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Adverse effects of Δ9-tetrahydrocannabinol on neuronal bioenergetics during postnatal development
Johannes Beiersdorf, Zsofia Hevesi, Daniela Calvigioni, Jakob Pyszkowski, Roman Romanov, Edit Szodorai, Gert Lubec, Sally Shirran, Catherine H. Botting, Siegfried Kasper, Geoffrey W. Guy, Roy Gray, Vincenzo Di Marzo, Tibor Harkany, Erik Keimpema
Johannes Beiersdorf, Zsofia Hevesi, Daniela Calvigioni, Jakob Pyszkowski, Roman Romanov, Edit Szodorai, Gert Lubec, Sally Shirran, Catherine H. Botting, Siegfried Kasper, Geoffrey W. Guy, Roy Gray, Vincenzo Di Marzo, Tibor Harkany, Erik Keimpema
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Research Article Cell biology Development

Adverse effects of Δ9-tetrahydrocannabinol on neuronal bioenergetics during postnatal development

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Abstract

Ongoing societal changes in views on the medical and recreational roles of cannabis increased the use of concentrated plant extracts with a Δ9-tetrahydrocannabinol (THC) content of more than 90%. Even though prenatal THC exposure is widely considered adverse for neuronal development, equivalent experimental data for young age cohorts are largely lacking. Here, we administered plant-derived THC (1 or 5 mg/kg) to mice daily during P5–P16 and P5–P35 and monitored its effects on hippocampal neuronal survival and specification by high-resolution imaging and iTRAQ proteomics, respectively. We found that THC indiscriminately affects pyramidal cells and both cannabinoid receptor 1+ (CB1R)+ and CB1R– interneurons by P16. THC particularly disrupted the expression of mitochondrial proteins (complexes I–IV), a change that had persisted even 4 months after the end of drug exposure. This was reflected by a THC-induced loss of membrane integrity occluding mitochondrial respiration and could be partially or completely rescued by pH stabilization, antioxidants, bypassed glycolysis, and targeting either mitochondrial soluble adenylyl cyclase or the mitochondrial voltage-dependent anion channel. Overall, THC exposure during infancy induces significant and long-lasting reorganization of neuronal circuits through mechanisms that, in large part, render cellular bioenergetics insufficient to sustain key developmental processes in otherwise healthy neurons.

Authors

Johannes Beiersdorf, Zsofia Hevesi, Daniela Calvigioni, Jakob Pyszkowski, Roman Romanov, Edit Szodorai, Gert Lubec, Sally Shirran, Catherine H. Botting, Siegfried Kasper, Geoffrey W. Guy, Roy Gray, Vincenzo Di Marzo, Tibor Harkany, Erik Keimpema

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Figure 4

THC disrupts the mitochondrial membrane potential and changes biophysical properties of the neuronal plasma membrane in vitro.

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THC disrupts the mitochondrial membrane potential and changes biophysica...
(A) Representative images of cortical primary neurons exposed to vehicle or to the THC concentrations indicated and processed by the Mito-ID assay. Note that THC-induced mitochondrial damage reduced the high mitochondrial membrane potential (MMP), while leaving its low component unchanged. Scale bar: 100 μm. (B) Quantitative analysis of the high/low MMP ratio revealed dose-dependent THC effects. (C and D) MMP in the presence of pTHC with or without AM251 (1 μM; C) or O-2050 (100 nM; D), both cell-permeable CB1R antagonists (40, 85). (E) Synthetic THC (sTHC) was as efficacious in disrupting the MMP as pTHC. Dose-response relationship is shown. (F) Illustration of the cell indentation procedure including relevant parameters for the calculations of the effective Young’s modulus: P, load induced by indenter tip; h, displacement; R, indenter radius. (G) Load displacement curve. The linear elastic response of the loading curve (red) was used to calculate cellular surface stiffness following the Hertz model (141, 142). (H and I) The effective Young’s modulus was dose-dependently reduced by THC (H), with O-2050 unable to prevent a significant reduction in membrane stiffness brought about by 10 μM THC (I). (J) Stress-relaxation curves indicate the altered viscoelastic profile of THC-exposed neurons relative to controls. Data in B–E were normalized to control. Data in B and C were expressed as mean ± SD of triplicates with individual experiments (circles) using n = 10 replicates each. Data in D and E were expressed as the mean ± SD of 10 parallel observations. Nanoindentation data were on n = 15–33 cells/group and expressed as mean ± SEM. *P < 0.05, **P < 0.005, ***P < 0.001, ****P < 0.0001; 1-way ANOVA followed by Bonferroni’s post hoc test was used for MMP measurements and nanoindentation data.

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