AMPK preferentially depresses retrograde transport of axonal mitochondria during localised nutrient deprivation

Mitochondrial clusters are found at regions of high energy demand, allowing cells to meet local metabolic requirements while maintaining neuronal homeostasis. AMP-activated protein kinase (AMPK), a key energy stress sensor, responds to increases in AMP/ATP ratio by activating multiple signalling cascades to overcome the energetic deficiency. In many neurological conditions, the distal axon experiences energetic stress independent of the soma. Here, we used microfluidic devices to physically isolate these two neuronal structures and to investigate whether localised AMPK signalling influenced axonal mitochondrial transport. Nucleofection of primary cortical neurons, derived from E16 mouse embryos (both sexes), with mito-GFP allowed monitoring of the transport dynamics of mitochondria within the axon, by confocal microscopy.Pharmacological activation of AMPK at the distal axon (0.1 mM AICAR) induced a depression of the mean frequency, velocity and distance of retrograde mitochondrial transport in the adjacent axon. Anterograde mitochondrial transport was less sensitive to local AMPK stimulus, with the imbalance of bi-directional mitochondrial transport resulting in accumulation of mitochondria at the region of energetic stress signal. Mitochondria in the axon-rich white matter of the brain rely heavily on lactate as a substrate for ATP synthesis. Interestingly, localised inhibition of lactate uptake (10 nM AR-C155858) reduced mitochondrial transport in the adjacent axon in all parameters measured, similar to that observed by AICAR treatment. Co-addition of compound C restored all parameters measured to baseline levels, confirming the involvement of AMPK. This study highlights a role of AMPK signalling in the depression of axonal mitochondrial mobility during localised energetic stress.</p

The computational model correctly predicted ATP, AMPK activity and glucose dynamics measured in neurons exposed to glutamate.

<p>(A) Model schematic. State variables are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.t001" target="_blank">Table 1</a>, and reaction numbers correspond to those listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.t002" target="_blank">Table 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.s002" target="_blank">S1 Table</a>. (B-E) Model input and simulations (solid black lines) overlaid on the median and inter-quartile regions (dotted black line, grey shaded area) of previously published fluorescence measurements in single cerebellar granule neurons exposed to glutamate for 10 min [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.ref015" target="_blank">15</a>]. The time of stimulus (model input or glutamate exposure) is marked with a light grey bar. Values were normalised to baseline. (B) A transient (10 min) increase in cytosolic calcium was applied as model input (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#sec008" target="_blank">Methods</a> and d[Cac]/dt equation in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.s002" target="_blank">S1 Table</a>), and fitted to fluorescence measurements of cytosolic calcium (Fluo-4 AM) in CGNs exposed to glutamate. (C) The simulated ATP dynamics closely aligned with experimental measurements of intracellular ATP concentration [ATeam is a fluorescent reporter of intracellular ATP concentration; [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.ref016" target="_blank">16</a>]]. (D) The simulated transient activation of AMPK resembled experimental measurements of AMPK activity [AMPKAR is a fluorescent reporter of AMPK activity [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.ref017" target="_blank">17</a>]]. (E) The model also correctly predicted a prolonged elevation of intracellular glucose and its delayed recovery [Glucose-FRET is a fluorescent reporter of intracellular glucose concentration [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.ref018" target="_blank">18</a>]].</p

Parameter constraints determined from steady-state (ss) analysis.

<p>Parameter constraints determined from steady-state (ss) analysis.</p

Multiple model simulations with varied parameter sets represented the experimental cell-to-cell heterogeneity.

<p>(A-E) Cell-to-cell heterogeneity was modelled by varying parameter values and input characteristics (magnitude and duration of the applied calcium influx) and performing multiple simulations. (A) The median and inter-quartile regions of all model inputs (solid black line within dark grey region) well resembled the median and inter-quartile regions (black dotted line within light grey region) of previously published fluorescent measurements of cytosolic calcium [Ca_c; [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.ref015" target="_blank">15</a>]]. (B-E) For each simulation, the metrics shown in (B-E) were calculated. The coloured data points link these simulations across the figures. The green data points were predicted with the parameter set as listed in Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.t001" target="_blank">1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.t002" target="_blank">2</a>. The parameter sets predicting the yellow, cyan and red data points are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.s003" target="_blank">S2 Table</a>. (B,C) The predicted variability in the (B) maximum fold change and (C) recovery duration of the model input (black box) closely matched experimental measurements (white box). Recovery duration was calculated as the time taken for the signal to recover to ±2% of baseline signal. (D) Box- and scatter-plots of the minimum ATP, maximum AMPK activity and maximum glucose fold changes during the excitotoxic stimulus, calculated from multiple model predictions and experimental measurements (Exp.) from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.ref015" target="_blank">15</a>]. (E) Box- and scatter-plots of the post-excitotoxicity recovery duration of the ATP, AMPK activity and glucose levels as calculated from model predictions and experimental measurements (Exp.) from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.ref015" target="_blank">15</a>].</p

Steady-state concentrations of modelled state variables.

<p>Steady-state concentrations of modelled state variables.</p

Sensitivity analysis indicated that glucose import dynamics are critical to the post-excitotoxic glucose recovery.

<p>(A-C) Parameters were varied by 0.5 (navy), 0.75 (light blue), 1 (green), 1.5 (orange) and 2 (red) times the steady-state values listed in Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.t001" target="_blank">1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.t002" target="_blank">2</a>, and the effect was calculated for the post-excitotoxic recovery duration of the (A) ATP, (B) AMPK activity and (C) Glucose signals. The varied parameter is written under each bar chart. The 5 parameters with the greatest effect on each metric are shown (left to right in order of effect), along with other parameters mentioned in the text. Data were omitted for parameter values at which the modelled state variables did not return to baseline within the simulation time (100 min). (D, E) Experimental traces (top panels) and multiple model simulations (bottom panels) of intracellular glucose concentration with either (D) glucose import or (E) AMPK inhibited prior to exposure to a transient excitotoxic stimulus. (D) Glucose import was inhibited by exposure to Cytochalasin B or by reduction of the modelled glucose import kinetics (Rx 9). (E) AMPK activity was inhibited by exposure to Compound C or by reduction of the modelled AMPK phosphorylation kinetics (Rx 6). Compound C experiments have been published previously [Fig 6A from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.ref015" target="_blank">15</a>]]. (F) Box- and scatter-plots of the glucose recovery duration with and without glucose import or AMPK inhibition (* ranksum p < 0.05).</p

The computational model can represent the bioenergetic collapse induced by severe excitotoxicity, modelling neuronal necrosis.

<p>We investigated the predicted responses following more severe excitotoxic stimuli by increasing the (A-C) duration or (D-F) magnitude of the applied calcium influx (model input). (A) Prolonging the Ca_c influx beyond 5 min did not predict further depletion of ATP beyond a minimal level (grey dots). In contrast, longer periods of Ca_c influx were predicted to speed up ATP recovery (black dots). The calcium influx for which the graphs in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.g001" target="_blank">Fig 1</a> were plotted is indicated with a black dashed line. (B) ATP was predicted to initiate recovery during longer periods of Ca_c influx. (C) The predicted ATP dynamics (black line) during a 60 min excitotoxic stimulus were validated with <i>de novo</i> fluorescent measurements of ATP concentration (ATeam) in CGNs exposed to glutamate for 60 min (grey lines) (D) More severe calcium influx was predicted to exacerbate ATP depletion (grey dots) and prolong its recovery (black dots). Behaviour consistent with neuronal necrosis was predicted for maximal values of calcium influx > 7 fold change, where the ATP concentration collapsed to ~0, and did not recover. (E) The model predicted characteristic switch-like behaviour in the ATP dynamics leading to necrotic energetic collapse on severe calcium influx. (F) At magnitudes of calcium influx that induced ATP collapse (lighter grey lines), the model also predicted altered calcium dynamics. (G) The steady-state concentrations of ATP and Ca_c well predicted the simulation outcome (viable/necrosis) following calcium influx. (H) The percentage of neurons predicted to undergo necrosis or to remain viable following an excitotoxic stimulus were similar to levels measured in populations of cortical neurons exposed to NMDA. Experimental data from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148326#pone.0148326.ref037" target="_blank">37</a>] Fig 1F, and the bar charts display mean ± SEM.</p

Reaction (Rx) equations and kinetic flux values for the modelled reaction network.

<p>Reaction (Rx) equations and kinetic flux values for the modelled reaction network.</p

Defining external factors that determine neuronal survival, apoptosis and necrosis during excitotoxic injury using a high content screening imaging platform

<div><p>Cell death induced by excessive glutamate receptor overactivation, excitotoxicity, has been implicated in several acute and chronic neurological disorders. While numerous studies have demonstrated the contribution of biochemically and genetically activated cell death pathways in excitotoxic injury, the factors mediating passive, excitotoxic necrosis are less thoroughly investigated. To address this question, we developed a high content screening (HCS) based assay to collect high volumes of quantitative cellular imaging data and elucidated the effects of intrinsic and external factors on excitotoxic necrosis and apoptosis. The analysis workflow consisted of robust nuclei segmentation, tracking and a classification algorithm, which enabled automated analysis of large amounts of data to identify and quantify viable, apoptotic and necrotic neuronal populations. We show that mouse cerebellar granule neurons plated at low or high density underwent significantly increased necrosis compared to neurons seeded at medium density. Increased extracellular Ca²⁺ sensitized neurons to glutamate-induced excitotoxicity, but surprisingly potentiated cell death mainly through apoptosis. We also demonstrate that inhibition of various cell death signaling pathways (including inhibition of calpain, PARP and AMPK activation) primarily reduced excitotoxic apoptosis. Excitotoxic necrosis instead increased with low extracellular glucose availability. Our study is the first of its kind to establish and implement a HCS based assay to investigate the contribution of external and intrinsic factors to excitotoxic apoptosis and necrosis.</p></div

Increased extracellular Ca²⁺ concentration sensitizes CGNs to excitotoxic insult and induces cell death mainly through apoptosis.

<p>CGNs were seeded at 50,000 cells/well and cultured <i>in vitro</i> for 7/8 days. CGNs were treated with different glutamate (Glut.) and Ca²⁺ concentrations as indicated for 10 min. A) The population (Popul.) of CGNs (%), for each glutamate and Ca²⁺ concentration, classified as viable (blue traces), apoptotic (green traces) or necrotic (red traces). Traces shown are median ± inter-quartile regions. Neurons exposed to high glutamate and high Ca²⁺ were more sensitive to cell death. B) A heatmap of median cell viability 24 h following glutamate exposure illustrates lower viability in CGNs exposed to increasing glutamate and extracellular Ca²⁺ concentrations. C) Populations of viable cells 24 h following glutamate excitation. Neurons exposed to 2.0 mM Ca²⁺ (white boxes) were more vulnerable to glutamate excitation. Boxplots show the median ± inter-quartile regions (n = 10 wells for each treatment, from 5 independent experiments). D) Populations of viable, apoptotic and necrotic neurons 24 h following exposure to 100μM or 300μM glutamate at 1.5 and 2.0 mM extracellular Ca²⁺. Neurons exposed to 300 μM glutamate and 2.0 mM Ca²⁺ show increased necrotic cell death. E) Heat map illustrating prolonged glutamate exposure induced cell death at 24 h post glutamate excitation. F) Distribution of viable apoptotic and necrotic neurons in response to prolonged glutamate excitation (100 μM /10 μM glycine) at 1h and 24 h post excitation. Quantification from 4 wells from 2 independent experiments. Boxplots show the median ± inter-quartile regions. Boxplot shows increase in apoptotic population in response to prolonged glutamate excitation. Total 8 wells from 4 independent experiments.</p