBP: diastolic blood pressure
Discussion
The UFSK is usually considerably enlarged, this constituting a compensatory diffuse but not partial, lobar enlargement, usually with a larger than normal pelvis [27]. The reduction of renal mass in the rat is associated with hyperfiltration and hypertrophy of the remaining nephrons, ultimately resulting in proteinuria, renal failure, and hypertension. Nagata et al. [28] reported that unilateral nephrectomy in young rats leads to a higher incidence of glomerular sclerosis than is observed in adult rats. A study conducted by Regazzoni et al. [29] in a follow-up of UNP in children has reported a decreased renal reserve capacity, but detected no evidence of hypertension or proteinuria.
In our study, subjects were screened with urine microalbumin, urine and serum creatinine, GFR, and urine microalbumin-to-creatinine ratios in first-morning urine for the detection of proteinuria and renal functions. No statistically significant difference was found between the urine microalbumin and microalbumin-to-creatinine ratios in the first-morning urine in all of the patient and control groups. However, the GFR was lower and the serum creatinine level was higher in the subgroups of patients than those of the control group. These results show that a mild renal dysfunction may be detected in patients with UFSK.
Although some investigators have reported an increased prevalence of hypertension, others have failed to document these abnormalities in children with UFSK [11]. Janda et al. [13] recorded marginal diastolic hypertension in one third of their subjects in a study of 40 children and/or adolescents (23 URA, 17 UNP). As we mentioned before, there are few reports which have utilized 24-h ABPM in UFSK children. Mei-Zahav et al. [21] showed that the presence of an UFSK, from whatever cause, leads to an increase in BP. This is particularly manifested in systolic BP and even more so in daytime systolic BP. Kasiske et al. [11] reported that UNP does not cause progressive renal dysfunction, but may be associated with a small increase in BP. On the other hand, Wikstad et al. [8] concluded that adults born with URA or UNP in childhood did not have a marked increase in arterial BP or renal insufficiency. We measured the ABPM in three groups of patient with UFSK and found that they have the same characteristics as in healthy controls. However, in our study, the mean office, 24-h systolic, daytime, and night-time systolic and diastolic BP, mean 24-h, daytime, and night-time heart rate, 24-h systolic BP load, systolic and diastolic BP dipping in all groups of patients were similar to those of the controls. In contrast, mean 24-h diastolic BP was higher in the URA group than those of the control group, but this finding showed no difference between the other patient groups and controls. The 24-h diastolic BP load was increased in the UAK group. In addition, an increased prevalence of hypertension was found in all of the patients groups. According to these results, we suggest that patients with a single kidney have potential risk factors for hypertension in the early childhood period.
Compensatory hypertrophy of the remaining kidney may be seen in patients with UFSK. In adult animals, a compensatory increase in size following UNP is, for the most part, attributable to hypertrophy (an increase in cell size), which is predominantly of proximal tubular origin. In young animals, renal growth is achieved primarily by cell multiplication, that is, hyperplasia [3]. In the present study, an inverse correlation was found between renal size SDS and 24-h MAP SDS, 24-h systolic and diastolic BP load SDS in all of the patients, but was not seen in the controls. Therefore, we think that there is an increased risk of hypertension in children without compensatory hypertrophy.
To this end, we conclude that subtle alterations of renal function might be already present in children with UFSK, while no obvious pathological findings were made for BP or urinary protein excretion in the early period. However, an increased BP load was seen in these patients (particularly in patients without compensatory hypertrophy). Nevertheless, this might not exclude an increased long-term risk for the development of hypertension or impaired renal function in this patient group. Because patients in the present study have a small sample and a shorter follow-up period, further prospective studies with a larger sample size and of longer term are suggested to find the relationship between these parameters.

blood pressure
renal function
solitary kidney


Silencing α-Synuclein Gene Expression Enhances Tyrosine Hydroxylase Activity in MN9D Cells

α-Synuclein has been implicated in the pathogenesis of Parkinson’s disease (PD). Previous studies have shown that α-synuclein is involved in the regulation of dopamine (DA) metabolism, possibly by down-regulating the expression of tyrosine hydroxylase (TH), the rate-limiting enzyme in DA biosynthesis. In this study, we constructed α-synuclein stably silenced MN9D/α-SYN− cells by vector mediated RNA interference and examined its effects on DA metabolism. We found that there were no significant differences in TH protein and mRNA levels between MN9D, MN9D/α-SYN− and MN9D/CON cells, suggesting that silencing α-synuclein expression does not affect TH gene expression. However, significant increases in phosphorylated TH, cytosolic 3, 4-dihydroxyphenylalanine (l-DOPA) and DA levels were observed in MN9D/α-SYN− cells. Our data show that TH activity and DA biosynthesis were enhanced by down-regulation of α-synuclein, suggesting that α-synuclein may act as a negative regulator of cytosolic DA. With respect to PD pathology, a loss of functional α-synuclein may result in increased DA levels in neurons that may lead to cell injury or even death.
Introduction
α-Synuclein is a 140-amino acid protein, which has been implicated in the pathogenesis of Parkinson’s disease (PD) [20, 30]. Previous studies have shown that α-synuclein mutations (A53T, A30P, E46K) are associated with some autosomal-dominant PD [14, 15, 27] and that aggregated α-synuclein is the major component of Lewy bodies and Lewy neurites, pathological hallmarks of PD [2, 21, 34]. Although the normal function of α-synuclein and its role in the pathogenesis of PD remain unclear, several hypotheses have been proposed based on its physical properties or interacting partners [17–19, 25, 28, 31, 36]. One theory suggested that α-synuclein might be linked to PD via the regulation of dopamine (DA) homeostasis [1, 18, 25, 31, 41]. Several studies have shown that α-synuclein may be involved in regulating the biosynthesis, vesicular storage and release, as well as reuptake of DA [3, 9, 26, 32, 33, 38, 40, 42]. However, some of these findings remain controversial. It has also been reported that α-synuclein may down-regulate the gene expression of tyrosine hydroxylase (TH), the rate-limiting enzyme involved in DA biosynthesis [3, 42]. Others report that α-synuclein regulates DA biosynthesis by reducing TH activity [26]. Another study showed that the tyrosine-mediated enhancement of phosphorylated TH in nigrostriatal DA neurons was inhibited in α-synuclein knockout mice, suggesting that α-synuclein may either enhance TH phosphorylation or hinder TH inactivation during accelerated neuronal activity [9]. Although it has been established that α-synuclein plays an important role in enhancing TH activity, conflicting reports indicate that further studies are required to examine the role of α-synuclein in regulating DA metabolism.
RNA interference (RNAi) is a new strategy for silencing gene expression that uses short double-stranded (ds) RNA to mediate the degradation of sequence specific mRNA [10–12]. It has been reported that introducing small interfering RNAs (siRNAs) or short hairpin RNA (shRNA) into mammalian cells could specifically silence the expression of target genes [7, 23]. The high specificity, efficiency and convenience of this technique means that it is a powerful tool for studying gene function.
In the present study, we silenced the expression of α-synuclein using vector mediated RNAi in dopaminergic MN9D cells and examined the effect of down-regulation of α-synuclein on TH and DA biosynthesis.
Materials and Methods
Construction of shRNA Expression Plasmids
Short hairpin RNA were designed to target specific regions of mouse α-synuclein mRNA (GenBank accession no. AF179273). We identified two 19-nucleotide stretches within the coding region of the α-synuclein gene that were 40–50% GC rich, located within one exon and unique within the mouse genome. A nine-nucleotide loop structure was designed to form the hairpin. We designed three sets of oligonucleotides as instructed by the BLOCK-iT™ Inducible H1 RNAi Entry Vector Kit (Invitrogen, Carlsbad, CA, USA): SH1/α-SYN: 5′-CACCGAGCAAGTGACAAATTTGTTCAAGAGACAACATTTGTCACTTGCTC-3′ (forward), 5′-AAAAGAGCAAGTGACAAATGTTGTCTCTTGAACAACATTTGTCACTTG CTC-3′ (reverse); SH2/α-SYN: 5′-CACCAGGCTACCAAGACTATGAGTTCAAGAG ACTCATAGTCTTGGTAGCC-3′ (forward), 5′-AAAAGGCTACCAAGACTATGA GTCTCTTGAACTCATAGTCTTGGTAGCCT-3′ (reverse); and SH/CON: 5′-CACCGGATCGCCAGAACAAGTATTTCAAGAGAATACTTGTTCTGGCGATCC-3′ (forward), 5′-AAAAGGATCGCCAGAACAAGTATTCTCTTGAAATACTTGTTCTGG CGATCC-3′ (reverse). Single-stranded (ss) DNA oligos (BioSia, Co., Ltd, Shanghai, China) were synthesized. Equal amounts of each pair of ss oligos were annealed to generate ds oligos. The ds oligos were inserted into the pENTRTM/H1/TO vector (Invitrogen) using T4 DNA ligase and transformed into One Shot® TOP10 chemically competent Escherichiacoli (Invitrogen). The transformants were verified by PCR and sequencing. The positive plasmids were named pSH1/α-SYN, pSH2/α-SYN and pSH/CON, respectively.
Cell Culture
The MN9D dopaminergic cell line was generated by fusion of rostral mesencephalic neurons from embryonic C57BL/6J (embryonic day 14) mice with N18TG2 neuroblastoma cells [8] (gift from Dr Bastian Hengerer, Novartis AG). Cells were cultured in DMEM/F12 media supplemented with 10% neonatal calf serum (GIBCO BRL, Grand Island, NY, USA; pH 7.2) and incubated at 37°C in an atmosphere of 5% CO2. MN9D cells endogenously express α-synuclein and TH, and produce measurable levels of DA.
Transfection and Selection
MN9D cells were transfected with pSH1/α-SYN, pSH2/α-SYN, pSH/CON or pcDNA3.1 containing human wild-type (WT) α-synuclein cDNA (constructed in our laboratory). For stable transfection of pSH1/α-SYN, pSH2/α-SYN and pSH/CON, cells were seeded in 6-well tissue culture plates pre-coated with poly-l-lysine (Sigma, St Louis, MO, USA) at 80–85% confluency. Transfections were performed using Lipofectamine™ 2000 (Invitrogen) according to the manufacturer’s instructions. Four to six hours after transfection, the media was replaced with fresh growth media. The following day, cells were trypsinized and replated into a larger sized tissue culture format. Stably transfected MN9D cells were selected using 400 μg/ml Zeocin™ (Invitrogen) for 12 days. The Zeocin™ resistant clones were then selected and expanded. Cells were maintained with media containing 50 μg/ml Zeocin™. Seventy-two hours post-transfection, cells transfected with pcDNA3.1 containing human WT α-synuclein cDNA were collected and named MN9D/α-SYN+.
RNA Isolation, RT-PCR and Real-time RT-PCR
Total RNA was extracted from pSH1/α-SYN, pSH2/α-SYN and pSH/CON stably transfected MN9D cells and MN9D/α-SYN+ cells using Trizol reagent. First-strand cDNA was synthesized from 5 μg of total RNA, using the SuperScript™ First-strand synthesis System for RT-PCR (Invitrogen). The reaction mixture for PCR consisted of 1 μl of cDNA template, 17 μl of sterile ddH2O and 10 pmol (1 μl) of each specific primer. The following primers were used: α-synuclein: 5′-ATAAGAATGCGGCCGCATGGATGTATTCATGAAAG-3′ (forward) and 5′-CCGCTCGAGGCTTCAGGTTCGTAGTCTTGA-3′ (reverse). As an internal control, β-actin cDNA was co-amplified using the following primer sequences: 5′-CCCATCTACGAGGGCTACGC-3′ (forward) and 5′-AGGAAGGAGGGCTGGAACA-3′ (reverse). Each PCR was started by predenaturation at 96°C for 3 min. Each PCR cycle consisted of 94°C for 30 s, 50°C for 55 s and 72°C for 55 s, followed by a final 10 min extension at 72°C. PCR amplification was carried out for 35 cycles for α-synuclein and 22 cycles for β-actin using a Perkin–Elmer DNA thermal cycler 480 (Applied Biosystems, Foster City, CA, USA).
The reaction mixture (20 μl) for real-time PCR consisted of 10 μl SYBR GREEN PCR Master Mix (Applied Biosystems), 0.5 μl of cDNA template, 0.4 μl of each specific primer (10 μM) and 9.7 μl of sterile ddH2O. The following primers were used: α-synuclein: 5′-TGACGGGTGTGACAGCAGTAG-3′ (forward) and 5′-CAGTGGCTGCTGCAATGC-3′ (reverse); TH: 5′-CAGCCCTACCAAGATCAAAC-3′ (forward) and 5′-TACGGGTC AAACTTCACAGA-3′ (reverse); and β-actin: 5′-ACCACCATGTACCCAGGCATT-3′ (forward) and 5′-CCACACAGAGTACTTGCGCTCA-3′ (reverse). Quantitative PCR was performed in duplicate or triplicate using a 7300 real-time PCR thermal cycler (Applied Biosystems). Each reaction was started by 50°C for 2 min, 95°C for 10 min and 96°C for 3 min, followed by 40 cycles of 94°C for 15 s, 59°C for 20 s and 72°C for 30 s.
Immunofluorescence Staining
Cells were washed with 0.01 M PBS, fixed in 4% paraformaldehyde (30 min), permealized with 0.5% Triton-X (20 min) and blocked in 5% normal goat serum (30 min). Samples were incubated with a specific monoclonal antibody against α-synuclein (2E3; 1:1000) (gift from Dr Shun Yu, Xuanwu Hospital, Capital Medical University, Beijing, China) overnight at 4°C. Cells were washed with 0.01 M PBS and incubated with goat anti-mouse IgG conjugated to cyanine 3 (Cy3TM; 1:400, Sigma) at 37°C for 30 min. After a final wash, cells were examined by fluorescence microscopy.
Cell Viability Assay
Cell viability was assessed using a MTT Kit (Promega, WI, USA). Briefly, the transfected MN9D cells were plated into 96-well plates (1.0 × 104 cells per well) and cultured for 24 h prior to the assay. Sixteen wells were measured for each stably transfected MN9D cell line.
Western Blot Analysis
Lysates of MN9D cells were prepared as previously described [39]. Protein concentrations were determined using BCA relative to BSA protein standards according to the manufacturer (Pierce, Rockford, IL, USA). Whole cell protein extracts were resolved by SDS-PAGE and electrophoretically transferred to nitrocellulose membrane. Prestained protein standards were used to determine the relative molecular mass of proteins. The membranes were blocked in 5% nonfat dry milk in Tris-buffered saline, then incubated with the TH specific monoclonal antibody (1:5000, Sigma) or α-synuclein specific monoclonal antibody (2E3; 1:1000) at room temperature for 3 h, followed by a 1-h incubation with a peroxidase-conjugated secondary antibody (1:5000, ZhongShan, Beijing, China) at room temperature. The blots were washed and immunodetection was carried out by chemiluminescence using SuperSignal (Pierce). Blots were then stripped and re-probed with an antibody against β-actin (1:500, Zhongshan). Blots were scanned using Kodak Image Station (440 CF; Kodak, Rochester, MN, USA) and the optical densities of the bands relative to β-actin within each lane were obtained.
HPLC for Assaying DA and 3, 4-Dihydroxyphenylalanine (l-DOPA) Levels
MN9D cells were collected and re-suspended in HPLC buffer consisting of 0.1 N perchloric acid, 0.3 mM EDTA and 0.1% l-lysine. Cells were freeze-thawed three times, then centrifugated at 15,000 g for 15 min at 4°C to remove particulates. The supernatants were collected and stored at −80°C until analysis. Briefly, the supernatant samples (20–50 μl) were injected onto a Phase II Column (ODS, 3.2 × 100 mm cartridge, Φ3.2 μm, Bioanalytical Systems, Inc, USA). The mobile phase consisted of 0.05 M CH3COONa, 0.05 M citric acid, 1 mM sodium octyl sulfate, 5 mM TEA, 0.075 mM Na2EDTA and 10% methanol (v/v), pH 2.7. The mobile phase was pumped through the system at 0.4 ml/min using a PM-80 pump (Bioanalytical Systems, Inc). Compounds were detected and quantified with an LC-4C detector (Bioanalytical Systems, Inc). The limit of detection for DA was 2 nmol/20 μl. Peaks were identified by retention times set to known standards.
Tyrosine hydroxylase activity was assessed by measuring the accumulation of l-DOPA within cells treated with the aromatic l-amino acid decarboxylase (AADC) inhibitor n-hydroxybenzylhydrazine dihydrochloride (NSD-1015) and measured by HPLC as described above. Briefly, MN9D cells were cultured in 12-well tissue culture plates, washed twice in artificial CSF (ACSF containing 147 mM NaCl, 2.7 mM KCl, 1.2 mM CaCl2 and 1.0 mM MgCl2) and equilibrated for 20 min at 37°C before addition of 200 μM NSD-1015 in ACSF for 30 min. ACSF samples were collected and assayed by HPLC as described above.
Statistical Analysis
ANOVA followed by Bonferroni post hoc multiple comparisons were used to examine the significance between differences among the experimental groups. A value of p < 0.05 was considered to be statistically significant. All data are expressed as mean ± SE.
Results
Construction of the shRNA Expression Plasmids
After synthesis, each pair of the ss oligos was annealed to generate ds oligos. The annealing efficiency was analyzed by 4% agarose gel electrophoresis. Each ds oligo annealing reactant showed a clearly detectable molecular weight band around 50 bp, as expected for the length of the designed ds oligos. The 50 bp bands were brighter than the corresponding lower molecular weight band, which represented the unannealed ss oligos in the annealing reactant. These data indicate that each pair of ss oligos annealed effectively to form ds oligos. The ds oligos were ligated with linearized pENTR™/H1/TO vectors and the positive clones were expanded and verified by PCR. Bands near 350 bp were found in all PCR reactants, indicating that the ds oligos were ligated into the pENTR/H1/TO vectors. Further analysis by sequencing confirmed that the orientation and sequence of the inserted ds oligos were correct.
α-Synuclein Expression in MN9D Transfected Cells
The gene expression of α-synuclein in the transfected MN9D cells was assayed by RT-PCR, real-time RT-PCR, immunofluorescence and Western blot. RT-PCR analysis revealed that α-synuclein mRNA levels were decreased in pSH2/α-SYN-transfected MN9D cells compared with MN9D and pSH/CON-transfected MN9D cells (Fig. 1a). However, α-synuclein mRNA levels in pSH1/α-SYN-transfected cells were not significantly different to levels observed in control MN9D and pSH/CON-transfected MN9D cells (Fig. 1a). These data suggest that α-synuclein expression may be inhibited by transfection with pSH2/α-SYN. In addition, real-time RT-PCR revealed that α-synuclein mRNA levels in pSH2/α-SYN-transfected cells (9.8 ± 2.4%) were significantly lower than in MN9D and MN9D/CON-transfected (81.6 ± 4.9%) cells (Fig. 1b). The immunofluorescence data also indicated that α-synuclein gene expression was down-regulated in pSH2/α-SYN-transfected cells (Fig. 1c). Weak immunofluorescence staining was observed in pSH2/α-SYN-transfected cells compared to strong α-synuclein staining in control MN9D, pSH/CON-transfected and pSH1/α-SYN-transfected MN9D cells. Furthermore, western blot analysis revealed that α-synuclein protein levels were significantly lower in pSH2/α-SYN-transfected cells (0.150 ± 0.004) than in MN9D cells (0.401 ± 0.038) (Fig. 1d, e). Thus, α-synuclein expression was inhibited after transfection of MN9D cells with pSH2/α-SYN. These data suggest that SH2/α-SYN was an effective siRNA sequence and that α-synuclein expression was stably silenced in pSH2/α-SYN-transfected MN9D cells. The pSH2/α-SYN and pSH/CON stably transfected cells were then named MN9D/α-SYN- and MN9D/CON, respectively.
Fig. 1Inhibition of α-synuclein expression in pSH2/α-SYN-transfected cells. (a) α-Synuclein mRNA levels were measured by RT-PCR. Lane 1 pSH1/α-SYN-transfected MN9D cells; Lane 2 pSH2/α-SYN-transfected MN9D cells; Lane 3 pSH/CON-transfected MN9D cells; Lane 4 MN9D cells; Lane 5 100 bp DNA ladder. (b) Statistical analysis of α-synuclein gene expression by real-time RT-PCR. α-Synuclein mRNA levels were decreased by approximately 90% in pSH2/α-SYN-transfected cells compared with normal MN9D cells [n = 5, F = 216.167, df(total) = 14, **p < 0.001 compared with MN9D cells, **p < 0.001 compared with MN9D/CON cells]. α-Synuclein mRNA levels were not significantly different in pSH1/α-SYN-transfected cells compared with MN9D and MN9D/CON cells. (c) Immunofluorescence staining of normal MN9D cells (a) and MN9D cells transfected with pSH1/α-SYN (b), pSH/CON (c) and pSH2/α-SYN (d) using an antibody against α-synuclein. Strong α-synuclein staining was observed in MN9D, pSH1/α-SYN-transfected and pSH/CON-transfected MN9D cells, whereas weak staining was observed in pSH2/α-SYN-transfected MN9D cells. Bar = 50 μm. (d) Western blot analysis of α-synuclein protein levels. Lane 1 MN9D cells; Lane 2 MN9D/CON cells; Lane 3 MN9D/α-SYN− cells. (e) Statistical analysis of α-synuclein protein levels in MN9D, MN9D/CON and MN9D/α-SYN− cells using the linear density ratio of α-synuclein/β-actin. α-Synuclein protein levels were significantly decreased in MN9D/α-SYN− cells (0.150 ± 0.004) compared with MN9D (0.401 ± 0.038) and MN9D/CON (0.321 ± 0.011) cells [n = 5, F = 162.034, df(total) = 14, **p < 0.001 compared with MN9D cells, **p < 0.001 compared with MN9D/CON cells]
Effects of Silencing α-Synuclein Expression on Cell Viability
No significant differences in cell morphology were observed between the three groups of MN9D cells (data not shown). As shown in Fig. 2, the cell viability of MN9D/α-SYN− cells was significantly decreased compared with control MN9D and MN9D/CON cells [n = 5, F = 421.457, df(total) = 14, **p < 0.001 compared with MN9D cells, **p < 0.001 compared with MN9D/CON cells]. Cell viability was decreased by 58.3 and 50% in MN9D/α-SYN− cells (0.091 ± 0.006) compared with MN9D (0.217 ± 0.012) and MN9D/CON (0.189 ± 0.005) cells, respectively (Fig. 2). These data suggest that α-synuclein may be important for the viability of MN9D cells.
Fig. 2Effects of silencing α-synuclein expression on cell viability. Cell viability was measured using an MTT assay. Cell viability was decreased in MN9D/α-SYN− cells compared with MN9D and MN9D/CON cells [n = 5, F = 421.457, df(total) = 14, **p < 0.001 compared with MN9D cells, **p < 0.001 compared with MN9D/CON cells]
TH Expression and Activity in MN9D Transfected Cells
Previous studies have reported that TH expression may be down-regulated by over-expression of α-synuclein in MES23.5 and M17 cells [3, 42]. Here, we examined whether a similar effect occurred after the silencing of α-synuclein expression in MN9D/α-SYN− cells. TH mRNA, protein and phosphorylation levels were measured by real-time RT-PCR and Western blot analysis, respectively. Real-time RT-PCR showed that there were no significant differences in TH mRNA levels between MN9D, MN9D/CON (116.00 ± 10.23%), MN9D/α-SYN− (135.89 ± 14.58%) and MN9D/α-SYN+ (85.67 ± 5.55%) cells (Fig. 3a). These observations show that silencing α-synuclein expression did not alter TH gene expression, suggesting that α-synuclein does not regulate expression of TH. Similar TH protein levels were observed in MN9D, MN9D/CON (103.55 ± 0.84%), MN9D/α-SYN− (126.40 ± 10.81%) and MN9D/α-SYN+ (83.89 ± 4.06%) cells by Western blot analysis (Fig. 3b, c). However, TH phosphorylation levels were significantly increased in MN9D/α-SYN− (157.63 ± 8.36%) cells compared with MN9D and MN9D/CON (97.45 ± 5.06%) cells, and significantly decreased in MN9D/α-SYN+ cells (20.36 ± 2.86%) (Fig. 3b, d). To directly assess TH activity, we used NSD-1015 to inhibit AADC, thereby blocking the conversion of l-DOPA to DA. We found that l-DOPA levels were increased in MN9D/α-SYN− cells (266.33 ± 11.05 nmol/l) compared with MN9D (121.92 ± 5.61 nmol/l) and MN9D/CON (124.89 ± 8.94 nmol/l) cells (Fig. 4). In contrast, a significant decrease in l-DOPA levels was observed in MN9D/α-SYN+ cells (41.14 ± 3.89 nmol/l). Since accumulation of l-DOPA could only occur if TH actively converted tyrosine to l-DOPA, these data suggest that TH activity was increased in MN9D/α-SYN− cells and diminished in cells over-expressing α-synuclein. These data support the hypothesis that silencing α-synuclein expression does not affect TH gene expression but can enhance TH activity by increasing TH Ser40 phosphorylation.
Fig. 3Effects of silencing α-synuclein expression on TH and phospho-Ser40-TH levels. (a) Statistical analysis of TH gene expression by real-time RT-PCR. Differences in TH mRNA levels between the transfected MN9D cells were not statistically significant (n = 5). (b) Western blot analysis of TH, phospho-Ser40-TH and β-actin protein levels. Lane 1 normal MN9D; Lane 2 MN9D/CON; Lane 3 MN9D/α-SYN−; Lane 4 MN9D/α-SYN+ cells. (c) Statistical analysis of TH protein levels in MN9D, MN9D/CON, MN9D/α-SYN− and MN9D/α-SYN+ cells using the linear density percent of TH/β-actin. No significant differences in TH protein levels were observed between the transfected MN9D cells (n = 5). (d) Statistical analysis of phospho-Ser40-TH protein levels in MN9D, MN9D/CON, MN9D/α-SYN− and MN9D/α-SYN+ cells using the linear density percent of phospho-Ser40-TH/TH. Phospho-Ser40-TH protein levels were significantly increased in MN9D/α-SYN− cells and significantly decreased in MN9D/α-SYN+ cells compared with MN9D and MN9D/CON cells [n = 5, F = 84.505, df(total) = 19, **p < 0.001 MN9D/α-SYN− cells compared with MN9D cells, **p < 0.001 MN9D/α-SYN− cells compared with MN9D/CON cells; **p < 0.001 MN9D/α-SYN+ cells compared with MN9D cells, **p < 0.001 MN9D/α-SYN+ cells compared with MN9D/CON cells]Fig. 4Effects of silencing α-synuclein expression on l-DOPA levels. Cell lysates were prepared from transfected MN9D cells incubated in the presence of 200 μM NSD-1015 for 30 min. l-DOPA levels were measured by HPLC. l-DOPA levels were significantly increased in MN9D/α-SYN− cells and significantly decreased in MN9D/α-SYN+ cells compared with MN9D and MN9D/CON cells [n = 6, F = 140.944, df(total) = 23, **p < 0.001 MN9D/α-SYN− cells compared with MN9D cells, **p < 0.001 MN9D/α-SYN− cells compared with MN9D/CON cells; **p < 0.001 MN9D/α-SYN+ cells compared with MN9D cells, **p < 0.001 MN9D/α-SYN+ cells compared with MN9D/CON cells]
Effects of Silencing α-Synuclein Expression on DA Levels
To assess differences in cytosolic DA levels between the transfected MN9D cells, we prepared cell extracts and measured DA content by HPLC with electrochemical detection. As shown in Fig. 5, DA levels were significantly increased in MN9D/α-SYN− cells compared with MN9D and MN9D/CON cells. The DA content was approximately 4.8-fold higher in MN9D/α-SYN− cells (834.72 ± 24.72 nmol/l) than in normal MN9D cells (186.04 ± 31.38 nmol/l) and 2.4-fold higher than in MN9D/CON cells (341.87 ± 46.25 nmol/l). No significant differences in DA content were observed between MN9D and MN9D/CON cells (Fig. 5). These results indicate that silencing α-synuclein expression leads to an increase in DA levels in MN9D cells.
Fig. 5Effects of silencing α-synuclein expression on DA levels. Changes in DA levels were detected by HPLC. DA levels were significantly increased in MN9D/α-SYN− cells compared with MN9D and MN9D/CON cells [n = 6, F = 161.774, df(total) = 17, **p < 0.001 compared with MN9D cells, **p < 0.001 compared with MN9D/CON cells]. Differences in DA levels between the MN9D and MN9D/CON cells were not statistically significant
Discussion
In the present study, we silenced the expression of α-synuclein in MN9D dopaminergic cells by vector mediated RNAi using the BLOCK-iT™ inducible H1 RNAi entry vector. This vector was selected for several reasons. Firstly, the vector contains a kanamycin resistance gene for selection in E.coli and a ZeocinTM resistance marker to allow generation of stable cell lines that express the shRNA of interest. Secondly, the cloning site of the vector contains a 4-nucleotide overhang on the 5′ end of each DNA strand, allowing directional cloning of the shRNA of interest. Thirdly, the H1 promoter of the vector is recognized by RNA polymerase III resulting in high-level, constitutive expression of shRNA in most mammalian cell types. We identified an effective targeting sequence for RNAi, which localized in the C-terminal coding sequence of the mouse α-synuclein gene, and used this to generate MN9D/α-SYN− cells in which the expression of α-synuclein was stably silenced. Due to the endogenous expression of α-synuclein and the dopaminergic characteristics of MN9D cells, MN9D/α-SYN− is a good cellular model for studying the normal function of α-synuclein and examining its role in PD pathogenesis.
We found that the cell viability of MN9D/α-SYN− cells was significantly decreased compared with MN9D and MN9D/CON cells, suggesting that loss of α-synuclein may induce cell injury directly or indirectly. Based on subsequent observations, we propose that a marked increase in cytosolic DA levels may account for decreased cell viability. DA can readily oxidize to generate hydrogen peroxide, superoxide and reactive DA quinones [16, 35], which are cytotoxic due to their inhibitory effects on the mitochondrial respiratory system [5, 13]. In addition, superfluous DA itself may be cytotoxic via inhibition of mitochondrial complex I activity [4]. Thus, it is also possible that increased cytosolic DA levels induced mitochondrial dysfunction resulting in decreased viability in MN9D/α-SYN− cells.
α-Synuclein may be involved in the regulation of TH, the rate-limiting enzyme of DA biosynthesis. Several studies have shown that α-synuclein regulates TH gene expression. In α-synuclein over-expressing MES23.5 dopaminergic cells, TH mRNA and protein levels were significantly reduced [42]. A similar effect was observed in M17 dopaminergic cells transfected with WT α-synuclein [3]. However, in the current study, no significant differences in TH mRNA and protein levels were observed between the four groups of MN9D cells. Interestingly, a previous study using MN9D cells reported that α-synuclein over-expression did not diminish endogenous TH levels [26], which is consistent with our results. We propose that the difference in TH levels between the present α-synuclein silenced system and the previously described α-synuclein over-expression system is mainly due to the different cell types and not due to the different ways in which the cells were manipulated.
Although TH protein levels remained unchanged in MN9D/α-SYN− cells, phosphorylation of TH at its Ser40 site was significantly enhanced. Furthermore, l-DOPA levels were significantly increased after NSD-1015 treatments. These results suggest that TH activity was enhanced in MN9D/α-SYN− cells, which is consistent with previous reports [24, 26]. In addition, we also found that cytosolic DA levels were significantly increased in MN9D/α-SYN− cells. Our data show that TH activity was enhanced in α-synuclein-silenced MN9D/α-SYN− cells, and that this was associated with an increase in DA biosynthesis followed by increased cytosolic DA levels. The mechanisms by which α-synuclein may regulate TH activity have not been elucidated. It is known that only the phosphorylated form of TH is active, and that the phosphorylation and dephosphorylation of TH are important in regulating DA biosynthesis [29, 37]. Furthermore, phosphorylation of TH at Ser40 results in the most prominent activation of TH [37]. Thus, it has been suggested that α-synuclein regulates TH activity by regulating the phosphorylation of TH [24]. Protein phosphatase 2A (PP2A), an important enzyme required for dephosphorylation of TH, can be activated by α-synuclein, resulting in a significant increase in TH dephosphorylation and reduction in TH activity [6]. In addition, other factors may also be involved in the regulation of TH phosphorylation by α-synuclein. For example, 14-3-3 enhances TH activity by binding directly to phosphorylated TH. α-Synuclein may directly interact with 14-3-3 [22], thereby accelerating the dissociation of 14-3-3 from phosphorylated TH. α-Synuclein may also affect TH activity by directly interacting with TH [26].
In conclusion, our data demonstrate that in PD pathology, silencing of α-synuclein expression results in enhanced TH activity and increased DA levels in neurons. We propose that in cells that unable to remove excess DA from the cytosol, DA itself and its oxidized products including reactive oxygen species and reactive DA quinones may lead to cell injury or even death. Further understanding of the normal function of α-synuclein in maintaining DA homeostasis may help to identify novel preventative or therapeutic strategies for PD.

α-synuclein
parkinson’s disease
dopamine
rna interference