Institute for Risk Analysis and Risk Communication, Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98105
2 To whom correspondence should be addressed at University of Washington, Department of Environmental and Occupational Health Sciences, 4225 Roosevelt Way NE, Suite #100, Seattle, WA 981056099. Fax: (206) 685-4696. E-mail: faustman{at}u.washington.edu.
Received February 24, 2005; accepted May 13, 2005
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ABSTRACT |
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Key Words: computational model; ethanol; fetal alcohol syndrome; neurogenesis; apoptosis; neocortex.
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INTRODUCTION |
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A systems biology approach to neurodevelopmental research will greatly enhance our understanding of mechanisms of normal neurodevelopmental processes and perturbations that may lead to neurodevelopmental disorders such as ARND (Andersen et al., 2005; Cummings and Kavlock, 2005
). This approach is meant to provide quantitative models to integrate the growing amounts of molecular, cellular, anatomical, and behavioral data that is being generated (Kitano, 2002
; Waters et al., 2003
). Here we develop computational models utilizing quantitative experimental data at the cellular level, specifically describing cell cycle kinetics and cell death in the developing brain. Our model links effects at the cell level to effects at the organ level by simulating neuron number acquisition in the adult through production and death of developing neurons in the developing organism.
This construct can also serve as a foundation for future application of data at the molecular and behavioral levels. Our model construct is based on the hypotheses that rapidly dividing, differentiating, and dying cells within a developing organ represent a sensitive target for environmental insults (Faustman et al., 1999; Leroux et al., 1996
). This framework is especially relevant for neurodevelopment, in which numerous experimental perturbations have shown disruption occurring during the discrete periods of neurogenesis, migration, and synaptogenesis will result in specific malformations (Berger-Sweeney and Hohmann, 1997
; Monk et al., 2001
; Rice and Barone, 2000
; Rodier, 2004
; Zamorano and Chuaqui, 1979
). For example, disruption of neurogenesis will most likely result in overall reduction of cell number. Manipulations that interrupt cell migration will likely result in ectopias, or abnormal locations of neurons; whereas manipulations that interrupt differentiation signals during synaptogenesis will likely result in apoptosis, or abnormalities in the connectivity of neurons. Considering synaptogenesis, a computational approach has been used to look at the in vitro effects of ethanol on the differentiation of neurons measured by dendritic growth, suggesting branching rather than elongation of dendrites is compromised (Granato and Van Pelt, 2003
).
Reduction in neuronal number is a salient feature of ethanol-induced developmental neurotoxicity (Maier and West, 2001). Stereological investigations in animal models have attempted to characterize the long-term effects on cell number in sensitive brain regions, such as the neocortex, hippocampus, and cerebellum (Maier et al., 1997
; Miller, 1995
, 1996
; Miller and Potempa, 1990
; Mooney et al., 1996
; Napper and West, 1995
; West et al., 1986
). By focusing the exposure period to correspond with the most susceptible period for the particular brain region of interest, these studies have shown different developmental processes are more or less sensitive to ethanol exposure depending on the brain region and time of exposure (Gohlke et al., 2002
).
Based on a review of stereological studies (Gohlke et al., 2002), we have focused on neocortical development as a sensitive target of ethanol-induced neurotoxicity, starting with the generation of neurons, their subsequent migration to permanent locations within the cortex, and finally the formation of synapses and a period of naturally occurring programmed cell death (PCD). The rat neocortex is particularly sensitive to neuronal loss following a relatively low exposure (approx. 150 mg/dl peak Blood Ethanol Concentration (BEC), which would be achieved in a pregnant woman after having 35 drinks) during the earlier periods of development including neurogenesis and migration (Miller and Potempa, 1990
). We have previously constructed computational models for normal neocortical development that simulates acquisition of adult neuron number through neurogenesis and synaptogenesis in the normal rat and mouse, corroborated by independent, stereologically determined neuron number data in the adult rodent (Gohlke et al., 2002
, 2004
).
Ethanol may cause neocortical developmental anomalies through inhibition of neuronal (Luo and Miller, 1998) and glial proliferation (Guerri et al., 1990
; Guizzetti and Costa, 1996
), induction of apoptosis (Dunty et al., 2001
; Ikonomidou et al., 2000
; Ward and West, 1992
), and perturbations during migration and synaptogenesis (Guerri, 1998
; Ward and West, 1992
). Various studies have shown ethanol to be a potent inhibitor of cellular proliferation, particularly in the developing brain (Dreosti et al., 1981
; Laev et al., 1995
; Pennington et al., 1984
). Effects seen include a reduction in the proliferating population or growth fraction (GF), and an increase in the length of the cell cycle, both contributing to fewer numbers of young neurons being generated (Guizzetti and Costa, 1996
; Miller, 1989
, 1992
; Miller and Kuhn, 1995
). No increases in pyknotic cells have been detected when exposure occurs during neurogenesis, suggesting again that inhibition of proliferation in the progenitor population is the target (Miller and Muller, 1989
). When we applied ethanol-induced cell cycle perturbations to our neocortical neurogenesis model, our simulations accurately predicted independent stereological evidence of long-term neocortical neuronal loss after an in utero exposure of 150 mg/dl peak BEC per day in the rat (Gohlke et al., 2002
).
Exposure to ethanol has also been shown to cause alterations in the natural waves of PCD during synaptogenesis, which occurs postnatally in the rat or during the third trimester in humans (Climent et al., 2002; Ikonomidou et al., 2000
). Ikonomidou et al. (2000)
suggest that, by blocking NMDA glutamate receptors and activating GABA receptors, ethanol triggers widespread increases in apoptosis during the period of synaptogenesis of many brain regions including the hippocampus, thalamus, and frontal, parietal, cingulate, and retrosplenial cortex. A discreet window of time, coinciding with the synaptogenesis period of each region tested, occurring anywhere from E19 to P14 depending on the region, was found to be the most susceptible period (Ikonomidou et al., 2000
). Cell death was measured by DeOlmos silver staining in this study and was confirmed with Caspase 3 activation in a more recent study (Olney et al., 2002
). Twenty-four h after a subcutaneous injection of ethanol (2.5 g/kg x 2) resulting in a peak BEC of 500 mg/dl, forebrain tissue contained 15 times the amount of apoptotic, silver-stained neuronal cells of control tissue (Ikonomidou et al., 2000
). Different acute exposures producing diverse blood ethanol profiles were also analyzed, and a dose-response relationship suggesting doses producing a peak BEC of 200 mg/dl or above for more than 4 h can significantly increase apoptotic neurons. The effect became progressively more severe in proportion to the length of time the BEC exceeded 200 mg/dl (Ikonomidou et al., 2000
). An earlier wave of apoptosis was subsequently identified using Caspase 3 activation at lower peak BECs of approximately 120 mg/dl (Olney et al., 2002
, 2004
; Tenkova et al., 2003
). Caspase 3 is a key component in a series of caspases that are activated in apoptosis to carry out the programmed degradation of proteins and DNA within the cell. Caspase 3 activation is frequently used to identify toxicant-induced apoptosis (Robertson and Orrenius, 2000
), whereas DeOlmos silver stain does not differentiate between apoptosis and other forms of cell death, such as necrosis.
The differential contribution of each of these mechanisms, namely inhibition of proliferation and induction of cell death, to the final spectrum of neurodevelopmental disorders attributed to in utero exposure to ethanol remains to be elucidated. Therefore, the construct of our computational model allows for direct quantitative linkage of these cellular mechanisms to a final outcome on neuronal number in the adult and can estimate the potential relative contributions of these mechanisms across dose ranges and developmental life stages.
The research presented in this paper builds upon the mechanistic work described above and quantitatively evaluates ethanol-induced cell death in the developing neocortex using a computational modeling approach. Our extended computational model for normal neocortical development includes PCD during the period of neocortical synaptogenesis, postnatal day 0 to postnatal day 14 (P0P14) in the rodent and is described elsewhere (Gohlke et al., 2004). We apply data of ethanol-induced cell death during the synaptogenesis period using both DeOlmos silver staining and Caspase 3 activation to identify dying cells in the rat to our model of neocortical development (Ikonomidou et al., 2000
; Tenkova et al., 2003
). We compare our simulations with those of our previous model looking at exposure during neocortical neurogenesis (G13G19) (Gohlke et al., 2002
). To further evaluate our mode of action based dose-response functions for ethanol-induced perturbations of proliferation and cell death, we also compare them to independent in vitro and in vivo studies. The resultant biologically based dose response (BBDR) models can facilitate improvements in risk assessment practices by quantitating the relative importance of temporally specific effects during the critical stages of neocortical neurogenesis and synaptogenesis.
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MATERIALS AND METHODS |
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Model of neocortical development.
In this application, the X cell represents progenitor cells in the ventricular epithelium, where one of three outcomes is possible: division, differentiation into a Y cell, or death (Fig. 1E), whereas the Y cell has two potential outcomes: division or death. For our neocortical model, the Y division rate is set to zero, as Y cells are defined as postmitotic neurons migrating to the cortical plate and subsequently differentiating. Therefore, the founder cell population of neurons in the neocortex is tracked through neurogenesis and synaptogenesis simulating the acquisition of final adult neocortical neuronal cell number. Our model results have compared favorably with independent stereological estimates of adult neocortical cell number in the mouse and rat (Bondolfi et al., 2002; Calhoun et al., 1998
; Duffell et al., 2000
; Moller et al., 1990
; Mooney et al., 1996
; Strange et al., 1991
).
Our model of neocortical neurogenesis has been previously described (Gohlke et al., 2002). Briefly, experimental data describing normal murine and rat neocortical neurogenesis were used to determine parameter values for our model of murine and rat neocortical neurogenesis under normal development (Miller and Kuhn, 1995
; Takahashi et al., 1995
, 1996
, 1997
). In our model, progenitor cells making up the pseudostratified ventricular epithelium (PVE) located in the developing rostral neural tube are referred to as "X cells," and postmitotic young neurons leaving the PVE to migrate and subsequently populate the cortical plate are labeled "Y cells" (Figs. 1A and 1B). The X cells have a time-dependent division and differentiation rate, and each subsequent cycle contributes a greater percentage of G1 cells to the leaving population that begin migration and eventually form the cortical layers, therefore leaving fewer cells to replenish the proliferating population (Fig. 1B).
The period of natural cell death for the rodent neocortex occurs between postnatal days 1 and 14 (P1P14), with a peak between P4 and P7, and has been quantified by stereological examination of stained brain sections (Figs. 1C and 1D) (Ferrer et al., 1990; Spreafico et al., 1995
; Thomaidou et al., 1997
; Verney et al., 2000
). In our previous investigation we analyzed these four datasets for estimating cell death through the period of synaptogenesis (Gohlke et al., 2004
). Here we use a baseline time-dependent postnatal cell death rate based on the Thomaidou et al. (1997)
dataset estimating the TUNEL+ and/or pyknotic neurons on P0, P7, and P14 (Table 1). We chose the Thomaidou et al. (1997)
dataset as our baseline, bacause this dataset was the most complete dataset analyzing neuronal death in the whole neocortex through the synaptogenesis period using the TUNEL technique, the technique used to estimate the clearance time of dying cells (see Gohlke et al., 2004
).
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We previously performed a sensitivity analysis of the growth fraction (GF), or percentage of cells actively cycling, an important experimental parameter in our neurogenesis model (Gohlke et al., 2002) that varies between 80 and 100% depending on the study (Gohlke et al., 2004
). Here we use the mid-range experimental estimate of 93% during rat neocortical neurogenesis (Cai et al., 1997
; Miller and Kuhn, 1995
; Miller and Nowakowski, 1991
; Miyama et al., 1997
; Takahashi et al., 1995
).
In the current rat model we are using the experimentally derived mouse founder cell population (X0) increased by 20% based on the larger fetal brain of the rat. Previously, we performed a sensitivity analysis of this parameter in which the founder cell population (X0) is increased in the rat model by 20 and 40%, respectively, and demonstrated that a 20% increase in the founder population leads to more accurate model predictions of neuronal number (Gohlke et al., 2004).
Dose-response analyses.
We have fit a Weibull dose-response function to our models for ethanol-induced cell death during synaptogenesis using the following equations to determine the predicted fractional decrease in neurons at a given dose:
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RESULTS |
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The data used in these calculations are shown in Table 2. An example calculation of percent cell death labeled for the peak BEC dose of 500 mg/dl at each timepoint evaluated is shown in Table 3. Figure 2 shows these time- and dose-dependent calculations transformed into a cell death rate (see Equation 1) for each time-step in which ethanol affects the cell death rate in our model. Although the MK801 time-course shows a significant drop below baseline of apoptotic neurons at the 48-h timepoint, we chose to keep the death rate at baseline at 48 h (see Table 3). If ethanol does indeed cause a decrease in cell death at 48 h, our simulations overestimate the cell death caused by ethanol.
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Ethanol may cause more acute apoptosis at lower levels of exposure than previously documented using the DeOlmos silver staining methodology. Using Caspase 3 activation immunohistochemistry to identify dying cells, Tenkova et al. (2003) suggested ethanol can induce apoptosis at doses causing peak BECs of approximately 122 mg/dl. To compare this Caspase 3 activation data to the data using silver staining techniques we utilize the quantitative data presented in Tenkova et al. (2003)
, showing significant increases of cell death labeled neurons in the ganglion cells of the retina and the superior colliculus (Tenkova et al., 2003
). Although this data is not in neocortical structures, similar results in other brain regions including neocortical structures have been discussed (Olney et al., 2002
, 2004
); however, quantitative data has not been published. Controls for this dataset on P8 matches well with other published data using TUNEL and pyknotic nuclei such as Ferrer et al. (1992)
and Thomaidou et al. (1997)
, showing only occasional isolated Caspase 3activated neuronal profiles (Olney et al., 2002
). To model this data, we determined the percent cell death labeled neurons at each timepoint after a given dose of ethanol on P7 using the following equations:
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The data used in these calculations are described in Table 1 and shown in Table 2. Our analysis approach is similar to that of the silver staining dataset (see Equations 45). Since only 3 timepoints were evaluated, we assume the peak labeling occurs at the 8-h timepoint, and the 10 and 12-h timepoints are similar in death labeled neurons as the 4 and 6 h timepoints (Fig. 2). The resulting calculation of percent cell death labeled for the peak BEC dose of 500 mg/dl at each timepoint evaluated is shown in Table 3. Figure 2 shows these time- and dose-dependent calculations used in the cell death rate equation (see Equation 1) between P7 and P9. Again we use the clearance time of 2.5 h based on Thomaidou et al. (1997), which is supported by a recent study looking at ethanol-induced cell death using Caspase-3 activation in the developing cerebellum (Light et al., 2002
).
Model Simulations of Ethanol-Induced Cell Death during Synaptogenesis
We have simulated dose-dependent ethanol-induced neocortical neuron death in the rat using DeOlmos silver staining data of Ikonomidou et al. (2000) (Fig. 3). This plot shows predicted number of neocortical neurons after various in utero exposures to ethanol reaching specific peak BECs. As shown here and detailed further elsewhere (Gohlke et al., 2004
), our model for unexposed rats predicts adult neocortical number well compared to independent, stereologically determined mean estimates between 1.5 x 107 and 2.1 x 107 neurons (Duffell et al., 2000
; Moller et al., 1990
; Mooney et al., 1996
; Stewart et al., 1997
; Strange et al., 1991
). We have shown here our most conservative (predicting the largest amount of cell death) model of cell death, which incorporates predicted cell death rates beyond 24 h post exposure mirroring those prior to the 24-h peak seen in the experimental research. Our predicted dose-dependent decreases in neurons at the peak blood ethanol concentrations reached in the study by Ikonomidou et al. (2000)
are shown with error bars representing the predicted standard deviation of neocortical neurons in adult rats based on stereological studies in unexposed rats (Duffell et al., 2000
; Moller et al., 1990
; Mooney et al., 1996
; Stewart et al., 1997
; Strange et al., 1991
). Furthermore, our model predicts significant neuronal loss only at the highest dose, reaching a peak BEC of 500 mg/dl.
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We compare results of simulations from our neurogenesis model to independent stereological evidence on long-term neocortical neuronal loss in the somatosensory region of the neocortex after an exposure regimen with peak BECs reaching approximately 140 mg/dl once during each day of neurogenesis and showing a 33% neuronal cell deficit (Miller and Potempa, 1990) (Fig. 5). Stereological analysis of the medial prefrontal cortex also shows a 30% reduction in neurons after a chronic exposure paradigm through neurogenesis with peak BECs of approximately 117 mg/dl (Mihalick et al., 2001
). This study also showed generalized learning impairment consistent with prefrontal damage. Furthermore, an analysis of DNA content of the entire neocortex shows a similar long-term neuronal loss after exposure during neurogenesis (Miller, 1996
). Conversely, when binge ethanol exposure reaching a peak of approximately 300 mg/dl is given during the period of synaptogenesis, no long-term neuronal loss was found, although glial cell loss and overall brain volume and weight reductions were evident (Mooney et al., 1996
).
As very little is known about dose-response relationships in vivo, we broadened our analysis by comparing effects on proliferation versus effects on cell death in in vitro model systems and compared these with our model predictions (Fig. 5). As a model of neocortical neurogenesis, primary cultures of neuroepithelial cells dissociated from the embryonic rat telencephalon on E13 were kept in serum-free medium containing basic fibroblast growth factor (bFGF) for 5 days then switched to bFGF-free medium (Ma et al., 2003). Ethanol exposure for 24 h dose-dependently blocked neuroepithial expansion measured by reduced 3H-thymidine incorporation. To model neocortical synaptogenesis, organotypic explant cultures of the developing rat cerebral cortex (P2) were maintained for 6 days in vitro (Cheema et al., 2000
). Apoptosis was measured using enzyme-linked immunosorbent assay for DNA fragmentation and flow cytometric analysis of Annexin-V binding to phosphatidylserine externalized to the outer leaflet of the plasma membrane. Ethanol exposure (on day 4 and 6) produced a dose-dependent induction of apoptosis on day 6. Another study corroborates the above study, looking at ethanol-induced neuronal death in organotypic cultures of the rat cerebral cortex and showing no effects until concentrations of 400 mg/dl are reached (Mooney and Miller, 2003
). These in vitro model systems support our model simulations suggesting ethanol-induced inhibition of neurogenesis may be a more sensitive endpoint for ethanol-induced developmental neocortical toxicity than induction of apoptosis during synaptogenesis.
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DISCUSSION |
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Other studies also suggest inhibition of proliferation may be a more important mechanism than induction of cell death in the etiology of ethanol neurotoxicity. A time-course study of neuron number, 3H-thymidine incorporation, and percent dying neurons in the principal sensory nucleus of the trigeminal nerve suggests the decreases in neuron number are more dependent on anti-proliferative effects than on induction of cell death at a chronic dosing regimen reaching a peak BEC of approximately 143 mg/dl (Miller, 1999). Furthermore, a recent mathematical model comparing ethanol-induced cell death versus inhibition of proliferation in primary cortical cultures and B104 neuroblastoma cells at media concentrations reaching 400 mg/dl also suggests the inhibition of proliferation is a larger contributor to the loss of cells when compared to cells lost from induction of cell death (Miller, 2003
). However, induction of cell death prenatally during neurogenesis is most likely dose dependent, in that higher doses of ethanol may induce cell death prenatally as well as postnatally. In vivo analyses at lower levels of exposure (150 peak daily BEC) show no increases in cell death during the prenatal neurogenesis period (Miller, 1989
, 1999
).
Our model prediction for neuronal loss after moderate to heavy exposures (below peak BEC 300 mg/dl) during the period of synaptogenesis is relatively small (approx. 1015% neuronal loss), taking into account normal variability of rodent neocortical neuronal number is between 10 and 13% (Duffell et al., 2000; Moller et al., 1990
; Mooney et al., 1996
; Stewart et al., 1997
; Strange et al., 1991
). Although a stereological analysis validates our simulations showing no statistically significant permanent decrease in neocortical neuronal number after postnatal exposure (peak BEC = 251387 mg/dl) (Mooney et al., 1996
), it should be noted that neuronal loss in other brain regions such as the cerebellum and hippocampus after a postnatal exposure scenario are well documented (for review see Livy et al., 2003
; Maier and West, 2001
). Furthermore, although long-term neocortical neuronal loss is not evident after exposure during synaptogenesis, this exposure scenario has been shown to cause long-term reduction in the glial cell number and total DNA content as well as decreased volume and weight of neocortical structures (Maier et al., 1997
; Mooney et al., 1996
). It is postulated that ethanol-induced effects on glial proliferation during this period may account for these findings.
There are over 12 million glial cells in the mature rat neocortex (Mooney et al., 1996), and they are produced almost exclusively during the brain growth spurt coinciding with synaptogenesis (Guerri, 1998
; Parnavelas, 1999
; Rakic, 1991
). Ethanol is thought to target astrogliogenesis (Costa and Guizzetti, 2002
). As a model of astrogliogenesis, Guerri et al. (1990)
found a significant increase in cell cycle length of primary cortical astrocyte cultures from newborn rat fetuses exposed to ethanol (Guerri et al., 1990
). Cellular doses equivalent to a mildly inebriating in vivo dose (BEC of 92115 mg/dl) were found to inhibit muscarinic receptor-mediated primary rat cortical astrocyte (E21) and human astrocytoma proliferation by as much as 70% (Guizzetti and Costa, 1996
). The dose-response relationship found in this study (Guizzetti and Costa, 1996
), and other studies looking at ethanol-induced inhibition of rat glial cell proliferation mediated through PKC signal transduction (Kotter et al., 2000
), including fetal human astrocyte cultures (Guizzetti et al., 2003
), is much steeper than dose-response relationships found in neuronal cell lines or primary neuronal cultures (Luo and Miller, 1997
, 1998
; Ma et al., 2003
) (Fig. 6). This data suggests that astrogliogenesis, coinciding with the synaptogenesis period, may be particularly sensitive to ethanol, explaining the reduced brain mass and loss of glial cells in the mature neocortex after ethanol exposure during this period (Maier et al., 1997
; Mooney et al., 1996
).
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Ethanol's abilities to cause cell death and inhibit proliferation may be dependent on interactions with the GABA and glutamate neurotransmitter systems. Ethanol has been shown to inhibit glutamate transmission through both NMDA and AMPA receptors (Fischer et al., 2003; Gruol et al., 1998
; Li and Kendig, 2003
). Recent studies have implicated GABA and glutamate in the regulation of neocortical neural progenitor proliferation (Haydar et al., 2000
; LoTurco et al., 1995
; Monk et al., 2001
). These neurotransmitters stimulate proliferation in the ventricular zone, while inhibiting proliferation in the subventricular zone through the GABAA and AMPA/kainate receptors (Haydar et al., 2000
; LoTurco et al., 1995
). Furthermore, there is evidence that ethanol actually induces subventricular proliferation while inhibiting ventricular proliferation in the developing neocortex (Miller, 1989
; Miller and Nowakowski, 1991
), which may be explained by the opposing effects of GABA and glutamate on these two different proliferative populations (Haydar et al., 2000
). Therefore, ethanol's ability to both inhibit proliferation and induce apoptosis during different stages of neocortical neuronal development may be explained by its glutamate antagonist and GABAmimetic properties. Interestingly, ethanol-induced inhibition of astroglial proliferation may be mediated through another neurotransmitter system, namely the cholinergic muscarinic receptor (Balduini et al., 1991
; Guizzetti and Costa, 1996
; Guizzetti et al., 2003
)
Although these time-dependent effects of ethanol on neocortical development may be explained by the same mechanism of action, it is important to differentiate the sensitivity of these periods to ethanol-induced effects. Our current model suggests ethanol's ability to inhibit proliferation during neurogenesis may have a greater long-term impact on neuronal number than the induction of apoptosis during the period of synaptogenesis, although the duration of exposure is longer to encompass neurogenesis than that simulated to encompass the sensitive window during synaptogenesis. Future linkage of our current model with a physiologically based pharmacokinetic (PBPK) model may allow for more detailed analysis of the tissue dose and exposure duration components of ethanol-induced developmental neurotoxicity. We have utilized this methodology to analyze methylmercury neurodevelopmental toxicity (Lewandowski et al., 2002).
Our current analysis involves the extrapolation to lower doses. However, the current lack of experimental data bars comparison of our model construct at doses below approximately 100 mg/dl peak daily BEC. There is empirical evidence suggesting the sensitivity of neurogenesis in the Rhesus monkey, at low daily alcohol exposure (peak BEC approx. 20 mg/dl) as it relates to neurobehavioral outcomes, is especially sensitive to exposure during earlier neurodevelopmental processes (gestational days 050) when compared to later exposure scenarios in these same studies (gestational days 50135) (Schneider et al., 2001).
Our current model does not take into account potential compensatory actions of the developing neocortex; therefore, simulation of exposure throughout neurogenesis and synaptogenesis would predict an additive response. For example, at a chronic dose of approximately 35 drinks/day (peak BEC of 150 mg/dl per day) our model would predict a 3540% neuronal deficit at the end of neurogenesis and an additional 715% neuronal deficit at the end of synaptogenesis. The research described below suggests compensatory mechanisms may come into play.
After in utero exposure to ethanol, irradiation, or ethylnitrosourea (ENU), there is evidence suggesting compensatory alterations in proliferation and death rates following the initial insult (Ferrer et al., 1992; Miller, 1999
; Mooney and Miller, 2001
; Oyanagi et al., 1998
). For example, after the initial inhibition of proliferation in the developing rodent brain immediately following ENU administration, a subsequent increase in proliferation is seen at later timepoints (Oyanagi et al., 1998
). Repair mechanisms may also take place during synaptogenesis by inhibiting PCD. Data presented in Mooney and Miller (2001)
indicate reduced Caspase 3 expression during synaptogenesis after exposure during neurogenesis in the rat neocortex (Mooney and Miller, 2001
). In line with this finding, PCD during synaptogenesis in the cerebral cortex has been shown to decrease in microencephalic rats produced after prenatal X-irradiation during neurogenesis, suggesting postnatal cell death may be correlated to the total number of cortical neurons present after neurogenesis (Ferrer et al., 1992
).
Recent studies have indicated adolescent and adult neurogenesis in the hippocampus is also acutely sensitive to ethanol-induced inhibition (Herrera et al., 2003; Jang et al., 2002
; Nixon and Crews, 2002
). For example, Jang et al. (2002)
shows a dose-dependent reduction of BrdU-positive cells in the rat dentate gyrus starting at a peak BEC reaching only 4 mg/dl. However, some studies also suggest enhanced proliferation of these progenitor granule cells up to 3 weeks after the exposure; thus, regeneration of the lost neurons may occur (Pawlak et al., 2002
; Zharkovsky et al., 2003
). It should be noted that adult generated neurons most likely have specific functional capabilities and therefore cannot replace those generated during development (Gould and Gross, 2002
).
We have discussed two mode of action hypotheses, namely inhibition of proliferation versus induction of apoptosis, and how they relate to our model and implications for risk assessment. For example, exposure assessment is an important part of risk assessment. We have evaluated human exposure levels and how they relate to our model of organ-level effects in the rodent. Another key component of risk assessment is risk characterization and an understanding of the mechanisms by which agents can cause effects and interact with susceptibility. In this paper we have evaluated temporal susceptibility and the key components of dose response assessment and risk characterization for ethanol effects on neocortical development. Our computational modeling approach specifically highlights the importance of a critical evaluation of temporal sensitivity in the risk assessment of developmental neurotoxicants.
Our computational model uncovers an important concept for assessing risk based on transient developmental perturbations. Although ethanol induces a measurably intense spike in apoptotic neurons (on average 15 times that of the baseline), this transient response may not confer to a significant long-term neuronal loss. In contrast, a relatively small, potentially harder to detect, lengthening of the cell cycle (less than two times the baseline) at the beginning of neurogenesis, can result in massive neuronal deficits in the mature neocortex. Therefore, transient impacts during development must be analyzed within the context of the critical underlying process in which they occur. Our modeling efforts suggest this type of context leads to more robust predictions of long-term impacts.
The rich database for ethanol developmental neurotoxicity found in the literature is an excellent source for evaluation of a systems biology computational model approach. At the cellular level, ethanol-induced developmental neurotoxicity is characterized by a range of effects depending on the dose and time of exposure (see Maier and West, 2001, for review). However, reduction in cell number may be a sufficient explanation to describe the key toxic effects of ethanol for risk assessment purposes. This mode of action hypothesis has been used to quantitatively evaluate methylmercury neurodevelopmental toxicity in a biologically based model framework (Lewandowski, 2000
; Lewandowski et al., 2003
). Integration of other neurodevelopmental toxicants such as organophosphate pesticides, fungicides such as benomyl, and irradiation-induced effects into an overall model for developmental neurotoxicology is also being explored (DeFrank et al., 2004
; Faustman et al., 2005
). A mode of action modeling methodology has the potential to vastly improve the usage of scientific data in the developmental toxicology risk assessment arena by providing a quantitative framework in which cellular and eventually molecular effects can be linked to an adverse neurodevelopmental outcome.
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NOTES |
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ACKNOWLEDGMENTS |
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