The Chris Dulla Lab

Christopher Dulla, Ph.D.

Professor and Vice Chair

Genetics, Molecular and Cellular Biology Program Program
Neuroscience Program
MS in Pharmacology & Drug Development Program
Cell, Molecular & Developmental Biology Program

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Research

Lab Members

The Role of Glutamate in Brain Development

The control of neurotransmitter release is a highly regulated process, as is the fate of neurotransmitter once it is released from the presynaptic terminal. Each time NT is released a transient occurs consisting of a rapid rise in the extracellular concentration of neurotransmitter followed by a return to baseline concentration. We aim to understand how glutamate activity drives cortical network maturation and how astrocyte glutamate uptake provides spatio-temporal control of neurotransmitter transients. We suspect that disruption of glutamate signaling in the developing cortex can lead to pathological disease states such as epilepsy. Our previous work has shown that the maintenance of stereotyped glutamate transients ensures proper neuronal network function and if perturbed can lead to pathological, epileptic network activity. The specific questions we aim to answer are: 1) how does glutamate reuptake shape NT transients in the developing brain, 2) how does neonatal brain injury alter NT transients, and 3) can these changes contribute to the progression of diseases such as epilepsy.

Figure 1. Glutamate transport by astrocytes is slower in neonatal cortex than in hippocampus. A. Representative glutamate transporter current (TC ) traces from hippocampal (grey) and cortical astrocytes (black) at ages P3-P28 normalized by amplitude. B. Similar to A. arranged by cortex (left panel) and hippocampus (right panel), showing ages P3 (black), P7 (grey), P14 (black dashed), P21 (light grey), and P28 (grey dashed). C. Representative example of a glutamate TC from a P7 cortical astrocyte before 100 μM TBOA wash-on (black line), after TBOA wash-on (light grey line), and showing the original trace with the TBOA trace subtracted (dark grey line). D. Quantification of fast decay constants (τFast) at P3 (nCor = 15, nHipp = 14 cells) P7 (nCor = 17, nHipp = 25 cells), P14 (nCor = 15, nHipp = 18 cells). P21 (nCor = 18, nHipp = 17 cells), and P28 (nCor = 9, nHipp = 19 cells). E. Quantification of TC centroids (). F. Quantification of TC charge transfer from the onset of the laser pulse to the return of the current to baseline. G. Quantification of the maximum TC amplitude after normalization for membrane and input resistance. Charge transfer and amplitude are normalized to membrane and input resistance. D,G. Two sample t-test, * P < 0.05, ** P < 0.01, *** P < 0.001. F. P > 0.05, one-way ANOVA. All error bars indicate SEM. 

Traumatic Brain Injury

Traumatic brain injury (TBI) causes cognitive and motor dysfunction and greatly increases the risk of developing epilepsy. Recent increases in head trauma due to sports, military, and automobile related injuries have increased the need to better understand the cellular and molecular changes which occur following brain trauma and to develop novel treatment strategies. Following TBI, inhibitory GABAergic interneurons are lost. These cells are essential to controlling network function and their loss may be linked to epilepsy. We have begun experiments which will allow us to pinpoint regions of interneuron loss following brain injury using cutting edge imaging and electrophysiological technologies. Our studies will determine whether interneuron loss or dysfunction contribute to pathological brain activity.

Figure 2. Number of parvalbumin (+) and somatostatin (+) GABAergic interneurons decrease following TBI. Representative images from PV (A) (red, left) and SST (E) (red, left) from sham- and CCI-injured cortical sections. NeuN images are shown in green and merged images are in yellow. Global quantification of PV+ (B) and SST+ (F) cells per 10 000 μm2 cortex from the entire cortical slice shows a significant decrease in cell number in CCI compared with sham-injured cortex, paired t-test. (C) Quantification of PV+ cells per 10 000 μm2 in CCI injured deep cortical subregions showing a significant decrease in cell number in the area adjacent to the CCI lesion, proximal deep and distal deep layers compared with deep sham layers. (D) Quantification of PV+ cells per 10 000 μm2 in CCI injured superficial cortical subregions showing a decrease in cell number in the area adjacent to the CCI lesion, proximal superficial and distal superficial layers compared with deep cortical sham layers. The area adjacent to the injury was significantly decreased compared with proximal and distal superficial layers, ***P < 0.001 compared with sham, ##P < 0.01 compared with proximal and distal regions, one-way ANOVA, bars represent mean + SEM, n = 3 sham, 4 CCI animals; 22 sham, 40 CCI sections. (G) Quantification of SST+ cells per 10 000 μm2 in CCI injured deep cortical subregions showing a significant decrease in cell number in the area adjacent to the CCI lesion, proximal deep and distal deep layers compared with deep sham layers. (H) Quantification of SST+ cells per 10 000 μm2 in CCI injured superficial cortical subregions showing a decrease in cell number in the area adjacent to the CCI lesion, proximal superficial, and distal superficial layers compared with deep cortical sham layers, **P < 0.01, ***P < 0.001 compared with sham, one-way ANOVA, bars represent mean + SEM, n = 3 animals, 16 sections.

Activity-dependent Regulation of Glutamate Uptake

We recently reported that neuronal activity alters the rate of astrocytic glutamate uptake with spatial and temporal specificity. This suggests that astrocytes may be able to control glutamate signaling in a new way, with synapse specificity. If correct, this would have significant implications on how we view neuron-astrocyte interactions. We have now begun to study the mechanisms behind this modulation. We suspect that rapid regional changes in astrocyte voltage drive this important, novel type of synaptic plasticity. We are developing novel imaging approaches to understand these changes and dive deeper into the ions and molecules at play.

Figure 3. Stimulus-dependent slowing of glutamate clearance. (A), Average traces of GFAP-iGluSnFr with responses to 1, 2, 3, 4, 5, and 10 stimuli at 100 Hz, normalized to 1 stim response, show a stimulus-dependent slowing of glutamate clearance. (B), Exponential decay time constants of glutamate clearance; N = 7 slices. (C), The 10th stimulus in a 100 Hz train is isolated by subtracting the previous 9 stimuli and compared with a single stimulus. (D), The isolated 10th stimulus shows significant slowing compared with a single stimulus; N = 17 slices. (E), Average glutamate transporter currents recorded from whole-cell patch-clamped astrocytes show similar stimulus-dependent slowing of glutamate clearance for 1 and 10 stimuli at 100 Hz. (F), Exponential decay time constants of glutamate transporter currents; N = 6 cells. (G), The 10th stimulus in a 100 Hz train of GTCs isolated by subtracting the previous 9 stimuli and compared with a single stimulus. (H), The isolated 10th stimulus is significantly slowed compared with a single stimulus; N = 7 cells. Statistical tests: paired t tests. *α = 0.05 (Holm–Bonferroni multiple-comparison correction). **α = 0.01 (Holm–Bonferroni multiple-comparison correction). ***α = 0.001 (Holm–Bonferroni multiple-comparison correction).