Neuronal vulnerability to fetal hypoxia-reoxygenation injury and motor deficit development relies on regional brain tetrahydrobiopterin levels

Abstract

Hypertonia is pathognomonic of cerebral palsy (CP), often caused by brain injury before birth. To understand the early driving events of hypertonia, we utilized magnetic resonance imaging (MRI) assessment of early critical brain injury in rabbit fetuses (79% term) that will predict hypertonia after birth following antenatal hypoxia-ischemia. We examined if individual variations in the tetrahydrobiopterin cofactor in the parts of the brain controlling motor function could indicate a role in specific damage to motor regions and disruption of circuit integration as an underlying mechanism for acquiring motor disorders, which has not been considered before. The rabbit model mimicked acute placental insufficiency and used uterine ischemia at a premature gestation. MRI during the time of hypoxia-ischemia was used to differentiate which individual fetal brains would become hypertonic. Four brain regions collected immediately after hypoxia-ischemia or 48 h later were analyzed in a blinded fashion. Age-matched sham-operated animals were used as controls. Changes in the reactive nitrogen species and gene expression of the tetrahydrobiopterin biosynthetic enzymes in brain regions were also studied. We found that a combination of low tetrahydrobiopterin content in the cortex, basal ganglia, cerebellum, and thalamus brain regions, but not a unique low threshold of tetrahydrobiopterin, contributed etiologically to hypertonia. The biggest contribution was from the thalamus. Evidence for increased reactive nitrogen species was found in the cortex. By 48 h, tetrahydrobiopterin and gene expression levels in the different parts of the brain were not different between MRI stratified hypertonia and non-hypertonia groups. Sepiapterin treatment given to pregnant dams immediately after hypoxia-ischemia ameliorated hypertonia and death. We conclude that a developmental tetrahydrobiopterin variation is necessary with fetal hypoxia-ischemia and is critical for disrupting normal motor circuits that develop into hypertonia. The possible mechanistic pathway involves reactive nitrogen species.

Keywords: Cerebral palsy; Fetal brain; Free radicals; Hypertonia; Infant newborn; Sepiapterin.

Graphical abstract

Fig. 1

Fetal brain H–I protocol and MRI predictive biomarker. (A) Pregnant rabbit dam at E25 underwent MRI before, during, and after uterine ischemia. After imaging fetal brain tissue collected 20 min after (0 h) or after 48 h following uterine ischemia (48 h). Fetal brains regions assayed for BH4. (B) Patterns of ADC change (ordinate) in fetal brain. Pattern I (green) shows no significant change from baseline. Pattern II (blue) shows a drop from baseline during H–I but not below a threshold (dotted brown line). (C) Pattern III (brown) shows a drop from baseline below the threshold. Pattern IV (red) shows a further drop after the end of H–I during uterine reperfusion. (D) Patterns III or IV are predictive of hypertonia found postnatally. The receiver operating characteristic curve shows area under curve of 88%. (E) The subpopulations of MRI patterns as a percentage of all surviving fetuses undergoing H–I. (F) Number of fetuses stratified according to time of tissue collection and their MRI pattern. There were no deaths at 0 h and 7 deaths at 48 h. MRI was not analyzed for the dead fetuses. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

Tetrahydrobiopterin (BH4) levels in brain regions. Tissue collected immediately after end of H–I/reperfusion period (0 h group) and stratified by MRI patterns. (A) Thalamus BH4 is significantly lower in Hypertonia group than Non-Hypertonia (*p < 0.01, two-sample t-test, power for actual medium-effect size = 86%). All fetuses underwent H–I. (B) Comparing thalamus BH4 in sham (no H–I) with all MRI patterns undergoing H–I, groups are significantly different (*p < 0.05, ANOVA, power for actual medium-large-effect size = 79%). Post hoc tests show pattern I different from patterns III and IV, but not different from sham or pattern II. Patterns III and IV not different from sham or group II (post hoc comparisons with Student-Newman-Keuls multiple range and Bonferroni tests). (C) Cerebellum BH4 is significantly lower in Hypertonia group than Non-hypertonia group (*p < 0.05, two-sample t-test, power for actual medium-effect size = 68%). (D) Comparing cerebellar BH4 in sham with all MRI patterns, groups are significantly different (*p < 0.05, ANOVA, power for actual medium-large-effect size = 80%). Post hoc tests show pattern I different from all other groups (post hoc comparisons with Student-Newman-Keuls and Bonferroni tests). (E) Cortex BH4 is not significantly different between hypertonia and Non-hypertonia groups (power for medium-effect size = 74%, for actual effect size = 23%; type II error that the groups are actually different is only 26%). (F) Comparing cortex BH4 in sham with all MRI patterns; groups are not significantly different from each other (power for actual effect size = 99%; similarly, type II error is only 1%). (G) Basal ganglia BH4 is not significantly different between Hypertonia and Non-hypertonia groups (power for medium-effect size = 75%, for actual effect size = 12%). (H) Comparing basal ganglia BH4 in sham with all MRI patterns, groups are not significantly different from each other (power for medium-large-effect size = 79%, for actual effect size = 14%).

Fig. 3

Regional brain tetrahydrobiopterin (BH4) at 0 h and hypertonia: two-region analysis. A–F. Each data point (either dot or square) is defined by two BH4 values and is unique to one animal. Two-dimensional relationship plots of cortex BH4 with basal ganglia (A), cerebellum (B), and thalamus (C) comparing Hypertonia (filled circles) vs. Non-hypertonia groups (open squares). Correlation between two regions are significant in both groups using Pearson correlation coefficient. (A) Cortex BH4 and basal ganglia BH4 correlation for Hypertonia (red line) is different from that in Non-hypertonia (blue linear regression lines, note difference in slopes). Pearson correlation coefficient transformed into a Fisher's z score and statistically compared between Hypertonia and Non-hypertonia. (B) Cortex BH4 and cerebellum BH4 correlation for Hypertonia (red line) is different from that in Non-hypertonia (blue line); linear regression lines shown. (C) Other correlations of two regions between Hypertonia and Non-Hypertonia groups were not significant including between cortex BH4 and thalamus BH4, shown here. (D–F) Depicted in D–F are the correlation for values arbitrarily chosen to be ≤ 20 pmol/mg protein. D corresponds to A, E to B, and F to C. At low BH4 levels, in the basal ganglia, cerebellum, or thalamus (depicted in the ordinate) predisposes to hypertonia for any value of BH4 in cortex. More red dots (Hypertonia) than blue open squares (Non-hypertonia) in the lower left quadrant, indicates that at really low BH4 levels, a low BH4 level in two regions may still predispose a fetus to hypertonia. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4

Influence of brain region tetrahydrobiopterin (BH4) at 0 h and hypertonia. Three-dimensional plots showing differences in the BH4 of three brain regions; (B,D) compare hypertonia and (A,C) compare non-hypertonia. A and B show the X-axis as cortex BH4, Y-axis as basal ganglia BH4, and Z-axis as cerebellum BH4. C and D show the X-axis as thalamus BH4, Y-axis as cortex BH4, and Z-axis as cerebellum BH4. The plots are different for hypertonia compared with non-hypertonia when any three regions are selected and compared.

Fig. 5

Gender as a factor in tetrahydrobiopterin and susceptibility to hypertonia. Box-and-whisker plots show thalamus BH4, with no statistical difference between males and females when compared between the hypertonia and non-hypertonia groups (two-way ANOVA; power for actual medium-large effect size = 82%). Note that a subpopulation of males with high tetrahydrobiopterin do not develop hypertonia.

Fig. 6

Influence of brain region tetrahydrobiopterin (BH4) at 0 h and hypertonia. Path analysis using structural equation modeling with the best fit from various models using all four regional BH4 values causing hypertonia. The other three regions have both direct and indirect effects through thalamus BH4. Black shows coefficients of the direct effect, gray shows covariance effects, and black italcs shows errors to the path (e1 and e2). The overall model has absolute Chi-square of 0.0000, Goodness of Fit Index of 1.000, Akaike Information Criterion 28, Schwarz Bayesian Criterion 63, and Bentler Comparative Fit Index of 0.9668 using Proc Calis in SAS.

Fig. 7

Gene expression of tetrahydrobiopterin (BH4) enzymes. Expression of mRNA was analyzed in brain regions showing low or high BH4. Identification of genes for rabbit BH4 biosynthetic and regeneration enzymes was performed. (A) Correlation of thalamus BH4 with SPR only in the Hypertonia group (filled dots) vs. Non-hypertonia (open dots), p = 0.0472. No significant correlation in Non-hypertonia group. (B). Comparison between Non-hypertonia and Hypertonia groups did not show any difference with any of the enzymes except for GTPCH-1 (Wilcoxon two-sample test, p = 0.0076) and in (C) for PTPS (p = 0.0549). Because of multiple comparisons and Bonferroni correction, these were not considered significant.

Fig. 8

Nitrotyrosine formation in cortex. (A). Chromatogram of automated Western blot showing nitrotyrosine formation by the difference between blocked antibody (negative control, gray lower line on right half) and unblocked antibody (black). Shaded highlighted portion shows proteins that have specific binding to nitrotyrosine. (B, C) The cortex was the only region analyzed because of tissue availability. Increase in cortex nitrotyrosine formation in the Hypertonia group compared with the Non-hypertonia group using height (B) or area (C) of the scan peaks; Wilcoxon two-sample test, n = 6–8/group, *p < 0.05.

Fig. 9

Tetrahydrobiopterin in brain regions 48 h after H–I: Regional BH4 analysis in hypertonia and non-hypertonia groups (A,C,E,G) and MRI patterns and sham (B,F,D,H). No significant difference was found between hypertonia and non-hypertonia groups. The powers of two-sample t-test for medium effect size were: thalamus 48% (A), cerebellum 45% (C), cortex 48% (E), and basal ganglia 45% (G). Comparing sham with various MRI patterns, groups are significantly different for cortex (F) and thalamus (B); *p < 0.05, ANOVA, powers for actual large effect size = 64% and 63%, respectively. Student-Newman-Keuls and Bonferroni post hoc tests showed a difference in the cortex only for MRI groups I & II and sham vs. II, III, IV and sham (F). Comparing sham with various MRI patterns, the cerebellum (D) and basal ganglia (H) were not different between groups; powers for actual medium-large effect size 41% and 28%, respectively.

Fig. 10

Sepiapterin treatment post H–I prevents severe hypertonia and death. (A) Sepiapterin administration to pregnant dams after start of 40-min uterine ischemia at 79% gestation (E25) through delivery of fetuses at E31.5. Neurobehavioral battery done at P1 (E32). (B) Sepiapterin was compared to its vehicle (DMSO) as well as saline controls in view of DMSO's possible off-target effects. Abscissa shows gross classification of neurobehavior (hypertonia or postural changes classified as severe) and ordinate shows the number of kits at P1. Each animal is counted in its own group, whether DMSO, saline or sepiapterin treatment. DMSO (gray bars) and saline (white bars) were combined as controls because they were not different. Black bars indicate sepiapterin treated kits. Sepiapterin treatment increases the number of normal kits and decreases the number of dead kits. It is possible that some of the dead kits saved by sepiapterin contribute to the increase in the severe outcome.