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Monitoring ATP dynamics in electrically active white matter tracts

  1. Andrea Trevisiol
  2. Aiman S Saab
  3. Ulrike Winkler
  4. Grit Marx
  5. Hiromi Imamura
  6. Wiebke Möbius
  7. Kathrin Kusch
  8. Klaus-Armin Nave  Is a corresponding author
  9. Johannes Hirrlinger  Is a corresponding author
  1. Max-Planck-Institute for Experimental Medicine, Germany
  2. University of Zurich, Switzerland
  3. University of Leipzig, Germany
  4. Kyoto University, Japan
  5. Center Nanoscale Microscopy and Molecular Physiology of the Brain, Germany
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Cite this article as: eLife 2017;6:e24241 doi: 10.7554/eLife.24241

Abstract

In several neurodegenerative diseases and myelin disorders, the degeneration profiles of myelinated axons are compatible with underlying energy deficits. However, it is presently impossible to measure selectively axonal ATP levels in the electrically active nervous system. We combined transgenic expression of an ATP-sensor in neurons of mice with confocal FRET imaging and electrophysiological recordings of acutely isolated optic nerves. This allowed us to monitor dynamic changes and activity-dependent axonal ATP homeostasis at the cellular level and in real time. We find that changes in ATP levels correlate well with compound action potentials. However, this correlation is disrupted when metabolism of lactate is inhibited, suggesting that axonal glycolysis products are not sufficient to maintain mitochondrial energy metabolism of electrically active axons. The combined monitoring of cellular ATP and electrical activity is a novel tool to study neuronal and glial energy metabolism in normal physiology and in models of neurodegenerative disorders.

https://doi.org/10.7554/eLife.24241.001

eLife digest

The brain contains an intricate network of nerve cells that receive, process, send and store information. This information travels as electrical impulses along a long, thin part of each nerve cell known as the nerve fiber or axon. The act of sending these electrical signals requires a lot of energy, and energy in cells is most often stored within molecules of adenosine triphosphate (called ATP for short).

Importantly, a better understanding of how the production and consumption of ATP in nerve cells relates to electrical activity would help scientists to better understand how a shortage of energy in the brain contributes to diseases like multiple sclerosis. However, to date, it has been challenging to study the dynamics of ATP in nerve cells that are active.

Now, Trevisiol et al. describe a new system that allows changes in ATP levels to be seen within active nerve cells. First, mice were genetically engineered to produce a molecule that works like an ATP sensor only in their nerve cells. This made it possible to visualize the amount of ATP inside the axons in real-time using a microscope. Measuring ATP levels and recording the electrical signals moving along an axon at the same time allowed Trevisiol et al. to see how ATP content and electrical activity correlate and regulate each other.

The experiments reveal that strong electrical activity reduces the ATP content of the axon. Trevisiol et al. also discovered that nerve cells are unable to generate enough energy on their own to sustain their electrical activity. These results provide evidence that other cells in the brain – most likely non-nerve cells called oligodendrocytes – play an active role in delivering energy-rich substances to the axons of nerve cells.

In the future, the same tools and approaches could be used to monitor ATP levels and electrical activity in mice that model neurological disorders. Such experiments could tell scientists more about how disturbing energy production in nerve cells affects these diseases.

https://doi.org/10.7554/eLife.24241.002

Introduction

In the vertebrate nervous system, long axons have emerged as the 'bottle neck' of neuronal integrity (Coleman and Freeman, 2010; Nave, 2010; Hill et al., 2016). In neurodegenerative diseases, axons often reveal first signs of perturbation, e.g. spheroids, long before the pathological hallmarks of disease are seen in neuronal somata, such as large protein aggregates or neuronal cell death. In white matter tracts, Wallerian degeneration of axons is a carefully regulated program of self-destruction that upon reaching a threshold leads to rapid calcium-dependent proteolysis of axonal proteins. This degeneration can be experimentally triggered in different ways, including acute axonal dissection or by blocking mitochondrial energy metabolism (Coleman and Freeman, 2010; Hill et al., 2016). In disease situations, the mechanisms leading to axon loss are much less clear. For example, in spastic paraplegia (SPG), which comprises an entire family of inherited axon degeneration disorders, long myelinated tracts of the spinal cord are progressively lost. However, virtually none of the subforms shows a clear link between the primary defect and the later loss of myelinated axons, with the possible exception of mitochondrial disorders, in which the perturbation of axonal energy metabolism is assumed to underlie a loss of axonal ATP (Ferreirinha et al., 2004; Tarrade et al., 2006). However, a disturbed energy balance has never been shown. Similarly, in the equally heterogeneous group of Charcot-Marie-Tooth (CMT) neuropathies of the peripheral nervous system only a small subset is caused by mitochondrial dysfunction. In most patients (CMT1A) the primary defect resides in the axon-associated myelinating glia (Schwann cells), and it is unknown whether the axonal energy metabolism is perturbed before the onset of axon degeneration.

Axonal ATP consumption in white matter tracts largely depends on the electric spiking activity that differs between regions, and it is virtually impossible to biochemically determine ATP selectively in the axonal compartment and under 'working conditions' with a high temporal and spatial resolution. To solve this problem, we have developed a novel approach to simultaneously study action potential propagation and ATP levels in axons of optic nerves of mice, a white matter tract suitable for studying axonal energy metabolism (Stys et al., 1991; Saab et al., 2016). We established the transgenic mouse line ThyAT, in which the fluorescent ATP-sensor ATeam1.03YEMK (Imamura et al., 2009) shows pan-neuronal expression in vivo. Stimulating electrically optic nerves acutely isolated from those mice, we combined recordings of compound action potentials (CAP) with confocal imaging of the ATP-sensor to evaluate and compare both axonal conductivity and ATP levels. We find that (1) axonal glycolysis is not sufficient to robustly sustain CAPs and physiological ATP levels, but mitochondrial function is needed to provide ATP; (2) during high-frequency propagation of action potentials, CAPs are a well-suited parameter to estimate axonal ATP levels; (3) lactate metabolism is essential for the maintenance of axonal ATP levels, and its inhibition severely affects energy homeostasis of myelinated axons.

Results

ATP imaging in optic nerves

The genetically encoded ATP-sensor ATeam1.03YEMK allows FRET-based monitoring of cellular ATP levels close to real-time (Imamura et al., 2009). We generated transgenic mice for pan-neuronal in vivo expression of this sensor driven by the murine Thy1.2 promoter (Caroni, 1997). One mouse line with 21 copies of the transgene [B6-Tg(Thy1.2-ATeam1.03YEMK)AJhi, referred to as ThyAT] displayed widespread neuronal expression (Figure 1). In the retina, fluorescence marked ganglion cells and their axons (Figure 1G,H) and expression of the ATP-sensor in myelinated axons appeared robust in all recorded channels (Figure 1I–K).

Characterization of the expression pattern of the newly generated B6-Tg(Thy1.2-ATeam1.03YEMK)AJhi (ThyAT)-mouse line.

(A) Sagittal section of the brain highlights broad ATeam1.03YEMK expression in neurons in almost all brain regions with the exception of the olfactory bulb. Scale bar: 1 mm. (B,C) ThyAT expression pattern in coronal brain sections revealing sensor expression e.g. in thalamus, hypothalamus, amygdala, cortex and hippocampus. Scale bar: 1 mm. Abbreviations used in panels A–C are: AV: arbor vitae; BLA: basolateral amygdalar nucleus, anterior; CA1: CA1 region of the hippocampus; cc: corpus callosum; Cf: columns of the fornix; CN: cerebellar nuclei; Cp: cerebral peduncle; CPu: caudate putamen; Fi: fimbria; IC: inferior colliculus; ic: internal capsule; LH: lateral area of the hypothalamus; M: medulla; opt: optic tract; P: pons; S: subiculum; SC: superior colliculus; SMA: somato-motor area (cortex); SN: substantia nigra; st: stria terminalis; T: thalamus; Zi: zona incerta (thalamus). (D) Within the cortex, neurons expressing ATeam1.03YEMK are clearly visible including their processes. Note the lack of ATP-sensor localization to the nucleus. Scale bar: 100 μm. (E) Also in the hippocampus neurons strongly express ATeam1.03YEMK. Scale bar: 100 μm. (F) In the cerebellum, Purkinje cells express the ATP-sensor. In addition, incoming mossy fibers strongly express ATeam1.03YEMK. Scale bar: 100 μm. Images in panels A, D–F are obtained on brain slices from a four month old animal, images in panels B and C are from mice at the age of two month. (G) Expression pattern of the ATP-sensor in the retina. Thy1.2 promoter drives the expression of ATeam1.03YEMK in ganglion cells. Scale bar: 1 mm. (H) Magnified view of neurons and axons in the retina expressing ATeam1.03YEMK. Scale bar: 100 μm. (I–K) Representative images of optic nerve axons showing the YFP channel (I), FRET channel (J) and CFP channel (K). The ATeam1.03YEMK expression is present in different axons independent of their diameter. Scale bar: 10 μm.

https://doi.org/10.7554/eLife.24241.003

Optic nerves from adult ThyAT mice were studied ex vivo, assessing axonal ATP levels by confocal microscopy and simultaneously monitoring stimulus-evoked CAPs (Figure 2). To verify sensor function, nerves were subjected to ATP depletion by blocking mitochondrial respiration with sodium azide (MB) and glucose deprivation (GD). An immediate drop of the FRET signal (Figure 2C,D) and increase in CFP emission (Figure 2C,D) indicated that ATP levels in axons dropped. Changes in pH can modulate YFP-fluorescence (Nagai et al., 2004; Zhao et al., 2011). However, ATeam1.03YEMK is almost insensitive to pH within the physiological range (Imamura et al., 2009; Surin et al., 2014) and YFP emission upon direct YFP excitation was unchanged (Figure 2C,D; and data not shown), suggesting that pH changes are not the cause of altered FRET signals.

Imaging of ATP combined with electrophysiology in acutely isolated optic nerves of ThyAT-mice.

(A) Binding of ATP induces a conformational change in the genetically encoded ATP-sensor ATeam1.03YEMK thus increasing the FRET effect (YFP emission upon CFP excitation) and simultaneous decreased emission of CFP (upon CFP excitation). The ratio between FRET and CFP can thus be correlated with the concentration of ATP present in the cell. (B) Schematic representation of the set-up to acquire evoked CAPs in the optic nerve and to simultaneously investigate relative ATP levels by electrophysiology and confocal imaging, respectively. (C) Time course of fluorescence intensity recorded in the YFP, FRET and CFP- channels during application of mitochondrial blockage (MB) and glucose deprivation (GD) for 2.5 min. Values are normalized to YFP intensity prior to application of MB+GD (n = 3 nerves). Time resolution: 10.4 s. (D) The combination of MB and GD is a fast and reliable way to deplete ATP in axons of the optic nerve. ATP depletion is measured as a decrease in FRET and increase in CFP, calculated as ratio between fluorophore intensity during MB+GD, over fluorophore intensity at baseline condition (MB+GD/Baseline). Notably, YFP emission upon YFP excitation remains unchanged (n = 5 nerves). (E) Ratiometric images displaying the FRET/CFP ratio of the ATeam1.03YEMK–sensor in the axons of the optic nerve during ATP depletion following MB+GD. The phases before (Baseline) and after (Recovery) are also shown. Scale bar: 10 μm. (F) FRET/CFP ratio values (not normalized) during baseline and MB+GD. The boxplots show summarized data of n = 19 nerves, lines in between boxplots show changes in the FRET/CFP ratio of all 19 individual nerves (***p<0.001). (G) To assess ATP variations, the ratio of the fluorescence intensities of the FRET and CFP-channel was calculated (FRET/CFP ratio) and normalized to baseline (set as 1) and MB+GD (set as 0). The red dashed line visualizes the slope of ATP drop at the point of maximal velocity of ATP decay during mitochondrial blockage (MB+GD, n = 3). (H) Recording of the evoked compound action potential (CAP), given as the normalized curve integral during mitochondrial blockage and glucose deprivation (MB+GD). The black dashed line represents the slope at the point of maximal velocity of CAP changes during MB+GD treatment (n = 3). Individual CAP traces are shown in Figure 3—figure supplement 1. (I) Stripe plot describing CAP (black) and ATP (red) kinetics, expressed as maximal variation per s, during MB+GD (p=0.39, n = 3, Welch’s t-test). Dots show individual data points, bars and lines represent the mean of all data.

https://doi.org/10.7554/eLife.24241.004

The ratio of FRET/CFP fluorescence (F/C-ratio) was calculated as a measure for the cytosolic ATP concentration (Imamura et al., 2009; Figure 2E,F). The baseline F/C-ratio (control condition; 10 mM glucose) was similar and stable between different nerves analyzed (Figure 2F), while after 2 min of MB+GD the F/C-ratio was reduced by 35.1 ± 1.7% (p<0.001; n = 19 nerves; Figure 2F). In further experiments, F/C-ratios were normalized between one (10 mM glucose) and zero (MB+GD; Figure 2G). As expected, during MB+GD the decrease in axonal ATP was mirrored by a decay of CAPs, reflecting conduction blocks (Figure 2H). Strikingly, also the decline rates of axonal ATP and conductivity were similar (Figure 2I).

Kinetics of ATP decline during energy deprivation

Next, we compared ATP and CAP dynamics in response to different modes of energy deprivation (Figure 3; Figure 3—figure supplement 1), including glucose deprivation (GD), blockage of mitochondrial respiration by azide (MB), and a combination of both (MB+GD). During GD both ATP and CAPs decayed after 13 min (Figure 3A). Inhibition of respiration resulted in a drop after about 3 min with a steeper decline (Figure 3B). MB+GD induced an immediate and fast decline of ATP, followed by an equally fast decline in CAPs (Figure 3C–E). To reduce permanent damage and to study recovery, MB and MB+GD were limited to 5 min. The observed change of the ATP sensor signal is most likely not caused by major changes in pH as YFP fluorescence remained almost unchanged under all conditions of energy deprivation (Figure 2D and Figure 3—figure supplement 2). These data suggest that optic nerves continue ATP production under GD, presumably by metabolizing glycogen of astrocytes (Brown et al., 2005). However, axonal ATP production strongly depends on mitochondrial respiration, and axonal glycolysis alone is insufficient to maintain ATP levels, even in the virtual absence of electrical activity (baseline recording conditions with 0.033 Hz stimulation frequency).

Figure 3 with 2 supplements see all
Impairment of axonal ATP and CAP by glucose deprivation and/or inhibition of mitochondrial respiration.

(A) Removal of glucose from the aCSF (glucose deprivation, GD, 45 min) induces similar ATP (red) and CAP (black) decays starting at around 13 min after onset of the treatment. Red and black dashed, straight lines represent the maximum velocity of ATP and CAP decay (also applies to panels B and C). When 10 mM glucose is restored, CAP recovery precedes ATP restoration (n = 4 nerves). (B) Blockade of mitochondrial respiration by azide (MB, 5 min) produces a fast decay in ATP (red) and CAP (black) starting at 1.8 min after beginning of treatment. When azide is removed, CAP and ATP are promptly restored, with CAP recovery preceding the ATP increase (n = 4 nerves). (C) Simultaneous removal of glucose and blockade of mitochondrial respiration with azide (MB+GD, 5 min) produces a fast decay in ATP (red) preceding CAP decay (black), starting already 0.5 min after onset of treatment. Following azide removal and replenishment of glucose, CAP and ATP are restored (n = 4 nerves). (D) Time of onset of the ATP or CAP decay. The slowest decay induction was observed during glucose deprivation. (E) Velocity of signal decay for ATP and CAP during each of the three treatments: glucose deprivation (GD), mitochondrial blockage (MB) and the combination of both (MB+GD). (F) Time of onset of ATP or CAP recovery after reperfusion with control aCSF containing 10 mM glucose. (G) Rate of recovery of both ATP and CAP during reperfusion of the nerves with aCSF containing 10 mM glucose after the treatments indicated. (H) Comparison of ATP and CAP area overall recovery after individual treatments. Data in D–H is presented as stripe plots, with dots representing individual data points, bars and lines showing the mean. Hash signs indicate statistically significant differences between ATP and CAP under the same condition (#p<0.05, ##p<0.01, paired t-test); asterisks on red (ATP) and black (CAP) lines indicate statistically significant differences between different conditions (*p<0.05, **p<0.01, ***p<0.001; one-way ANOVA with Newman-Keuls post-hoc test).

https://doi.org/10.7554/eLife.24241.006

When nerves were reperfused (aCSF, 10 mM glucose) after GD, CAPs recovered with a delay of about 3 min, and the recovery of CAPs preceded the visible recovery of ATP levels by several minutes (Figure 3A,F). Interestingly, following MB+GD, CAPs recovered significantly sooner, concomitantly with axonal ATP, and faster (Figure 3F,G). We presume that, under MB+GD, lactate/pyruvate was not depleted from the optic nerve and is readily available for ATP generation by mitochondria after cessation of MB+GD.

Electrical activity during high frequency stimulation depletes ATP

Maintaining axonal conductivity ex vivo depends on energy-rich substrates and high- frequency stimulation of axons causes CAPs to decrease (Brown et al., 2001; Tekkök et al., 2005). However, it has never been possible to study the reverse, i.e. the impact of electrical activity and spiking frequency on axonal ATP levels. In ThyAT optic nerves incubated in aCSF containing 10 mM glucose and subjected to electrical stimulation at 16 Hz, 50 Hz or 100 Hz for 2.5 min, CAPs dropped in a stimulation frequency dependent manner (16 Hz: 94.9 ± 1.5%; 50 Hz: 79.6 ± 0.4%; 100 Hz: 66.4 ± 1.0%; n = 5 nerves; Figure 4A,C, Figure 4—figure supplement 1). At the same time, axonal ATP levels decreased as stimulation frequencies increased (16 Hz: 91.6 ± 1.0%; 50 Hz: 82.3 ± 1.4%; 100 Hz: 68.9 ± 2.8%; n = 5 nerves; Figure 4B,D) indicating higher axonal ATP consumption. Even maximal ATP changes and maximal loss of conductivity correlated over stimulation frequencies (Figure 4E,F). Similar results were obtained with a different stimulation paradigm with continuously increasing frequencies (Figure 4—figure supplement 2); however, the causal relations of the correlation of CAP and ATP changes are most likely more complex (see discussion below). Finally, both the rates of ATP drop and recovery correlated with the amplitude of ATP changes (Figure 4—figure supplement 3).

Figure 4 with 5 supplements see all
Comparison of ATP and CAP dynamics during high frequency stimulation.

(A) The CAP area decreases over time during high-frequency stimulation (HFS). The decay amplitude deviates from the absence of HFS, indicated by the dashed line (0.1 Hz, used for normalization to 1.0), and increases progressively with the increase in stimulation frequency (16 Hz, 50 Hz, 100 Hz). Traces from one representative nerve incubated in aCSF containing 10 mM glucose are shown. (B) Axonal ATP levels also decrease with increasing stimulation frequency, reaching a new steady state level which depends on the stimulation frequency. Same experiment as in panel A. (C) Remaining CAP area at the end of the HFS (overall decay amplitude) during incubation of nerves in different glucose concentrations quantified during the last 30 s of HFS. The stripe plot shows summarized data from n = 5, 5, or 4 nerves for 10 mM, 3.3 mM and 2 mM glucose, respectively. The dashed line at 1 shows CAP size at 0.1 Hz stimulation frequency, which was used for normalization. (D) Quantification of ATP decay amplitude during incubation of the same nerves as in (C) in different glucose concentrations. The dashed line at 1 shows ATP levels at 0.1 Hz stimulation frequency. (E) Correlation of the amplitude of ATP and CAP decay during HFS of nerves bathed in aCSF containing the glucose concentrations indicated. Data points are very close to the diagonal of the graph indicating that ATP and CAP change by similar factors. (F) Ratio of ATP and CAP drop during HFS in the presence of glucose in the concentrations indicated. If both parameters change by the same factor, this ratio remains equal to one. Data in (C–D) is presented as stripe plots, with dots representing individual data points and bars and lines showing the mean. Asterisks indicate statistically significant differences between glucose concentrations (*p<0.05, ***p<0.001; Welch’s t-test).

https://doi.org/10.7554/eLife.24241.010

ATP homeostasis of myelinated axons depend on lactate metabolism

Next, we asked whether lower (physiological) glucose concentrations have an impact on nerve conduction and axonal ATP levels when nerves were challenged with different spiking frequencies (Figure 4C,D; Figure 4—figure supplement 4). In presence of 10 mM and 3.3 mM glucose, CAP performance and ATP levels dropped similarly with increasing stimulation frequencies. However, when only 2 mM glucose was applied both CAP and ATP levels were substantially decreased (Figure 4C,D) and the ATP/CAP ratio was more variable at 2 mM glucose (Figure 4F). Analysis of the fluorescence intensity of single channels indicated that the observed changes of the ATP sensor signal are indeed caused by ATP changes because YFP fluorescence remained rather unaffected (Figure 4—figure supplement 5). Taken together, these data suggest that 2 mM glucose is below the threshold required for maintaining the axonal energy balance of the optic nerve in the ex vivo experimental setting.

At high-frequency stimulation and under aerobic conditions, optic nerve CAPs were maintained equally well with either 10 mM glucose, 10 mM lactate or 10 mM pyruvate as extracellular energy substrate (Figure 5A), in agreement with earlier findings (Brown et al., 2001; Tekkök et al., 2005) presumably because absolute axonal ATP consumption is unaffected by the type of metabolic support. However, at 100 Hz the relative decrease in axonal ATP levels was larger with lactate (or pyruvate) than with glucose (glucose: 68.9 ± 2.8%; lactate: 56.3 ± 2.8%; pyruvate: 47.4 ± 2.8%; p=0.05 and p=0.01, respectively; n = 3 nerves; Figure 5B, Figure 5—figure supplement 1), showing that 10 mM glucose maintains axonal ATP production better than 10 mM lactate or pyruvate if applied exogenously.

Figure 5 with 2 supplements see all
Energy metabolism of optic nerves depends on the type and concentration of substrates and involves lactate metabolism.

(A) The comparison between CAP area decay of optic nerves incubated in 10 mM glucose aCSF (n = 5 nerves) versus optic nerves incubated either in 10 mM lactate or 10 mM pyruvate (n = 3 nerves) during HFS, shows no significant differences among the three substrates (p>0.05, Welch’s t test). (B) In contrast, analysis of axonal ATP levels shows that at higher frequencies glucose is a better substrate to maintain axonal ATP levels. Same experiments as in panel A. (C) In the presence of glucose (3.3 mM; n = 5 nerves) as exogenous energy substrate, inhibition of lactate metabolism by D-lactate (20 mM; competitive inhibitor of endogenous L-lactate metabolism at MCTs and LDH, n = 6) or AR-C155858 (10 µM; MCT1 and MCT2 selective inhibitor, n = 6) does not significantly affect CAPs. (D) Analysis of ATP under the same conditions as in (C): ATP levels undergo a strong decrease at higher frequencies in the presence of D-lactate or AR-C155858. (n = 5 nerves for all conditions). The dashed lines in panels A–D at 1 show CAP size or ATP levels at 0.1 Hz stimulation frequency used for normalization. (E) Inhibition of metabolism of endogenously produced L-lactate in the presence of glucose as the sole exogenous energy substrate shifts the correlation of ATP and CAP to the upper left showing that ATP changes more strongly than CAP. (F) The ratio of ATP and CAP drop decreases significantly in the presence of inhibitors of lactate metabolism, confirming that ATP changes more strongly than CAP. Asterisks in (A–D and F) indicate significant differences among conditions: *p<0.05, **p<0.01, ***p<0.001; Welch’s t test.

https://doi.org/10.7554/eLife.24241.017

Do axons require pyruvate/lactate metabolism also in the presence of glucose as energy substrate? To address this question, we studied optic nerves conduction and ATP levels in 3.3 mM glucose plus 20 mM D-lactate, which competitively inhibits transport through MCTs as well as lactate dehydrogenase (LDH; D-lactate is a stereoisomer of L-lactate, which is the physiological molecule in energy metabolism). D-lactate did not affect CAPs (Figure 5C, Figure 5—figure supplement 2) but had a strong effect on axonal ATP (at 100 Hz: Glc: 59.7 ± 2.9%; Glc+D-Lac: 29.2 ± 2.2%; p=0.0001; n = 5 nerves; Figure 5D). Similarly, when MCT1/MCT2-mediated lactate transport was inhibited using the specific inhibitor AR-C155858 (Ovens et al., 2010), relative ATP levels were strongly reduced (at 100 Hz: 24.8 ± 4.1%; p=0.0004; n = 5 nerves), but with no effect on CAP performance (at 100 Hz: Glc: 69.5 ± 4.2%; Glc+AR-C155858: 62.8 ± 3.3%; p=0.37; n = 6 nerves; Figure 5C,D; Figure 5—figure supplement 2), indicating the presence of a ‘safety window’. When lactate metabolism was impaired, ATP/CAP ratios strongly deviated from the normal ‘1:1 ratio’ observed in the presence of glucose alone (Figure 5E,F). Collectively, these results strengthen the conclusion that pyruvate/lactate metabolism is required to maintain ATP levels in fast spiking axons.

Discussion

The axonal energy balance is determined by the equilibrium of ATP consumption and ATP generation, which is a function of axonal spiking frequency and metabolic support, the latter provided in part by glycolytic oligodendrocytes (Fünfschilling et al., 2012; Lee et al., 2012; Saab et al., 2016). Here, we have generated transgenic mice expressing an ATP-sensor in neurons and in the axonal compartment of myelinated nerves. These mice allowed us to study for the first time the interrelationship of electrical activity and axonal ATP homeostasis in real time by adapting an established optic nerve model of white matter electrophysiology (Brown et al., 2001; Brown and Ransom, 2007). Specifically, we show that both CAPs and axonal ATP content decrease in a stimulation frequency-dependent manner, and that even in the presence of glucose as (exogenous) energy substrate, lactate contributes to ATP homeostasis. We also analyzed short-term adaptations of metabolism in a model of ischemic stroke when ATP demands are increased, i.e. the recovery phase from transient glucose deprivation and mitochondrial inhibition.

While being a powerful experimental system, some caveats need to be taken into account to interpret the obtained results. First, the signal of the ATeam1.03YEMK sensor is not linearly related to ATP over an extended range of concentrations (Imamura et al., 2009). However, in cultured neurons the cytosolic ATP concentration has been estimated at approximately 2 mM (Rangaraju et al., 2014; Pathak et al., 2015), i.e. within the linear range of ATeam1.03YEMK. We are thus confident that the observed changes in sensor signal reflect similar relative changes of axonal ATP. However, as calibration of the ATP-sensor signal to absolute cytosolic concentrations is difficult in the myelinated optic nerve, a precise estimation of the absolute concentration of axonal ATP awaits further technical advances. Nevertheless, changes of the ATP signal in the course of an experiment could be detected even after mild stimulations, which suggest that the basal axonal ATP level is within the dynamic range of the sensor with its KD of 1.2 mM (Imamura et al., 2009).

Secondly, with the spatial resolution of our approach, ATP signals reflect mean cytosolic concentrations in the axoplasm. It has been suggested that local differences of ATP consumption by Na+/K+-ATPase lead to ATP microdomains close to the cell membrane with low ATP concentrations (Haller et al., 2001; Toloe et al., 2014). Contrarily, ATP diffusion is reported to be very fast not allowing formation of ATP microdomains (Barros et al., 2013). While such highly localized changes of ATP would be missed in our experiments, they are unlikely to affect the principle findings.

Finally, CAPs recorded with supra-threshold stimulation reflect the summation of action potential propagation of virtually all axons within the optic nerve. In contrast, ATP imaging was performed on a single optical plane close to the surface of the nerve comprising typically around 30 axons. Therefore, the observed ATP signals are likely not representative for all axons, as the diffusion of substrates into the core of the nerve may be less efficient than to axons closer to the surface. Our attempts to address this issue by 2-photon laser scanning microscopy deeper within the nerve have failed so far, due to scattering occurring in a densely packed and fully myelinated optic nerve (ASS, unpublished observation). Taking such an inside-out gradient of increasing perfusion efficacy into account, our approach is presently the best possible approximation of the simultaneous monitoring of both parameters. Thus, we are not yet able to assign absolute ATP concentrations to physiological properties of spiking axons. However, we can readily (i) monitor ATP changes as a function of axonal spiking activity, (ii) compare metabolites, (iii) assess the role of disease-causing mutations on axonal conduction and energy metabolism, also under physiological challenge, and finally (iv) study selected drugs and nutrients in axonal energy metabolism to explore treatment strategies.

Monitoring axonal ATP homeostasis

Targeting the energy producing biochemical pathways by the withdrawal of glucose (GD), blocking mitochondrial respiration (MB), or both (MB+GD), rapidly reduced axonal ATP content, as expected. In GD, ATP fell after 13 min, consistent with the utilization of astroglial glycogen as a reserve fuel (Brown and Ransom, 2007). In contrast, MB caused an immediate drop in ATP. In all conditions, ATP decreases preceded or coincided with CAP decay, confirming that energy depletion causes axonal failure (but see caveat above). Interestingly, after GD in the subsequent recovery phase with glucose, the restoration of ATP levels appeared delayed compared to the recovery of conduction. We therefore suggest that at the beginning of reperfusion most newly generated ATP is consumed by Na+/K+-ATPases for reestablishing ion gradients and conduction before it accumulates in the axon and binds to the ATP-sensor.

Astrocytes can mobilize glycogen and generate lactate to support axonal energy homeostasis (Brown et al., 2001, 2003, 2005). However, it has been a long-standing debate whether neuronal compartments prefer glucose or lactate as energy-rich substrates (Pellerin and Magistretti, 2012; Dienel, 2012; Nave, 2010; Hirrlinger and Nave, 2014). In the optic nerve, glucose and pyruvate/lactate can sustain CAP propagation. We found that axonal ATP levels were significantly lower during HFS when nerves were incubated with lactate or pyruvate as the sole energy substrate. This suggests that lactate/pyruvate alone does not support ATP production as well as glucose and confirms observations in cultured cerebellar granule cells with repetitive activation of NMDA-receptors (Lange et al., 2015).

Nevertheless, lactate metabolism contributes to axonal energy homeostasis, even in the presence of glucose, as evidenced by blocking lactate metabolism with D-lactate or AR-C155858. In the presence of these inhibitors, ATP decreased more strongly than CAP (Figure 5E,F), suggesting the presence of a ‘safety window’. It also breaks the striking correlation of ATP and CAP observed under control conditions (Figures 4E,F and 5E,F). One possible explanation is that changes in CAP are related to ATP consumption, while inhibition of lactate transport affects ATP production. Since the axonal stimulation protocol is unchanged, also the consumption of ATP should be similar in the presence of inhibitors of lactate metabolism. Therefore, the inhibition of lactate metabolism impairs ATP production and causes a lower steady-state level of ATP, but the consumption rates during HFS remain the same.

Furthermore, this finding suggests that ‘endogenous’ lactate (i.e. produced from glucose) is more efficient than lactate provided in the medium. One possibility is that glucose enters the nerve more rapidly than lactate, because glucose transporter expression is optimized in comparison to MCT1. Moreover, glucose and glycolysis intermediates are readily shuttled via gap junctional coupling between astrocytes and oligodendrocytes, with lactate being generated also close to the periaxonal space, whereas exogenous lactate has a more complex path of diffusion involving mostly extracellular space (Hirrlinger and Nave, 2014). We note that from an evolutionary point of view, such optimization of glucose utilization is of advantage for the brain, because the liver always aims at generating a constant blood glucose level, even under starvation conditions.

Taken together, combining ATP imaging and electrophysiology, we demonstrate that metabolic imaging is a very sensitive analysis tool for real-time monitoring of axonal energy homeostasis and the underlying neuron-glia interactions in electrically active fiber tracts. Applied to models of diseases this technology will be able to detect subtle perturbations of axonal energy homeostasis and allow addressing the hypothesis that energy deficits are an early and most likely causal event for neurodegeneration.

Materials and methods

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Animals were treated in accordance with the German Protection of Animals Act (TSchG §4 Abs. 3), with the guidelines for the welfare of experimental animals issued by the European Communities Council Directive 2010/63/EU as well as the regulation of the institutional ‘Tierschutzkommission’ and the local authorities (T04/13, T20/16; Landesdirektion Leipzig, LAVES Niedersachsen).

Transgenic mice

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Animals were bred in the animal facility of the Max-Planck-Institute for Experimental Medicine as well as the Medical Faculty of the University of Leipzig. Mice were housed in a 12 hr/12 hr light dark cycle with access to food and water ad libitum. The transgene construct was assembled in pTSC (Hirrlinger et al., 2005) containing Thy1.2 promoter sequences. The plasmid pDR-GW AT1.03YEMK (Bermejo et al., 2010) containing the open reading frame of the ATP-sensor ATeam1.03YEMK (Imamura et al., 2009) was obtained from Wolf Frommer (via Addgene; plasmid #28004). The open reading frame of ATeam1.03YEMK was subcloned into the XhoI restriction site of pTSC using PCR. The linearized transgene was injected into fertilized mouse oocytes of the C57BL/6J mouse strain. Transgenic founders were identified using PCR-based genotyping on genomic DNA isolated from tail tips (primer 1: 5’-CGCTGAACTTGTGGCCGTTTACG-3’; primer 2: 5’-TCTGAGTGGCAAAGGACCTTAGG-3’). The mouse line has been registered at the RRID Portal (https://scicrunch.org/resources; RRID:MGI:5882597).

Determination of copy number

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Mouse genomic DNA was isolated from tail biopsies of four C57BL/6J and six ThyAT-mice with Invisorb Spin Tissue Mini Kit according to the manufacturer’s instructions (Stratec Biomedical, Birkenfeld, Germany). Short FAM-labeled hydrolysis probes (UPL) were used for qPCR reactions (Roche Diagnostics GmbH, Mannheim, Germany). Primers and the UPL-probe were designed by ProbeFinder version 2.50 for Mouse (Roche Diagnostics; Primer Thy1s: TGCCGGTGTGTTGAGCTA; Thy1as: TGGTCCTGTGTTCATTGCTG; UPL 60; amplicon 73 bp). The genomic sequence of Nrg1 was used to calibrate for the amount of DNA (Primer Nrg1s: GGCTATAATGCTAACACAGTCCAA; Nrg1as: AGTGGATCGTAACAACACTGTCA; UPL 38; amplicon 61 bp) as described (Besser et al., 2015). 10 to 25 ng of genomic DNA was subjected to qPCR amplification to measure the amount of Thy1.2 on a Light Cycler 480 system (Roche Diagnostics) according to the manufacturer’s instructions. The copy number of the Thy1.2-ATeam1.03YEMK transgene was calculated using the ΔΔCt method compared to wild type mice carrying only the two endogenous alleles of the Thy1 gene (Besser et al., 2015).

Analysis of the sensor expression pattern

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Adult mice were transcardially perfused with 4% formaldehyde solution (PFA, in phosphate buffered saline: 137 mM NaCl, 2.7 mM KCl, 8 mM Na2HPO4, 1.5 mM KH2PO4, pH 7.4) under deep anesthesia. The brain and the eyes were removed and post-fixed for 24 hr in the same fixative. 45 μm thick sections were cut on a vibratome (Leica VT1000 S, Nussloch, Germany) and slices were mounted directly after cutting with Vectashield embedding medium (Vectashield HardSet Mounting Medium, Vector Laboratories, Burlingame, CA, USA). From the eyes, retinal whole mounts were prepared.

For imaging of fixed brain slices and retina, confocal images were acquired on a LSM Olympus IX71 inverted microscope using an UPlanFL 10x/0.3 objective (Olympus, Hamburg, Germany) for overview images (Figure 1A–C,G) or an UApo/340 40x/1.35 oil objective (Olympus) for detailed images (Figure 1D–F,H), respectively. Microscopic images were acquired and processed using Olympus Software Fluoview v5.0. ATeam1.03YEMK sensor fluorescence was excited with a 488 nm argon laser and detected through a BA 510–540 nm emission filter (AHF Analysentechnik AG, Tübingen, Germany). For all images, a Kalman filter of two was used for denoising and images were acquired with 1024 × 1024 resolution (pixel size for overview images: 0.9 µm; pixel size for detail images: 0.35 µm). Z-stacks comprise 12–38 singles z-planes for overview images and 30–85 z-planes for detailed images, respectively, with distances between each z-plane of 2 µm (overview), 0.5 µm (Figure 1D–F) and 0.35 µm (Figure 1H). Single z-stacks were converted to maximum intensity projections (by using Fiji macro ‘Flattening V2f.ijm’; generously provided by Jens Eilers; Jens-Karl.Eilers@medizin.uni-leipzig.de) and for overview images maximum intensity projections of different positions (210 for Figure 1A; 154 for Figure 1B; 160 for Figure 1C; 49 for Figure 1G) were stitched by using Fiji software and a Fiji stitching plugin (Preibisch et al., 2009).

Optic nerve preparation and electrophysiological recordings

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For optic nerve experiments, mice were used in an age of 8 to 12 weeks. Optic nerves were excised from decapitated mice, placed into an interface perfusion chamber (Harvard Apparatus, Holliston, MA) and continuously superfused with artificial cerebrospinal fluid (aCSF). The perfusion chamber was continuously aerated by a humidified gas mixture of 95% O2/5% CO2 and experiments were performed at 37°C. Custom-made suction electrodes back-filled with aCSF were used for stimulation and recording as described (Stys et al., 1991; Saab et al., 2016). The stimulating electrode, connected to a battery (Stimulus Isolator 385; WPI, Berlin, Germany) delivered a supramaximal stimulus of 0.75 mA to the nerve evoking compound action potentials (CAP). The recording electrode was connected to an EPC9 amplifier (Heka Elektronik, Lambrecht/Pfalz, Germany). The signal was amplified 500 times, filtered at 30 kHz, and acquired at 20 kHz or 100 kHz. Before recording, optic nerves were equilibrated for at least 30 min in the chamber.

The CAP, elicited by the maximum stimulation of 0.75 mA, was recorded at baseline stimulation frequency at 0.1 Hz. During HFS a burst-stimulation was applied consisting of 100 stimuli at the given frequency (16, 50 or 100 Hz), separated by 460 ms, during which the CAP was recorded; HFS overall duration was 150 s, independent of the frequency.

Imaging

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Live imaging of optic nerves was performed using an up-right confocal laser scanning microscope (Zeiss LSM 510 META/NLO, Zeiss, Oberkochen, Germany) equipped with an Argon laser and a 63x objective (Zeiss 63x IR-Achroplan 0.9 W). The objective was immersed into the aCSF superfusing the optic nerve. Theoretical optical sections of 1.7 μm over a total scanned area of 66.7 μm x 66.7 μm (512 × 512 pixels) of the optic nerve were obtained every 10.4 s in three channels, referred as CFP (excitation 458 nm; emission 470–500 nm), FRET (Ex 458 nm; Em long pass 530 nm) and YFP (Ex 514 nm; Em long pass 530 nm).

Solutions

Optic nerves were superfused by aCSF containing (in mM): 124 NaCl, 3 KCl, 2 CaCl2, 2 MgSO4, 1.25 NaH2PO4, and 23 NaHCO3, which was continuously bubbled with carbogen (95% O2/5% CO2). This solution was substituted with the appropriate energy substrates as indicated for which the solution containing 10 mM glucose served as the standard solution in respect to pH and osmolarity. For glucose deprivation (GD), glucose was removed from the aCSF and substitute by sucrose (Merck Millipore, Darmstadt, Germany) to maintain the correct osmolarity. For mitochondrial blockage (MB), aCSF containing 10 mM glucose was supplemented with 5 mM sodium azide (Merck Millipore). For the combination of MB+GD glucose was substituted by sucrose and 5 mM sodium azide was added.

For HFS, aCSF was supplemented with different substrates. Glucose (Fluka BioChemika, Munich Germany) was used at three concentrations: 10 mM, 3.3 mM and 2 mM. L-lactate and pyruvate (Sigma-Adrich, Munich, Germany) were used at 10 mM. Inhibitors of lactate metabolism were added to a basal concentration of glucose of 3.3 mM: the MCT1 inhibitor AR-C155858 (Med Chem Express, Sollentuna, Sweden) was used at 10 μM; sodium D-lactate (Sigma-Aldrich) was used at 20 mM. All aCSF-based solutions were adjusted for the same pH, sodium concentration and osmolarity.

Data analysis

CAP

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Optic nerve function was monitored quantitatively as the area under the supramaximal CAP. The CAP area is proportional to the total number of excited axons and represents a convenient and reliable means of monitoring optic nerve axon function (Stys et al., 1991; Saab et al., 2016). CAP was expressed as area under the CAP wave form in a range of approximately 1.5 ms, defined by the beginning of the response (typically at 0.2 ms after the stimulus) and approximately the end of the second peak of the CAP wave form, thereby analyzing the first and second peak characteristic of the optic nerve evoked CAP. These two peaks are indicative for the large and medium-sized axons within the optic nerve, which are those that will mainly be resolved by confocal imaging of the ATP sensor expressed in axons. During high frequency action potential propagation, amplitudes of CAP peaks generally decrease, while peak latencies increase. The measured CAP area integrates these two functional changes of nerve conduction. It is, therefore, a parameter well suited to quantify overall functional changes of the optic nerve. Data were normalized to the mean obtained during the initial 15 min (defined as baseline, i.e. no experimental condition applied). Results from several nerves were pooled, averaged, and plotted against time.

ATP

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The relative amount of ATP in the optic nerve was calculated in two steps. Initially, the mean intensity of FRET and CFP channel were extracted from the imaging time-series (in Fiji) and the ratio F/C was calculated. All fibers visible in the field of view were included within the analysis without any prior selection. The F/C ratio was then normalized to zero using the F/C value obtained by application of MB+GD at the end of each experiment, i.e. by depleting all axons from ATP. Furthermore, F/C values obtained during baseline recordings in 10 mM glucose at the beginning of each experiment were set to one. In all experiments, YFP fluorescence upon direct excitation of YFP was analyzed in parallel. Importantly, YFP fluorescence intensity remained stable throughout the experiments suggesting that the observed changes of the F/C ratio are most likely not due to artificial modulation e.g. by changes in intra-axonal pH.

ATP and CAP parameters

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For GD, MB and MB+GD conditions, m coefficients for the slope-intercepts at the point of maximal change of the signal were used to calculate the CAP and ATP rate per second, during the initial decay and the recovery following reperfusion with aCSF containing 10 mM glucose. The time of the start of decay/recovery of the ATP and CAP curves were defined as the first time-point at which the slope-intercept of the signal was above a threshold value based on the standard deviation calculated at the baseline. For HFS analysis three parameters were considered for both CAP and ATP: (1) the overall amplitude obtained by averaging the data at the time points within the last 15 s of the stimulation (2) the rate of initial decay defined within the 45 s that followed the beginning of the stimulation (3) the rate of recovery defined within the 60 s that followed the end of the stimulation. Rates were calculated from the m coefficients for the slope-intercepts in the defined time-range.

Presentation of data

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All data are presented either as mean ± s.e.m. or as stripe plots showing all data points and the mean (line within the plot). The number of optic nerves analyzed for each condition is given as n. As for no condition both optic nerves of one animal were used, the number of nerves is equal to the number of animals analyzed for each condition. If not indicated otherwise in the figure legends, data were statistically evaluated using Welch’s t-test and assuming a normal distribution (*p<0.05; **p<0.01; ***p<0.001).

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    Sweet sixteen for ANLS
    1. L Pellerin
    2. PJ Magistretti
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    Journal of Cerebral Blood Flow & Metabolism 32:1152–1166.
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Decision letter

  1. Gary L Westbrook
    Reviewing Editor; Vollum Institute, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Monitoring ATP dynamics in electrically active white matter tracts" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Gary Westbrook as the Senior Editor and Reviewing Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Marc Freeman; Brian MacVicar; Peter Stys. The reviewers have discussed the reviews with one another and the Senior Editor has drafted this decision to help you prepare a revised submission.

Summary: All three reviewers thought that this was an important study and were enthusiastic about publication after appropriate revisions. The major issue was concerning the sample size on some of the experiments particularly the last set of data, and with some technical considerations raised by reviewers 2 and 3 that require your attention and potentially some additional data. This sample size issue is well-elaborated in the comments of reviewer 2 below. You may also want to consider whether the MCT experiments contribute sufficiently to the manuscript to warrant inclusion as described by reviewer 1. As the reviewer comments are rather straightforward and non-overlapping, the original text of their reviews is included below. Your revisions should modify the text of the revised manuscript so that readers of your paper that have similar concerns will be able to assess the data appropriately.

Reviewer #1:

This is a very important study that combines live imaging, a mouse model of disease, and metabolic experiments to explore some very basic and important questions in axonal biology. Nave and colleagues, for the first time to my knowledge, directly measure (simultaneously) compound action potentials (CAP) and axonal ATP levels in a dissected whole optic nerve preparation. They first generate what will be a very useful mouse for the field where an ATP sensor is expressed under control of the Thy1 promoter and identify lines that nicely label most neurons. They then track ATP levels and CAP after stimulation at different frequencies. In short, they provide a number of interesting observations that argue for a major role for ATP in axons in regulating the ability of neurons to fire. By performing glucose deprivation and blockade of mitochondrial function they explore the differences in how ATP levels fall, how that affects CAPs, and how quickly neurons recover the ability to fire CAPs. Through these experiments they answer many long standing questions and show: 1) axonal glycolysis is not enough to robustly sustain CAPs, mitochondrial function is key, 2) blocking mitochondrial function rapidly blocks CAPs, 3) glucose, pyruvate and lactate can all sustain CAP activity, and 4) blockade of MCT function or lactate transport only mildly affects CAP, but more strongly affects ATP. It is suprising how much ATP can be around, yet CAPs still fire. The final interesting, but not fully explained phenotype that is observed is the fact that PLP1 KO animals exhibit strong defects in ATP and CAPs, even in the presence of extracellular lactate or glucose. Why PLP1 KO animals are more sensitive than WT controls to high frequency stimulation is unclear. Finally, the MCT het KO experiments are not particularly informative. If easy, repeating the experiment in an oligo-specifc cKO situation would be very interesting. So, I'm not sure what that experiment really adds.

Overall the experiments are very well done, they establish an exciting new system to examine axonal ATP levels and CAPs in an intact nerve preparation, and they provide some of the most satisfying experiments I know of that address the sufficiency of glucose, lactate, and pyruvate for sustaining axonal ATP and CAPs.

Reviewer #2:

This study follows from an impressive series of discoveries over the past few years from this research team on the roles for oligodendrocytes in providing energy substrates to maintain axonal functions and survival. They have created a very impressive and clever animal model of disease with a novel strategy to detect ATP levels in neurons using a FRET sensor for ATP. This potentially provides the authors with a method to examine the changes in ATP levels and metabolically driven alterations in ATP at different times leading up to clear manifestations of the disease phenotype. The authors observed remarkable parallels between CAP and ATP that are very reassuring for the interpretation of their altered relationship in the disease model. The authors have also been able to test the impact of spiking frequencies on ATP levels as a parallel to several previous studies that have shown decreasing ATP levels reduces action potential firing in optic nerves. Again, the authors found striking correlations between the spike frequency, the decrease in ATP levels and the impact on compound action potentials as shown in Figure 4.

1) The authors show in Figure 3 that there is a striking correspondence between decreases in both CAP and ATP during high frequency stimulation. However, the relationship appears to break down when changes are compared in fairly high concentrations of D-lactate or in the MCT1/2 inhibitor AR-C155858. Why is this?

2) The pH dependence from the original Imamura PNAS paper is complex and varies depending on the ATP concentration. The excitation for FRET at 458 is a longer wavelength than the Imamura paper (435 nm). At 435 nm there is selective excitation of CFP but at 458 there is some excitation of YFP. Therefore, at this wavelength there is a minor component of YFP direct excitation contributing to the FRET signal. This is a potential worry that the authors could eliminate by testing the pH dependence using their system. The authors mention normalizing some of the results with changes in YFP fluorescence changes. However, this is not clearly explained in the Methods. Was this always done? Could changes in pH play a role in altering the FRET signals in the animals’ disease models at different times of development. I suspect not but the authors should include some discussion on these points.

3) The activity dependent changes in ATP signals and therefore ATP changes during high frequency spiking are very interesting. However, the results in Figure 6 are the critical data for concluding there are early changes in the disease model. There are small changes in the disease model in the ATP FRET signal at 50 Hz stim in glucose and in the lactate groups. However, these results are only from 3 nerves in each group (Figure 6D and F). This small number seems too low particularly as there is one quite divergent value in the 50 Hz lactate category. In addition the authors should include both the number of animals and the number of nerves (e.g. was more than one nerve used from one animal). The text in the Results section states there were 4 nerves in each set but there are only 3 points in the bar charts and the figure legends says 3 nerves.

4) Subsection “PLP loss in oligodendrocytes impairs axonal ATP homeostasis”, last paragraph: The authors hint there is a non-significant trend towards reduced ATP levels in the optic nerves from Slc16a1 gets but I don't see this and it is not significant. Therefore, I would conclude that there is no effect if it is not statistically significant. In the conclusions "Applied to a mouse model of X-linked spastic paraplegia or SPG-2 we were able to detect an effect of mutant oligodendrocytes on axonal energy metabolism and physiology several months before these animals develop widespread axonal loss and spastic paraplegia. Compared at a preclinical stage to wildtype nerves, ATP homeostasis is impaired in axons conducting at high frequency, but appears normal under resting conditions." The data has the minimum n values to state that ATP homeostasis is impaired. The authors should increase the number of experiments to provide more robust evidence. There may be variations in the handling etc. that may distort a few of the values in such a few experiments.

Reviewer #3:

This is a very interesting and elegant study of CNS axon energy metabolism with a focus on axonal ATP using using a novel axonally-expressed FRET-based fluorescent ATP reporter. Importantly, they shed important light on how genetic defects of myelin production paradoxically result in axonal degeneration.

1) "while after 2min of MB+GD the F/C-ratio was reduced by 35.1% ± 1.7% (n=19 121 nerves; Figure 2F)": p values and significance need to be cited.

2) Subsection “Data analysis” – CAP analysis: I agree with authors' choice of analyzing CAP area up to 1.5ms, which should include large and medium-sized axons, the ones that will be resolvable by confocal imaging. Authors should explain this point in Methods.

3) Figure 3: authors should include a supplementary figure showing representative CAP waveforms and how they decay with the various energy deprivation paradigms, which may differ.

4) Given points 2 and 3 above, raises a potential difficulty: consider Figure 4 for example, where CAP changes also include a significant lengthening of the waveform: by restricting the area analysis to 1.5ms, while still imaging ATP FRET signals in medium-sized axons by confocal, there may arise an artifactual disconnect between CAP area and axonal ATP levels, merely because CAP lengthening will move significant signal beyond their 1.5ms integration window. Authors should explain how they dealt with this potential limitation and how it may have affected their results.

5) Subsection “ATP homeostasis of myelinated axons depends on lactate metabolism”, last paragraph: "…plus 20mM D-lactate.…": authors should clarify that physiological lactate is the L isomer for readers who may not know.

6) Discussion, third paragraph: given their ATP FRET reporter, can the authors give a rough estimate of absolute ATP concentrations in axons? This would be an interesting piece of reference data for the field.

https://doi.org/10.7554/eLife.24241.021

Author response

Reviewer #1:

This is a very important study that combines live imaging, a mouse model of disease, and metabolic experiments to explore some very basic and important questions in axonal biology. Nave and colleagues, for the first time to my knowledge, directly measure (simultaneously) compound action potentials (CAP) and axonal ATP levels in a dissected whole optic nerve preparation. They first generate what will be a very useful mouse for the field where an ATP sensor is expressed under control of the Thy1 promoter and identify lines that nicely label most neurons. They then track ATP levels and CAP after stimulation at different frequencies. In short, they provide a number of interesting observations that argue for a major role for ATP in axons in regulating the ability of neurons to fire. By performing glucose deprivation and blockade of mitochondrial function they explore the differences in how ATP levels fall, how that affects CAPs, and how quickly neurons recover the ability to fire CAPs. Through these experiments they answer many long standing questions and show: 1) axonal glycolysis is not enough to robustly sustain CAPs, mitochondrial function is key, 2) blocking mitochondrial function rapidly blocks CAPs, 3) glucose, pyruvate and lactate can all sustain CAP activity, and 4) blockade of MCT function or lactate transport only mildly affects CAP, but more strongly affects ATP. It is suprising how much ATP can be around, yet CAPs still fire.

The final interesting, but not fully explained phenotype that is observed is the fact that PLP1 KO animals exhibit strong defects in ATP and CAPs, even in the presence of extracellular lactate or glucose. Why PLP1 KO animals are more sensitive than WT controls to high frequency stimulation is unclear. Finally, the MCT het KO experiments are not particularly informative. If easy, repeating the experiment in an oligo-specifc cKO situation would be very interesting. So, I'm not sure what that experiment really adds.

We very much agree with these points and have changed the manuscript accordingly (see also the response to referee 2 below).

The original "motivation" to include Plp1 KO and MCT1 het data at this stage was twofold. We had thought this could provide a missing "proof-of-principle" that the recently discovered function of oligodendrocytes in metabolic support of axon function (Fünfschilling et al., Nature 2012; Lee et al., Nature 20012) and our much older observation that oligodendrocytes are required for axonal integrity independent of myelin itself (Griffiths et al., Science 1998; Lappe-Siefke et al., Nat Genet. 2003) are two sides of the same coin. However, we realize that it is better to make this point with a full-sized data set once we have generated the necessary mouse numbers. Additional mouse mutants will be available only in about 6 months from now.

Similarly, the motivation to include MCT1 heterozygous mice here was the attempt to bridge to the Nature paper by Lee et al. (2012), assuming axonal degeneration in the optic nerve is preceded by an early metabolic phenotype. Surprisingly, no differences in axonal ATP levels were detected yet between MCT heterozygous mice and controls (see original Figure 6—figure supplement 1). This is likely no contradiction to Lee et al. as we investigated optic nerves at much earlier time points. Most likely, we will make different observations later in life. We also point out that the most degeneration-prone axons of Lee et al. 2012 (and also in Griffiths et al., 1998) are those of small radial caliber, whereas we selected on average larger axons for FRET analysis and CAP recordings.

We have now followed the advice of referee 1 and have removed Figure 6—figure supplement 1 from the final manuscript. The suggestion to include oligodendrocyte-specific MCT1 cKO mice awaits the collaboration with our US colleagues who may be willing to provide us with MCT1-floxed mice.

Reviewer #2:

[…] 1) The authors show in Figure 3 that there is a striking correspondence between decreases in both CAP and ATP during high frequency stimulation. However, the relationship appears to break down when changes are compared in fairly high concentrations of D-lactate or in the MCT1/2 inhibitor AR-C155858. Why is this?

We presume this point refers to Figures 4 and 5 (notably Figure 5E,F). Indeed, this is an intriguing finding, which confirms CAP area on one hand as a proxy for axonal ATP levels; however, under metabolic stress (high frequency stimulation in combination with inhibition of lactate metabolism) CAP recordings appear less vulnerable than ATP measurements.

The concentration of ATP in the axon is affected by both the usage of ATP and its production. Changes in CAP might be more related to ATP consumption, while inhibition of lactate transport and metabolism most likely mainly affects ATP production. As the stimulation protocol is identical in controls and after application of D-lactate or AR-C155858, we assume that consumption of ATP is similar in these conditions. Therefore, we think the most likely explanation is that inhibition of lactate metabolism impairs ATP production, resulting in a lower level of ATP as observed in our experiments.

Formally, we cannot exclude other mechanisms, such as the increasing levels of extracellular K+ or altered Ca2+ signaling. However, all changes in CAP are likely related to ATP consumption, while inhibition of lactate transport would affect ATP production. We also note that the CAP waveform is not affected by the presence of these inhibitors (see new Figure 5—figure supplement 2), suggesting that axonal conduction properties not dramatically impaired.

A discussion of this issue has been added to the last paragraph of the subsection “ATP homeostasis of myelinated axons depends on lactate metabolism”.

2) The pH dependence from the original Imamura PNAS paper is complex and varies depending on the ATP concentration. The excitation for FRET at 458 is a longer wavelength than the Imamura paper (435 nm). At 435 nm there is selective excitation of CFP but at 458 there is some excitation of YFP. Therefore, at this wavelength there is a minor component of YFP direct excitation contributing to the FRET signal. This is a potential worry that the authors could eliminate by testing the pH dependence using their system. The authors mention normalizing some of the results with changes in YFP fluorescence changes. However, this is not clearly explained in the Methods. Was this always done? Could changes in pH play a role in altering the FRET signals in the animals’ disease models at different times of development. I suspect not but the authors should include some discussion on these points.

We agree this is an important point that indeed might affect experiments using FRET based sensors for metabolites.

In our experiments, we had always determined the ATP concentration by a ratiometric measurement, i.e. FRET / CFP = (excitation of CFP / emission of YFP) divided by (excitation of CFP / emission of CFP). YFP-fluorescence is considered the most pH sensitive part of the sensor and YFP fluorescence should change in case of significant pH changes. We had therefore also monitored (routinely) the YFP fluorescence following direct YFP excitation at 514 nm to reveal such pH effects, which we did not detect. In fact, the YFP signal was very stable in those experiments, which is shown for the experiments with application of mitochondrial blockage plus glucose deprivation in Figure 1C and D of the original manuscript. Thus, it is unlikely that the ATP-signal is mainly a modulation of the readout due to pH changes. We have added a clarifying statement in the Methods section (subsection “ATP”).

In response to this point, we have added more experiments analyzing YFP fluorescence during glucose deprivation, mitochondrial blockage, and during high frequency stimulation (see new Figure 3—figure supplement 2, Figure 4—figure supplement 5) and added a corresponding description in the Results sections “Kinetics of ATP decline during energy deprivation” and “ATP homeostasis of myelinated axons depends on lactate metabolism”. This shows that also under more challenging conditions, pH changes did not significantly affect our ATP recordings.

3) The activity dependent changes in ATP signals and therefore ATP changes during high frequency spiking are very interesting. However, the results in Figure 6 are the critical data for concluding there are early changes in the disease model. There are small changes in the disease model in the ATP FRET signal at 50 Hz stim in glucose and in the lactate groups. However, these results are only from 3 nerves in each group (Figure 6D and F). This small number seems too low particularly as there is one quite divergent value in the 50 Hz lactate category.

We completely agree and have removed this data set as too preliminary from the revised manuscript. See also our response to reviewer 1 (above).

In addition the authors should include both the number of animals and the number of nerves (e.g. was more than one nerve used from one animal).

In all experiments, we have used only one nerve from each animal for each condition of experiment shown, i.e. the number of nerves equals to the number of animals. We have added a clarifying sentence in the Methods section “ATP”.

The text in the Results section states there were 4 nerves in each set but there are only 3 points in the bar charts and the figure legends says 3 nerves.

We assume that this refers to Figure 5C and D, showing D-Lac and AR-C155858 data, where CAP data were given for 4 nerves while ATP levels are shown only for 3 nerves. Here, ATP imaging of one nerve could not be properly analyzed (due to loosing of focal plane) while CAP recordings were normally analyzed. We have now repeated these experiments and n numbers are now for both inhibitors increased to n=6 for CAP and n=5 for ATP. We have clarified this point in the last paragraph of the Results section “ATP homeostasis of myelinated axons depends on lactate metabolism” and figure legend of Figure 5C and D.

4) Subsection “PLP loss in oligodendrocytes impairs axonal ATP homeostasis”, last paragraph: The authors hint there is a non-significant trend towards reduced ATP levels in the optic nerves from Slc16a1 gets but I don't see this and it is not significant. Therefore, I would conclude that there is no effect if it is not statistically significant.

As suggested by reviewer 1, the too preliminary data from mutant mice (original Figure 6) have been removed from the revised manuscript. See also above.

In the conclusions "Applied to a mouse model of X-linked spastic paraplegia or SPG-2 we were able to detect an effect of mutant oligodendrocytes on axonal energy metabolism and physiology several months before these animals develop widespread axonal loss and spastic paraplegia. Compared at a preclinical stage to wildtype nerves, ATP homeostasis is impaired in axons conducting at high frequency, but appears normal under resting conditions." The data has the minimum n values to state that ATP homeostasis is impaired. The authors should increase the number of experiments to provide more robust evidence. There may be variations in the handling etc. that may distort a few of the values in such a few experiments.

As suggested by reviewer 1, the too preliminary data from mutant mice (original Figure 6) have been removed from the revised manuscript. See also above.

However, we have performed additional experiments to increase the n of wildtype mice for experiments studying the effect of D-Lac and AR-C155858 (see revised Figure 5; now n=6 nerves for CAP and n=5 nerves for ATP) and the results confirmed our previous data.

Reviewer #3:

This is a very interesting and elegant study of CNS axon energy metabolism with a focus on axonal ATP using using a novel axonally-expressed FRET-based fluorescent ATP reporter. Importantly, they shed important light on how genetic defects of myelin production paradoxically result in axonal degeneration.

We thank the reviewer for his positive comments.

1) "while after 2min of MB+GD the F/C-ratio was reduced by 35.1% ± 1.7% (n=19 121 nerves; Figure 2F)": p values and significance need to be cited.

Respective p values and significance values have been added to the text and the figure.

2) Subsection “Data analysis” – CAP analysis: I agree with authors' choice of analyzing CAP area up to 1.5ms, which should include large and medium-sized axons, the ones that will be resolvable by confocal imaging. Authors should explain this point in Methods.

As requested, we have added an explanatory statement in the Methods section “CAP”. We also now indicate these time windows in Figure 3—figure supplement 1 and Figure 4—figure supplement 1.

3) Figure 3: authors should include a supplementary figure showing representative CAP waveforms and how they decay with the various energy deprivation paradigms, which may differ.

As requested, a new supplementary figure panel was added to the revised manuscript, which shows the CAP waveforms for all three paradigms of energy deprivation (glucose deprivation, mitochondrial blockage, and both) for different time points. See new Figure 3—figure supplement 1.

4) Given points 2 and 3 above, raises a potential difficulty: consider Figure 4 for example, where CAP changes also include a significant lengthening of the waveform: by restricting the area analysis to 1.5ms, while still imaging ATP FRET signals in medium-sized axons by confocal, there may arise an artifactual disconnect between CAP area and axonal ATP levels, merely because CAP lengthening will move significant signal beyond their 1.5ms integration window. Authors should explain how they dealt with this potential limitation and how it may have affected their results.

We thank the reviewer for raising this important issue. How to quantify "CAP areas" is indeed a complicated issue and all paradigms have their own pros and cons. When using a "fixed" time window (as done in our present analysis) there is no need to define the borders between different peaks, which can be difficult and arbitrary, or to define the time point when the CAP area reaches baseline level again (note the asymptotic curve). However, as pointed out this approach is risking an overestimation of the loss of CAP area with increasing peak latencies (which is indeed the case for the high frequency stimulation depicted in Figure 4). Thus, the CAP areas measured with a "fixed" time window integrate two functional changes of nerve conduction: the decrease of peak amplitudes and the increase in peak latency. It is, therefore, a parameter well suited to quantify overall functional changes of the optic nerve. As already discussed in our original manuscript, ATP imaging was performed in a single optical plane comprising typically around 30 axons with a random proportion of large and medium sized axons, while CAP recordings reflect the summation of action potential propagation of virtually all axons within the optic nerve. We therefore never did attempt to relate changes in single CAP peaks to ATP changes. However, the results obtained are consistent between different nerves irrespectively of the selected imaging region (and therefore the proportion of large and medium-sized axons), suggesting that both parameters used are a good approximation for the functional properties of the optic nerve.

We have a clarifying sentence Methods section “CAP”.

5) Subsection “ATP homeostasis of myelinated axons depends on lactate metabolism”, last paragraph: "…plus 20mM D-lactate.…": authors should clarify that physiological lactate is the L isomer for readers who may not know.

We agree and have added an explanatory note on D-lactate and L-lactate in the third paragraph of the subsection “ATP homeostasis of myelinated axons depends on lactate metabolism”.

6) Discussion, third paragraph: given their ATP FRET reporter, can the authors give a rough estimate of absolute ATP concentrations in axons? This would be an interesting piece of reference data for the field.

We fully agree with the reviewer that the absolute concentration of ATP in axons is a missing information. Indeed, we have spent significant effort in trying to calibrate the ATP sensor, which, however, is more difficult than expected:

i) Calibration should be performed in the same tissue as the measurement itself (optic nerve axons) in order to avoid errors caused by sample specific optical properties i.e. scattering light in anisotropic tissue.

ii) For calibration, ATP concentrations need to be controlled from outside, e.g. by permeabilizing the membrane, but without leakage of the sensor itself. So far we were unsuccessful to do this, because myelin membranes require quite harsh permeabilization conditions.

iii) Even if permeabilization is successful, it is difficult to achieve an equal distribution of ATP inside and outside a cellular compartment. We note that ATP is a negatively charged molecule that does not diffuse freely into axons through a membrane with a negative membrane potential.

iv) Numerous intracellular enzymes use ATP and it is almost impossible to block all ATP consuming processes (the main consumer Na+/K+-ATPase can be blocked, but many others not). The risk remains that ATP inside the axon is lower than outside. For all these reasons we have not included absolute ATP concentrations in our original manuscript.

However, as we readily see changes of the ATP signal in the course of an experiment, it is very likely that the basal ATP level is within the dynamic range of the sensor (Kd = 1.2 mM, Imamura et al., 2009), possibly close to 2 mM. This concentration would also be in line with the recently reported concentration of free ATP in cultured neurons (1.4 mM; Rangaraju et al., 2014) and synaptic boutons of cultured neurons (2-4mM; Pathak et al., 2015).

We have added a sentence explaining this issue in the second paragraph of the Discussion.

To really solve this issue in the not too distant future, we have started analyzing the ATP sensor signals by fluorescent life time imaging (FLIM). This approach should allow a more precise calibration and absolute measurement of ATP concentrations in optic nerves, as the FLIM readouts are expected to not be that affected by the cellular environment.

https://doi.org/10.7554/eLife.24241.022

Article and author information

Author details

  1. Andrea Trevisiol

    Department of Neurogenetics, Max-Planck-Institute for Experimental Medicine, Göttingen, Germany
    Contribution
    AT, Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared.
  2. Aiman S Saab

    1. Department of Neurogenetics, Max-Planck-Institute for Experimental Medicine, Göttingen, Germany
    2. Institute of Pharmacology & Toxicology, University of Zurich, Zurich, Switzerland
    3. Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
    Contribution
    ASS, Conceptualization, Formal analysis, Validation, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared.
  3. Ulrike Winkler

    Carl-Ludwig-Institute for Physiology, University of Leipzig, Leipzig, Germany
    Contribution
    UW, Formal analysis, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared.
  4. Grit Marx

    Carl-Ludwig-Institute for Physiology, University of Leipzig, Leipzig, Germany
    Contribution
    GM, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared.
  5. Hiromi Imamura

    Graduate School of Biostudies, Kyoto University, Kyoto, Japan
    Contribution
    HI, Resources, Writing—review and editing
    Competing interests
    No competing interests declared.
  6. Wiebke Möbius

    1. Department of Neurogenetics, Max-Planck-Institute for Experimental Medicine, Göttingen, Germany
    2. Center Nanoscale Microscopy and Molecular Physiology of the Brain, Göttingen, Germany
    Contribution
    WM, Formal analysis, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2902-7165
  7. Kathrin Kusch

    Department of Neurogenetics, Max-Planck-Institute for Experimental Medicine, Göttingen, Germany
    Contribution
    KK, Formal analysis, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared.
  8. Klaus-Armin Nave

    Department of Neurogenetics, Max-Planck-Institute for Experimental Medicine, Göttingen, Germany
    Contribution
    K-AN, Conceptualization, Supervision, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    nave@em.mpg.de
    Competing interests
    K-AN: Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8724-9666
  9. Johannes Hirrlinger

    1. Department of Neurogenetics, Max-Planck-Institute for Experimental Medicine, Göttingen, Germany
    2. Carl-Ludwig-Institute for Physiology, University of Leipzig, Leipzig, Germany
    Contribution
    JH, Conceptualization, Data curation, Formal analysis, Supervision, Methodology, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    johannes.hirrlinger@medizin.uni-leipzig.de
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6327-0089

Funding

Deutsche Forschungsgemeinschaft

  • Johannes Hirrlinger

H2020 European Research Council

  • Klaus-Armin Nave

European Molecular Biology Organization

  • Aiman S Saab

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

Within the Max-Planck-Institute for Experimental Medicine, Göttingen, we thank the transgene core unit for performing oocyte injections, the mechanical workshop for construction of the imaging setup as well as the IT-department for help with handling huge datasets. We would like to thank Prof. Jens Eilers, Carl-Ludwig-Institute for Physiology, Leipzig, for providing custom written Fiji plugins and established work flows for image analysis.

Ethics

Animal experimentation: Animals were treated in accordance with the German Protection of Animals Act (TSchG §4 Abs. 3), with the guidelines for the welfare of experimental animals issued by the European Communities Council Directive 2010/63/EU as well as the regulation of the institutional "Tierschutzkommission" and the local authorities (T04/13, T20/16; Landesdirektion Leipzig, LAVES Niedersachsen).

Reviewing Editor

  1. Gary L Westbrook, Vollum Institute, United States

Publication history

  1. Received: December 14, 2016
  2. Accepted: April 16, 2017
  3. Accepted Manuscript published: April 17, 2017 (version 1)
  4. Version of Record published: May 3, 2017 (version 2)

Copyright

© 2017, Trevisiol et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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