1. Immunology and Inflammation
  2. Neuroscience

Enhancing mitochondrial activity in neurons protects against neurodegeneration in a mouse model of multiple sclerosis

  1. Sina C Rosenkranz
  2. Artem A Shaposhnykov
  3. Simone Träger
  4. Jan Broder Engler
  5. Maarten E Witte
  6. Vanessa Roth
  7. Vanessa Vieira
  8. Nanne Paauw
  9. Simone Bauer
  10. Celina Schwencke-Westphal
  11. Charlotte Schubert
  12. Lukas Can Bal
  13. Benjamin Schattling
  14. Ole Pless
  15. Jack van Horssen
  16. Marc Freichel
  17. Manuel A Friese  Is a corresponding author
  1. Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Germany
  2. Department of Pathology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Netherlands
  3. Department of Molecular Cell Biology and Immunology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Netherlands
  4. Fraunhofer ITMP ScreeningPort, Germany
  5. Institute of Pharmacology, Heidelberg University, Germany
https://doi.org/10.7554/eLife.61798
Share this article
Cite this article
  1. Sina C Rosenkranz
  2. Artem A Shaposhnykov
  3. Simone Träger
  4. Jan Broder Engler
  5. Maarten E Witte
  6. Vanessa Roth
  7. Vanessa Vieira
  8. Nanne Paauw
  9. Simone Bauer
  10. Celina Schwencke-Westphal
  11. Charlotte Schubert
  12. Lukas Can Bal
  13. Benjamin Schattling
  14. Ole Pless
  15. Jack van Horssen
  16. Marc Freichel
  17. Manuel A Friese
(2021)
Enhancing mitochondrial activity in neurons protects against neurodegeneration in a mouse model of multiple sclerosis
eLife 10:e61798.
https://doi.org/10.7554/eLife.61798
  2. Download BibTeX
  3. Download .RIS

Abstract

While transcripts of neuronal mitochondrial genes are strongly suppressed in central nervous system inflammation, it is unknown whether this results in mitochondrial dysfunction and whether an increase of mitochondrial function can rescue neurodegeneration. Here, we show that predominantly genes of the electron transport chain are suppressed in inflamed mouse neurons, resulting in impaired mitochondrial complex IV activity. This was associated with post-translational inactivation of the transcriptional co-regulator proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α). In mice, neuronal overexpression of Ppargc1a, which encodes for PGC-1α, led to increased numbers of mitochondria, complex IV activity, and maximum respiratory capacity. Moreover, Ppargc1a-overexpressing neurons showed a higher mitochondrial membrane potential that related to an improved calcium buffering capacity. Accordingly, neuronal deletion of Ppargc1a aggravated neurodegeneration during experimental autoimmune encephalomyelitis, while neuronal overexpression of Ppargc1a ameliorated it. Our study provides systemic insights into mitochondrial dysfunction in neurons during inflammation and commends elevation of mitochondrial activity as a promising neuroprotective strategy.

eLife digest

Multiple sclerosis is a life-long neurological condition that typically begins when people are in their twenties or thirties. Symptoms vary between individuals, and within a single individual over time, but can include difficulties with vision, balance, movement and thinking. These occur because the immune system of people with multiple sclerosis attacks the brain and spinal cord. This immune assault damages neurons and can eventually cause them to die. But exactly how this happens is unclear, and there are no drugs available that can prevent it.

One idea is that the immune attack in multiple sclerosis damages neurons by disrupting structures inside them called mitochondria. These cellular ‘organs’, or organelles, produce the energy that all cells need to function correctly. If the mitochondria fail to generate enough energy, the cells can die. And because neurons are very active cells with high energy demands, they are particularly vulnerable to the effects of mitochondrial damage.

By studying a mouse version of multiple sclerosis, Rosenkranz et al. now show that mitochondria in the neurons of affected animals are less active than those of healthy control mice. This is because the genes inside mitochondria that enable the organelles to produce energy are less active in the multiple sclerosis mice. Most of these genes that determine mitochondrial activity and energy production are under the control of a single master gene called PGC-1alpha. Rosenkranz et al. showed that boosting the activity of this gene — by introducing extra copies of it into neurons — increases mitochondrial activity in mice. It also makes the animals more resistant to the effects of multiple sclerosis.

Boosting the activity of mitochondria in neurons could thus be a worthwhile therapeutic strategy to investigate for multiple sclerosis. Future studies should examine whether drugs that activate PGC-1alpha, for example, could help prevent neuronal death and the resulting symptoms of multiple sclerosis.

Introduction

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) and the most frequent non-traumatic cause of neurological impairment during early adulthood. Neuronal loss occurs already from disease onset and correlates best with irreversible disability in MS patients (Fisher et al., 2008; Fisniku et al., 2008; Tallantyre et al., 2010). While there has been substantial progress in the understanding and treatment of the immune response, the pathogenesis of concurrent neuronal damage is incompletely understood. Recently, two drugs, ocrelizumab and siponimod, have been licensed for the treatment of progressive MS patients; however, their mode of action relies on immune regulation (Kappos et al., 2018; Montalban et al., 2017). Since there are currently no therapies available to counteract the progression of neurodegeneration in MS patients (Dendrou et al., 2015; Feinstein et al., 2015) and repurposing of drugs to directly target neurodegeneration in MS has been disappointing (Chataway et al., 2020), a better understanding of the molecular processes that determine neuronal cell loss in MS is urgently needed.

Neuronal loss in MS and its animal model, experimental autoimmune encephalomyelitis (EAE), has been associated with enhanced production of reactive oxygen and nitrogen species by immune cells and with increased iron accumulation in the gray matter (Stephenson et al., 2014). Both processes can lead to damage of neuronal mitochondria with subsequent metabolic failure (Campbell et al., 2011). Moreover, disruption of neuronal ion homeostasis (Craner et al., 2004; Friese et al., 2014) and aggregation of neuronal proteins (Schattling et al., 2019) consume high amounts of energy that might further drive neuroaxonal injury. Furthermore, excessive activation of calcium-dependent processes and neuronal calcium overload seems to be another important component of neuronal injury (Friese et al., 2007; Schattling et al., 2012; Witte et al., 2019). While identifying druggable targets that specifically induce neuronal resilience has been extremely difficult due to insufficient insights into key modulators, mitochondria could serve as an important hub as they are pivotal for both energy production and calcium homeostasis (Vafai and Mootha, 2012).

Mitochondria usually have a high calcium buffering capacity, which is driven by their negative membrane potential that is generated by the activity of oxidative phosphorylation (Kann and Kovács, 2007; Zorova et al., 2018). However, overload of mitochondria with calcium as a consequence of CNS inflammation can result in inappropriate activation of the mitochondrial permeability transition pore (PTP) with subsequent mitochondrial swelling and cell death (Giorgi et al., 2018; Rizzuto et al., 2012), which is one of the neuropathological hallmarks in neurons during EAE (Nikić et al., 2011). Inhibiting the mitochondrial matrix protein cyclophilin D, a regulator of the PTP can partially counteract this dysregulation (Forte et al., 2007). Additionally, to counteract ion imbalance, a higher activity of ATP-dependent ion pumps would be required. However, postmortem studies of MS patients’ CNS tissue revealed a compromised neuronal ATP production by oxidative phosphorylation as decreased mitochondria complex IV activity in demyelinated axons and neurons was detected (Campbell et al., 2011; Mahad et al., 2009). Similarly, mitochondrial gene expression is suppressed in motor neurons of the spinal cord in EAE mice (Schattling et al., 2019), further supporting a comprised mitochondrial function during CNS inflammation.

Thus, excess neuroaxonal calcium, together with mitochondrial dysfunction, has been postulated to trigger neuroaxonal injury observed in MS and EAE. This would be predicted to lead to elevated levels of calcium within neuronal and axonal mitochondria, further perpetuating mitochondrial dysfunction and neuronal injury. However, it is currently unknown whether interventions that increase mitochondrial energy production in neurons can rescue neurodegeneration during CNS inflammation.

Here, we discovered by an unsupervised survey of neuronal gene expression during CNS inflammation that genes involved in the electron transport chain (ETC), especially in complex I and IV, are repressed in motor neurons. While this is not accompanied by a decrease of mitochondrial numbers in motor neuronal somata, we detected a decrease in neuronal mitochondrial complex IV activity. This implies a lower activity of neuronal mitochondria during EAE, and we were able to associate that with a post-translational inactivation of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), one of the master regulators of mitochondrial numbers and function. Notably, neuronal overexpression of Ppargc1a, which encodes for PGC-1α, led to an increase of mitochondrial activity, especially in complex IV activity and substantially elevated calcium buffering capacity. Subjecting these mice to EAE resulted in a significantly better recovery from clinical disability in comparison to wild-type controls. Together, induction of neuronal mitochondrial activity commends as a promising therapeutic approach to counteract inflammation-induced neurodegeneration.

Results

Impaired neuronal oxidative phosphorylation in inflamed spinal cords

Since we have previously discovered that mitochondrial gene transcripts are markedly suppressed in motor neurons during CNS inflammation in the EAE model (Schattling et al., 2019), we first investigated whether unifying biological processes are dysregulated. Therefore, we derived a gene expression signature of motor neurons in spinal cord during CNS inflammation (Schattling et al., 2019) and tested for gene set enrichment of biological process gene ontology (GO) terms. While the majority of terms was enriched during inflammation, only a confined group of 29 terms showed strong de-enrichment (Figure 1A, dashed line) and was dominated by terms representing mitochondrial function (Figure 1B). A recurring theme was the suppression of the ETC (Figure 1C). Suppression of gene transcripts that are involved in the ETC, which is the key mechanism of oxidative phosphorylation, can either result in reduced number of mitochondria or an impairment of mitochondrial function. Therefore, we quantified mitochondria content in the soma of motor neurons in EAE mice at the chronic stage in comparison to healthy control mice, but did not detect any difference in mitochondria area normalized to neuronal size (Figure 1D, Figure 1—figure supplement 1A). However, we detected a tendency towards an increase in overall numbers of motor neuronal mitochondria (Figure 1—figure supplement 1B) that was based on an increased size of the neuronal cell body during EAE (Figure 1E). By contrast, the size of mitochondria did not differ in motor neurons of healthy and EAE mice (Figure 1—figure supplement 1C).

Figure 1 with 1 supplement see all
Download asset Open asset
Reduced activity of oxidative phosphorylation in spinal cord neurons during experimental autoimmune encephalomyelitis (EAE).

(A) Gene set enrichment analysis (GSEA) of biological process gene ontology (GO) terms in motor neurons during central nervous system inflammation in the EAE model. Dashed line indicates 29 de-enriched terms. (B) Top 15 de-enriched GO terms with normalized enrichment score. (C) GSEA plot of the de-enriched term ‘GO:0022900 electron transport chain’. (D) Transmission electron microscopy (TEM) analysis of motor neuronal mitochondrial content normalized to size of healthy (n = 3) and chronic EAE (n = 3) mice (2–3 cells per mice). Bars show mean values ± s.e.m. (E) TEM analysis of motor neuronal size of healthy (n = 3) and chronic EAE (n = 3) mice (2–3 cells per mice). Bars show mean values ± s.e.m. (F) Representative images of cytochrome c oxidase (COX) histochemistry of cervical spinal cord sections of healthy, acute, and chronic EAE mice. Scale bar: 250 µm. (G) Quantification of COX histochemistry of cervical spinal cord gray matter (GM) and ventral horn (VH) of healthy (n = 5), acute (n = 3), and chronic (n = 5) EAE mice (2–3 stainings per mice) normalized to HuC/HuD-positive neurons. Bars show mean values ± s.e.m. Statistical analysis in D and E was performed by unpaired, two-tailed Student’s t-test, and in G by one-way ANOVA following Tukey’s post-hoc test for multiple comparisons. *p<0.05, ****p<0.0001.

Dismissing differences in mitochondrial content, we reasoned that downregulation of ETC transcripts could result in a compromised oxidative phosphorylation activity. By analyzing COX histochemistry that represents complex IV activity, we detected a significant decrease in absolute neuronal complex IV activity in the entire gray matter and ventral horn of acute EAE animals (day 15 post immunization) and in chronic EAE animals (day 30 post immunization) in comparison to healthy control mice (Figure 1F). Normalization to HuC/HuD-positive neurons revealed that the reduced complex IV activity at day 30 was partly driven by neuronal loss, whereas at day 15 the reduced complex IV activity was independent of neuronal numbers (Figure 1G, Figure 1—figure supplement 1D–E).

Inactivation of PGC-1α contributes to mitochondrial dysfunction in CNS inflammation

Next we interrogated whether a unifying mechanism can explain the inflammation-induced downregulation of gene transcripts that are involved in ETC with consecutively impaired complex IV activity in neurons at the acute stage of EAE. Notably, we detected that many of the genes that drive the downregulation of the ETC theme are usually induced by the transcriptional coactivator Ppargc1a (Lucas et al., 2014; Puigserver et al., 1998; Figure 2A). Consequently, we asked whether Ppargc1a could be the sought-after unifying factor that is functionally disturbed. Motor neuronal Ppargc1a expression itself was not altered in our previously published dataset comparing acute EAE to controls (Figure 2B). After confirming this result by an additional translating ribosome affinity purification of motor neuronal transcripts followed by Ppargc1a qPCR (Figure 2C) and immunoblot of PGC-1α of the cervical spinal cord of EAE and healthy mice (Figure 2D, E), we concluded that transcriptional or translational Ppargc1a/PGC-1α regulation cannot be the explanation of the observed ETC downregulation.

Figure 2 with 1 supplement see all
Download asset Open asset
Inactivation of proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) in spinal cord neurons during experimental autoimmune encephalomyelitis (EAE).

(A) Heatmap of genes driving the de-enrichment of ‘GO:0022900 electron transport chain’. Heatmap is annotated for electron transport chain complexes and target genes induced by the transcription factor Ppargc1a (Lucas et al., 2014). (B) Normalized RNA-seq expression of Ppargc1a in healthy (n = 5) and acute EAE (n = 5) motor neurons. Bars show mean values ± s.e.m. (C) Relative qPCR mRNA expression of Ppargc1a to Tbp in healthy (n = 3) and acute EAE (n = 3) motor neurons. Bars show mean values ± s.e.m. (D) Quantification of PGC-1α protein in cervical spinal cords of healthy (n = 5) and acute (n = 5) EAE mice. Each sample was normalized to its β-actin. Bars show mean values ± s.e.m. (E) Representative immunoblots of PGC-1α, phosphorylated PGC-1αS570 (pPGC-1α), and corresponding β-actin of cervical spinal cords of healthy and acute EAE mice. (F) Quantification of phosphorylated PGC-1αS570 (pPGC-1α) in relation to PGC-1α total protein in cervical spinal cords of healthy (n = 5) and acute (n = 5) EAE mice. Each sample was normalized to its β-actin. Bars show mean values ± s.e.m. Statistical analysis in B and C was performed by unpaired, two-tailed Student’s t-test, and in D and F by unpaired, two-tailed Mann–Whitney test; *p<0.05.

Besides quantity, PGC-1α can be post-translationally inactivated by phosphorylation at serine 570 (Li et al., 2007; Xiong et al., 2010), thereby preventing the recruitment of PGC-1α to its regulated promoters, which we tested next. After validation of phosphorylation specificity of the pPGC-1αS570 antibody (Figure 2—figure supplement 1), we detected a significant increase in phosphorylated-serine570 PGC-1α in spinal cords of acute EAE mice in comparison to healthy control mice (Figure 2E, F). Thus, we concluded that due to the phosphorylation of PGC-1α during acute stage of EAE that is associated with downregulation of its target gene transcripts and compromised mitochondrial function, Ppargc1a could be a potential target to rescue mitochondrial function in EAE-affected motor neurons.

Neuronal Ppargc1a induction augments mitochondrial function

Next, we reasoned that elevating neuronal Ppargc1a could restore mitochondrial function and serve as a strategy to improve neuronal resilience in CNS inflammation. We utilized transgenic mice with a neuron-specific overexpression of Ppargc1a driven under the Thy1 promoter (Thy1-Ppargc1a) (Mudò et al., 2012), which is expressed in mature neurons (Figure 3—figure supplement 1A). We could not observe any differences in the health status of these mice. We made sure that elevated transgenic DNA copies of Ppargc1a in neurons (Figure 3—figure supplement 1B) resulted in elevated mRNA expression of Ppargc1a in cortex, hippocampus, and spinal cord as well as in cortical and hippocampal primary neurons of Thy1-Ppargc1a mice in comparison to wild-type mice (Figure 3—figure supplement 1C–D). Additionally, we revealed that Thy1-Ppargc1a mice showed an increase of Ppargc1a-dependent transcriptional targets in primary neurons (Figure 3—figure supplement 1E–F) and cytochrome c oxidase subunit 4 (Cox4i1) and citrate synthase (Cs) in CNS tissue (Figure 3—figure supplement 1G–I). Increased mRNA level led to a corresponding increase in protein amount of PGC-1α in primary neurons (Figure 3—figure supplement 1J) and spinal cord, which we confirmed by staining for the transgenically fused FLAG-tag (Figure 3—figure supplement 1K). Besides Cox4i1, in primary hippocampal neurons, Ppargc1a overexpression induced 11 of the 17 Ppargc1a regulated ETC genes, which were downregulated in EAE (Figure 3A). Accordingly, neuronal Ppargc1a overexpression resulted in increased mitochondrial content in primary neurons (Figure 3B) and CNS tissue (Figure 3C).

Figure 3 with 1 supplement see all
Download asset Open asset
Neuronal overexpression of Ppargc1a increases neuronal mitochondrial activity.

(A) Relative qPCR mRNA expression in hippocampal primary neuronal cultures (hPNC) (DIV14) of wild-type (n = 4) and Thy1-Ppargc1a (n = 4) mice of Ppargc1a-regulated electron transport chain genes that were detected to be downregulated during experimental autoimmune encephalomyelitis. Bars show mean values ± s.e.m. (B) Mitochondrial DNA copy numbers (mtDNA) relative to diploid nuclear chromosomal DNA (nDNA) in hPNC (DIV14) of wild-type (n = 3) and Thy1-Ppargc1a (n = 3) mice. Bars show mean values ± s.e.m. (C) mtDNA relative to nDNA in muscle, liver, hippocampus (Hip), cortex, and cervical spinal cord (SC) of wild-type (n = 3) and Thy1-Ppargc1a (n = 3) mice. Bars show mean values ± s.e.m. (D) Representative images and quantification of cytochrome c oxidase (COX) histochemistry of cervical spinal cord gray matter (GM) or ventral horn (VH) of wild-type (n = 5) and Thy1-Ppargc1a (n = 6) mice (2–3 stainings per mice) normalized to neuronal nuclei (NeuN)-positive neurons. Bars show mean values ± s.e.m. Scale bar 250 µm. (E) Profile and quantification of oxygen consumption rate in hPNC (DIV14) of wild-type (n = 4) and Thy1-Ppargc1a (n = 4) mice. Yellow: basal respiration (Basal); blue: ATP-dependent respiration (ATP dependent); orange: maximal respiratory capacity (Max); green: non-mitochondrial respiration (NMR). Bars show mean values ± s.e.m. (F) Representative images of hPNC and mean fluorescence intensity quantification of tetramethylrhodamin-ethylester mitochondrial membrane potential assay of hPNC (DIV14) and cortical primary neuronal culture of wild-type (n = 3) and Thy1-Ppargc1a (n = 3) mice (five cells per culture). FCCP(carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone) was used as ionophore uncoupler of oxidative phosphorylation. Bars show mean values ± s.e.m. Scale bar: 10 µm. Statistical analysis in A was performed by unpaired, one-tailed Student’s t-test, and in B–E by unpaired, two-tailed Student’s t-test. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Figure 3—source data 1

Analysis of oxygen consumption rate in primary neurons of Thy1-Ppargc1a mice.

https://cdn.elifesciences.org/articles/61798/elife-61798-fig3-data1-v3.docx

To determine the mitochondrial activity in these mice, we analyzed whether Ppargc1a-mediated Cox4i1 induction resulted in increased complex IV activity. We detected an elevated complex IV activity in the gray matter and ventral horn of Thy1-Ppargc1a mice, which was not due to a higher number of NeuN-positive neurons (Figure 3D). Metabolically, primary neurons of Thy1-Ppargc1a mice showed an elevated oxygen consumption rate and maximum respiratory capacity (Figure 3E, Figure 3—source data 1), which was independent of neuronal numbers (Figure 3—figure supplement 1L). In concordance with higher activity of oxidative phosphorylation, primary neurons of Thy1-Ppargc1a mice retained higher tetramethylrhodamin-ethylester (TMRE) fluorescence intensity than wild-type controls, representing a hyperpolarized mitochondrial membrane potential (Figure 3F).

Induction of neuronal Ppargc1a improves neuronal calcium buffering capacity

As CNS inflammation results in substantial neuronal calcium influx (Witte et al., 2019) leading to neurodegeneration, which could potentially be buffered by mitochondria, we next asked whether increased neuronal mitochondrial activity and membrane potential in Thy1-Ppargc1a neurons could alleviate toxic calcium levels. By analyzing spontaneously active cortical primary neuronal cultures (cPNC) transduced with the virally encoded calcium indicator GCaMP6f (Figure 4A), we recorded a significantly faster clearance of intracellular calcium concentrations and decreased amount of mean calcium in spontaneously active Thy1-Ppargc1a primary neurons in comparison to wild-type control neurons, whereas the amplitude and number of calcium transients did not differ (Figure 4B, C). The results were confirmed with a chemical calcium indicator (Figure 4D and Video 1).

Figure 4
Download asset Open asset
Neuronal overexpression of Ppargc1a improves neuronal calcium buffering.

(A) Experimental approach for analysis of Ca2+ signaling. GCamp6f fluorescence was recorded for 10 minutes in spontaneously active cortical primary neuronal culture (cPNC) (DIV15) prior to ionomycin application used for signal normalization. (B) Representative cytosolic calcium transients of GCaMP6f-transduced spontaneously active cPNC (DIV15) of wild-type and Thy1-Ppargc1a mice normalized to fluorescence of cytosolic calcium-saturated conditions (Fmax). (C) Quantification of calcium transient decay constant Tau (T) as an indicator of cytosolic calcium clearance time, calcium transient amplitude (A), mean cytosolic calcium signal intensity and of the number of cytosolic calcium transients presented as firing rate of GCaMP6f-transduced cPNC (DIV15) of wild-type (n = 74 cells from three different mice) and Thy1-Ppargc1a (n = 51 cells from three different mice). 478s of the recorded trace were analysed. Bars show mean values ± s.e.m. (D) Representative cytosolic calcium trace of Fluo-4-stained cPNC (DIV14–16) of wild-type and Thy1-Ppargc1a mice. Statistical analysis in C was performed by unpaired, two-tailed Student’s t-test. **p<0.01.

Video 1
Download asset
Representative video of calcium transients of Fluo4 stained cortical primary neuronal culture (DIV15) of Thy1-Ppargc1a (left) and wild-type (right) mice.

Enhancing mitochondrial activity in neurons protects against neurodegeneration in a mouse model of multiple sclerosis.

Elevated neuronal mitochondrial activity ameliorates neurodegeneration in CNS inflammation

As we assumed that inactivation of neuronal Ppargc1a during CNS inflammation aggravates mitochondrial dysfunction and spurs neuronal vulnerability, we first tested whether this hypothesis holds true in Ppargc1a-deficient neurons. We used Ppargc1aflx/flx × Eno2Cre+ mice to specifically delete Ppargc1a in neurons as Eno2 is widely expressed in neurons (Figure 5—figure supplement 1A). Although Eno2 expression is also present by immune cells (Heng et al., 2008), we could exclude Ppargc1a expression in immune cells (Figure 5—figure supplement 1B, Heng et al., 2008), therefore securing that Ppargc1aflx/flx × Eno2Cre+ mice will not result in altered immune responses. Neuronal deletion of Ppargc1a resulted in decreased Ppargc1a mRNA levels (Figure 5—figure supplement 1C) and Ppargc1a -regulated downstream targets in hippocampus (Figure 5—figure supplement 1D), cortex (Figure 5—figure supplement 1E), and spinal cord (Figure 5—figure supplement 1F). Consequently, neuron-specific Ppargc1a knockout mice (Ppargc1aflx/flx × Eno2Cre+) showed a more severe EAE disease course (Figure 5A, Figure 5—source data 1) and by using NeuN-staining an increased neuronal loss in the gray matter and ventral horn in comparison to Ppargc1aflx/flx control EAE mice (Figure 5B). Since NeuN-staining can be compromised in the chronic phase of EAE, we corroborated our findings by using the neuronal marker HuC/D as an alternative staining of spinal cord neurons (Figure 5—figure supplement 1G). Notably, Ppargc1a deletion in neurons did not result in different neuronal numbers in healthy mice by using NeuN staining (Figure 5—figure supplement 1H).

Figure 5 with 1 supplement see all
Download asset Open asset
Neuronal Ppargc1a levels determine neuronal injury and clinical disability in experimental autoimmune encephalomyelitis (EAE).

(A) Mean clinical scores of Ppargc1aflx/flx (n = 6) and Ppargc1aflx/fl × Eno2Cre+ (n = 6) mice undergoing EAE. Curves show mean ± s.e.m. (B) Representative images and analysis of immunohistochemical stainings of surviving NeuN-positive neurons in cervical spinal cord ventral horn (VH) or gray matter (GM) of Ppargc1aflx/flx (n = 4) and Ppargc1aflx/flx × Eno2Cre+ (n = 4) mice (two areas per mice) at day 40 post-immunization with quantification. Bars show mean values ± s.e.m. Scale bar 100 µm. (C) Mean clinical scores of wild-types (n = 31) and Thy1-Ppargc1a (n = 22) mice undergoing EAE. Curves show mean ± s.e.m., all pooled from three independent experiments. (D) Histopathological stainings of T cells (CD3), microglia (IBA1), and macrophages (MAC-3) in cervical spinal cord sections of wild-type (n = 6) and Thy1-Ppargc1a (n = 5) mice (2–3 stainings per mice) at day 15 post immunization with quantifications. Bars show mean values ± s.e.m. Scale bar 250 and 100 µm. (E) Quantification of cytochrome c oxidase (COX) histochemistry of cervical spinal cord GM or VH of wild-type (n = 6) and Thy1-Ppargc1a (n = 4) mice (2–3 stainings per mice) at acute stage of EAE normalized to neuronal nuclei (NeuN)-positive neurons. Bars show mean values ± s.e.m. Scale bar 250 µm. (F) Representative images and analysis of immunohistochemical stainings of surviving NeuN-positive neurons in cervical spinal cord VH or GM and surviving neurofilament-positive axons in the dorsal columns of wild-type (n = 5) and Thy1-Ppargc1a (n = 5) (two areas per mice for NeuN, three areas per mice for neurofilament) at day 40 post immunization. Bars show mean values ± s.e.m. Scale bar: 100 µm and 50 µm. Statistical analysis in A and C was performed by one-tailed Mann–Whitney U test of area under the curve (AUC) starting at peak (day 15) of disease; in B, D, E, and F by unpaired, two-tailed Student’s t-test. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Figure 5—source data 1

Numerical data of clinical scores of Ppargc1aflx/flx (flx/flx) (n = 6) and Ppargc1aflx/flx × Eno2Cre+ (flx/flx × Eno2Cre) (n = 6) mice undergoing experimental autoimmune encephalomyelitis (EAE).

https://cdn.elifesciences.org/articles/61798/elife-61798-fig5-data1-v3.xlsx
Figure 5—source data 2

Numerical data of clinical scores of wild-types (WT) (n = 31) and Thy1-Ppargc1a (TT) (n = 22) mice undergoing experimental autoimmune encephalomyelitis (EAE).

https://cdn.elifesciences.org/articles/61798/elife-61798-fig5-data2-v3.xlsx

As we observed that neuronal Ppargc1a overexpression led to an increased oxidative phosphorylation and improved calcium buffering capacities, we next tested its translatability to the preclinical MS model. EAE induction in Thy1-Ppargc1a and wild-type littermate control mice resulted in a comparable maximum score in the acute phase of the disease. However, Thy1-Ppargc1a mice showed a significantly better recovery from clinical disability in comparison to wild-type controls (Figure 5C, Figure 5—source data 2). Notably, this protection is unlikely to be driven by an impaired immune response as numbers of infiltrating CD3+ T cells, activated IBA1+ microglia, and MAC-3+ macrophages were similar to WT mice (Figure 5D). Moreover, neuronal overexpression of Ppargc1a resulted in a rescue of complex IV activity during acute EAE in the entire gray matter and ventral horn (Figure 5E), leading to a significant reduction of neuronal loss in the gray matter and ventral horn and more intact axons at day 40 post immunization by using NeuN or HuC/HuD as neuronal marker (Figure 5F, Figure 5—figure supplement 1I). Neuronal Ppargc1a overexpression did also not influence neuronal numbers in healthy mice by using NeuN staining (Figure 5—figure supplement 1J).

Thus, induction of neuronal mitochondrial activity represents an attractive neuroprotective strategy in CNS inflammation by compensating mitochondrial dysfunction.

Discussion

Here, we show that neuronal oxidative phosphorylation is compromised during CNS inflammation, which directly contributes to neuronal vulnerability and can be counteracted by induction of neuronal mitochondrial activity.

In an unsupervised survey, we discovered that expression of mitochondrial genes is massively suppressed in motor neurons during CNS inflammation (Schattling et al., 2019) and here in particular genes that participate in oxidative phosphorylation. This sparked our interest to explore upstream mechanisms that coordinate this mitochondrial shutdown and whether counter-regulation could rescue neuronal integrity. Our observation is in concordance with other reports that showed alterations of mitochondria in the brain of MS patients with compromised oxidative phosphorylation in demyelinated axons and neurons (Campbell et al., 2011; Mahad et al., 2009). This could potentially contribute to mitochondrial swelling and dysfunction of axons in EAE (Nikić et al., 2011; Sadeghian et al., 2016). While we could not detect a decrease in somatic neuronal mitochondrial numbers in the spinal cord during EAE, we discovered a compromised complex IV activity in the spinal cord gray matter. This compromised oxidative phosphorylation during CNS inflammation is likely coordinated by inactivation of PGC-1α, a transcriptional coactivator that acts as a master switch of mitochondrial function (Mootha et al., 2003; Puigserver et al., 1998). However, in contrast to MS patients, in which a marked decrease in cortical PGC-1α expression has been reported (Witte et al., 2013), we could not detect a regulation of Ppargc1a in inflamed mouse motor neurons.

By contrast, our data show that CNS inflammation results in a post-translational modification of PGC-1α (Fernandez-Marcos and Auwerx, 2011). Increased phosphorylation of serine 570 leads to an inactivation of PGC-1α, which could be mediated by increased AKT activity (Li et al., 2007). Three different isoforms of AKT exist. Whereas AKT1 and AKT3 are mainly expressed in neurons, AKT2 is predominantly expressed in astrocytes (Levenga et al., 2017). So far, the exact function of AKT in neurons is not clear, but AKT1 has been implicated in late long-term potentiation (Levenga et al., 2017) and AKT3 in neuronal growth (Adams et al., 2016; Rivière et al., 2012). AKT is activated by phosphatidylinositol 3-kinase (PI3K), and the PI3K/AKT signaling pathway is known to reduce apoptosis and promote survival and proliferation (Brunet et al., 1999) and thereby counteracts neuronal cell death (Peltier et al., 2007). As TNF-α activates the PI3K/AKT pathway (Gu et al., 2006; Li et al., 2017; Osawa et al., 2001), upregulation of AKT could function as an immediate strategy of neurons against cell death during inflammation. As a trade-off, prolonged downregulation of mitochondrial gene transcripts will reduce ATP levels that could limit neuronal integrity during chronic inflammation. Thus, tuning Ppargc1a could act as a switch that modulates oxidative phosphorylation by regulating respective mitochondrial genes under challenging environmental conditions, such as CNS inflammation.

This seems to be particularly relevant in tissues with a high energy demand, for example, muscle, liver, and CNS in which Ppargc1a is highly expressed. While astrocytes are mostly relying on glycolysis, neurons mainly generate their energy by oxidative phosphorylation with low glycolytic capacities (Camandola and Mattson, 2017). Therefore, mitochondrial damage and inactivation is particularly deleterious for neuronal metabolism. That Ppargc1a is important for neuronal health is supported by our observation of an aggravated EAE disease course in neuron-specific Ppargc1a-deficient mice. This is in accordance with the global Ppargc1a knockout mice that showed neurological symptoms such as myoclonus, dystonia, and limb clasping that was attributed to axonal striatal degeneration (Lin et al., 2004), although we were not able to detect this phenotype in our neuron-specific Ppargc1a knockout mice.

While a recent report using Ppargc1a overexpression under the Eno2 promoter led to an ameliorated EAE disease course (Dang et al., 2019), we believe that this is mainly driven by suppressing the immune response and not by a neuron-intrinsic effect. As Eno2 is also expressed in immune cells and especially T cell function is influenced by altered mitochondrial activity (Buck et al., 2016), in accordance with our notion, the authors reported a reduced immune cell infiltration, less demyelination, a later EAE onset, and an ameliorated EAE disease course (Dang et al., 2019), which are typical features for an immunosuppressive phenotype. By contrast, our data show that specific neuronal overexpression of Ppargc1a elevated mitochondrial biogenesis and an improved activity of neuronal oxidative phosphorylation, particularly complex IV, which also resulted in a higher overall mitochondrial membrane potential. Notably, these alterations equipped mitochondria with an improved calcium buffering capacity. Besides MS, rise in neuronal intracellular calcium is one of the hallmarks of neurodegenerative diseases (Mattson, 2007). Several mechanisms are relevant that lead to intracellular neuronal Ca2+ accumulation in EAE (Siffrin et al., 2015), among them upregulation of voltage-gated sodium channels, Na+-Ca2+ antiporters (Craner et al., 2004), Ca2+ permeable ion channels (Friese et al., 2007; Schattling et al., 2012), and nanoscale ruptures of the axonal plasma membrane (Witte et al., 2019). Therefore, the improved calcium buffering capacity in Thy1-Ppargc1a neurons could efficiently rescue calcium overload and neuronal demise, which was indicated by an increased neuronal survival during EAE. Calcium uptake by mitochondria holds the promise as a therapeutic strategy for several neurodegenerative diseases (Lee et al., 2018; Parone et al., 2013). As neuronal mitochondrial deficiency is also associated with other neurodegenerative diseases, for example, Alzheimer’s disease (Pannaccione et al., 2020), induction of mitochondrial activity could serve as a unifying neuroprotective approach (Murphy and Hartley, 2018). Similarly, downstream targets of Ppargc1a are repressed in dopaminergic neurons of Parkinson's disease patients (Zheng et al., 2010) in which neuronal overexpression of Ppargc1a protects dopaminergic neurons in its mouse model (Mudò et al., 2012).

As we detected a higher neuronal resilience in Thy1-Ppargc1a mice during CNS inflammation, pharmacological increase of neuronal mitochondria and oxidative phosphorylation could be a promising neuroprotective strategy. Some drugs have already been described to induce Ppargc1a in different tissues (Dumont et al., 2012; Hofer et al., 2014; Noe et al., 2013; Ye et al., 2012); however, we could not confirm neuronal induction of Ppargc1a by treating mice with bezafibrate or using transient receptor potential cation channel subfamily V member 4 (Trpv4) knockout mice (data not shown). Another possible drug candidate is sirtuin-1 that activates PGC-1α by deacetylation, and its neuronal overexpression leads to an ameliorated EAE disease course (Nimmagadda et al., 2013). Besides extrinsic induction or activation of Ppargc1a, it can also be induced by intrinsic mechanisms such as cold exposure or exercise (Lin et al., 2005). Exercise ameliorates the EAE disease course (Klaren et al., 2014; Rossi et al., 2009) and shows several benefits in MS patients (Motl et al., 2017); however, the proof of a neuroprotective effect that is mediated by Ppargc1a induction is currently outstanding.

Taken together, we provide evidence for a therapeutic potential of inducing mitochondrial activity in inflammation-induced neurodegeneration, supporting further studies that aim at finding drugs to target this pathway in neurons.

Materials and methods

Key resources table
Reagent type
(species) or resource		DesignationSource or referenceIdentifiersAdditional
information
AntibodyAlexa Fluor 488
α-chicken
(donkey polyclonal)		JacksonRRID: AB_2340375IHC (1:800)
AntibodyAlexa Fluor 647
α-rabbit
(donkey polyclonal)		JacksonRRID: AB_2752244IHC (1:800)
Antibodyα-NeuN
(chicken polyclonal)		MilliporeRRID: AB_11205760IHC (1:500)
Antibodyα-HuC/HuD
(mouse monoclonal)		Thermo ScientificRRID: AB_221448IHC (1:500)
Antibodyα-Mouse IgG
(goat polyclonal)		JacksonRRID: AB_2338476IHC (1:200)
AntibodyCy3 α-mouse
(donkey polyclonal)		JacksonRRID: AB_2340816IHC (1:800)
Antibodyα-FLAG (Clone M2)
(mouse monoclonal)		SigmaRRID: AB_259529IHC (1:200)
Antibodyα-SMI31
(mouse monoclonal)		CovanceRRID: AB_10122491IHC (1:500)
Antibodyα-SMI32
(mouse monoclonal)		CovanceRRID: AB_2564642IHC (1:500)
Antibodyα-PGC-1α
(rabbit polyclonal)		NovusRRID: AB_1522118IHC (1:100)
ICC (1:200)
WB (1:2000)
Antibodyα-MAP2
(chicken polyclonal)		AbcamRRID: AB_2138153ICC (1:2500)
Antibodyα-CD3 (clone SP7)
(rabbit monoclonal)		AbcamRRID: AB_443425IHC (1:100)
Antibodyα-Mac3 (clone M3/84)
(rat monoclonal)		BD BiosciencesRRID: AB_394780IHC (1:100)
Antibodyα-Iba1
(rabbit polyclonal)		WakoRRID: AB_839504IHC (1:100)
Antibodyα-Phosphorylated PGC-1αS570
(rabbit polyclonal)		R and D SystemsRRID: AB_10890391WB (1:1000)
Antibodyα-ß-Actin
(rabbit polyclonal)		Cell Signaling TechnologyRRID: AB_330288WB (1:1000)
AntibodyHRP α-rabbit
(goat polyclonal)		LI-COR BiosciencesRRID: AB_2721264WB (1:15,000)
Antibodyα-CD45-APC/Cy7 (clone 30F11)
(rat monoclonal)		BioLegendRRID: AB_312981FACS (1:100)
Antibodyα-CD4-FITC (clone GK1.5)
(rat monoclonal)		BioLegendRRID: AB_312691FACS (1:100)
Antibodyα-CD3-BV605 (clone 17A2)
(rat monoclonal)		BioLegendRRID: AB_2562039FACS (1:300)
Antibodyα-CD8-Pacific Blue (clone 53–6.7)
(rat monoclonal)		BioLegendRRID: AB_493425FACS (1:100)
Antibodyα-CD19-PE/Cy7 (clone 1D3)
(rat monoclonal)		BDRRID: AB_10894021FACS (1:100)
Antibodyα-CD19-BV605 (clone 6D5)
(rat monoclonal)		BioLegendRRID: AB_11203538FACS (1:100)
Antibodyα-Ly6G-BV711 (clone 1A8)
(rat monoclonal)		BDRRID: AB_2738520FACS (1:100)
Antibodyα-F4/80-BV421 (clone T45-2342)
(rat monoclonal)		BDRRID: AB_2734779FACS (1:100)
Antibodyα-NK1.1- PE/Cy7 (clone PK136)
(rat monoclonal)		BioLegendRRID: AB_389364FACS (1:300)
Antibodyα-CD11b-FITC (clone M1/70)
(rat monoclonal)		BioLegendRRID: AB_312789FACS (1:300)
Antibodyα-Fc-Block (true stain anti-mouse CD16/32, clone 93)
(rat monoclonal)		BioLegendRRID: AB_1574975FACS (1:1000)
Chemical compound, drugMycobacterium tuberculosisBD DifcoBD 231141
Chemical compound, drugFreund’s adjuvantDifco LaboratoriesCat. number: 263910
Chemical compound, drugDynabeads MyOne Streptavidin T1InvitrogenCat. number:
65601
Chemical compound, drugCytochrome c from bovine heartSigma-AldrichCAS number:9007-43-6
Chemical compound, drugBis(2-amino-phenoxy) ethane tetraacetic acidThermo ScientificCAS number: 126150-97-8
Chemical compound, drug3,3-Diaminobenzidine (DAB),≥98%Sigma-AldrichCat. number: D8001
Chemical compound, drugFluo-4, AMInvitrogenCat. number: F14201
Commercial assay or kitBicinchoninic acid (BCA) kit for protein determinationSigma-AldrichCat. number:
BCA1
Commercial assay or kitSeahorse XF cell mito stress test kitAgilentPart number: 103015-100
Commercial assay or kitCalcein-AMSigma-AldrichCAS number: 14850434–1
Commercial assay or kitultraView Universal DAB detection kitVentanaCat. number:
760-500
Commercial assay or kitTMRE-mitochondrial membrane potential assay kitAbcamAb113852
Genetic reagent (Mus musculus)C57BL/6J wild-type miceJackson LaboratoryStock #: 000664
RRID: MGI:2159769
Genetic reagent (M. musculus)Thy1-Flag-Ppargc1aPMID: 21984601Dr. Dan Lindholm (Minerva Medical Research Institute)
Genetic reagent (M. musculus)Eno2Cre+Jackson LaboratoryStock #: 006663
RRID: MGI:2177175
Genetic reagent (M. musculus)Ppargc1aflx/flxJackson LaboratoryStock #: 009666
RRID: MGI:5576884
Genetic reagent (M. musculus)Chat-L10a-eGFPJackson LaboratoryStock #: 030250
RRID: MGI:5496680
OtherBolt 4–12% Bis-Tris Plus GelInvitrogenCat. number: NW04120BOX
Peptide, recombinant proteinMOG35-55 peptidePeptides and elephantsOrder number: EP02030_1
Sequence-based reagentActbThermo ScientificMm_00607939_s1
Sequence-based reagentAlasThermo ScientificMm01235914_m1
Sequence-based reagentCoq9Thermo ScientificMm00804236_m1
Sequence-based reagentCox2IThermo ScientificMm03294838_g1
Sequence-based reagentCox4iIThermo ScientificMm01250094_m1
Sequence-based reagentCox5aThermo ScientificMm00432638_m1
Sequence-based reagentCox5bThermo ScientificMm01229713_g1
Sequence-based reagentCox6a1Thermo ScientificMm01612194_m1
Sequence-based reagentCsThermo ScientificMm00466043_m1
Sequence-based reagentCyc1Thermo ScientificMm00470540_m1
Sequence-based reagentIscuThermo ScientificMm02342800_g1
Sequence-based reagentNdufa10Thermo ScientificMm00600325_m1
Sequence-based reagentNdufa12Thermo ScientificMm01240336_m1
Sequence-based reagentNdufa8Thermo ScientificMm00503351_m1
Sequence-based reagentNdufb6Thermo ScientificMm07294890_m1
Sequence-based reagentNdufs8Thermo ScientificMm00523063_m1
Sequence-based reagentNrf1Thermo ScientificMm01135606_m1
Sequence-based reagentPpargc1aThermo ScientificMm00464452_m1
Sequence-based reagentSdhaThermo ScientificMm01352366_m1
Sequence-based reagentSdhbThermo ScientificMm00458272_m1
Sequence-based reagentSdhdThermo ScientificMm00546511_m1
Sequence-based reagentSlc25a12Thermo ScientificMm00552467_m1
Sequence-based reagentSod2Thermo ScientificMm01313000_m1
Sequence-based reagentTbpThermo ScientificMm01277042_m1
Sequence-based reagentTfamThermo ScientificMm00447485_m1
Sequence-based reagentUqcr10Thermo ScientificMm01186961_m1
Sequence-based reagentUqcrbThermo ScientificMm01615741_gH
Sequence-based reagentPpargc1aThermo ScientificMm00164544_cn, FAM
Sequence-based reagentTfrcThermo ScientificTaqMan Copy Number reference Assay for mouse, VIC
Transfected construct (synthetic)pAAV.Syn.GCaMP6f.WPRE.SV40AbcamRRID: Addgene_100837Cytosolic calcium indicator

Mice

All mice (C57BL/6J wild type [The Jackson Laboratory, Bar Harbor, USA], Thy1-Flag-Ppargc1a [Thy1-Ppargc1a] mice on a C57BL/6J genetic background provided by D. Lindholm [Mudò et al., 2012], Eno2Cre+ mice [The Jackson Laboratory], Ppargc1aflx/flx mice [The Jackson Laboratory], and Chat-L10a-eGFP mice [Heiman et al., 2008]) were kept under specific pathogen-free conditions in the central animal facility of the University Medical Center Hamburg-Eppendorf, Hamburg, Germany. Neuron-specific knockout mice were generated by crossing Ppargc1aflx/flx with Eno2Cre+ mice. Mice were grouped housed in a facility with 55–65% humidity at 24 ± 2°C with a 12 hr light/dark cycle and had free access to food and water. Sex- and age-matched adult animals (10–20 weeks of age) were used in all experiments. Wild-type mice were allocated to EAE or healthy group by cages. All procedures were carried out in accordance with the ARRIVE guidelines (Kilkenny et al., 2010).

EAE induction

Request a detailed protocol

Mice were anesthetized with isoflurane 1–2% v/v oxygen and immunized subcutaneously with 200 μg myelin oligodendrocyte glycoprotein 35–55 (MOG35–55) peptide (peptides and elephants) in complete Freund’s adjuvant (BD) containing 4 mg/ml Mycobacterium tuberculosis (BD, Franklin Lakes, USA). A 200 ng pertussis toxin (Merck, Darmstadt, Germany) was injected intravenously on the day of immunization and 48 hr later. Animals were scored daily for clinical signs by the following system: 0, no clinical deficits; 1, tail weakness; 2, hind limb paresis; 3, partial hind limb paralysis; 3.5, full hind limb paralysis; 4, full hind limb paralysis and forelimb paresis; and 5, premorbid or dead. Animals reaching a clinical score ≥4 were sacrificed according to the regulations of the Animal Welfare Act. The experimenters were blinded to the genotype until the end of the experiment, including data analysis. Sex- and age-matched adult animals (8–12 weeks of age) were used in all experiments. For Thy1-Ppargc1a mice and wild-type controls, three independent EAE experiments were conducted, and the data were pooled for final analysis. For Ppargc1aflx/flx × Eno2Cre+ EAE and Ppargc1aflx/flx controls, one EAE was conducted. For analysis of the disease course and weight, we only included animals that received a score ≥1 until day 15 and survived until the end of the experiment. Animals were either analyzed at acute stage of EAE (day 12 to day 16 after immunization) or chronic stage (day 30 to day 40 after immunization).

Gene set enrichment analysis

Request a detailed protocol

Gene signature of motor neurons during CNS inflammation was generated by ranking all expressed genes by the DESeq2-derived t statistics based on the comparison of healthy versus EAE motor neurons (Schattling et al., 2019). Enrichment analysis was performed using the function ‘gseGO’ of the R package clusterProfiler (Yu et al., 2012) on biological process GO terms with at least 50 members. Gene sets with a Benjamini–Hochberg adjusted p value <0.05 were considered significant and ordered by their normalized enrichment score, with positive values indicating enrichment and negative values indicating de-enrichment. Core enrichment genes driving the de-enrichment of the term ‘GO:0022900 electron transport chain’ were extracted from clusterProfiler results and plotted as heatmap of gene expression values after variance stabilizing transformation. Plotting was performed with the R packages ggplot2 (https://ggplot2.tidyverse.org; Wickham, 2016), clusterProfiler and tidyheatmaps (Engler, 2020).

Ppargc1a target identification

Request a detailed protocol

For the identification of Ppargc1a-induced downstream targets, a published microarray dataset of Ppargc1a-overexpressing SH-SY5Y cells was used (Lucas et al., 2014). The expression matrix was downloaded via GEOquery (GSE100341) and analyzed using limma. Genes with a false discovery rate (FDR)-adjusted p value <0.05 and a log2 fold change >1 in Ppargc1a overexpression were considered positively regulated Ppargc1a targets.

Mouse tissue preparation and histopathology of mice

Request a detailed protocol

Mice were anesthetized intraperitoneally with 100 µl solution (10 mg/ml esketamine hydrochloride [Pfizer, New York City, USA], 1.6 mg/ml xylazine hydrochloride [Bayer, Leverkusen, Germany] dissolved in water) per 10 g of body weight. For histopathology and immunohistochemistry, mice were perfused with 4% paraformaldehyde (PFA), cervical spinal cord tissue was dissected, fixed for 30 min with 4% PFA, and then transferred to 30% sucrose in phosphate-buffered saline (PBS) at 4°C. Cervical spinal cord was used as it was shown that clinical scores correlate better with cervical than lumbar spinal cord lesions in EAE (Fournier et al., 2017). We transversely cut midcervical spinal cord sections at 12 μm with a freezing microtome (Leica Jung CM3000) and stored them in cryoprotective medium (Jung) at –80°C. For transmission electron microscopy, mice were perfused with 4% PFA, 1% glutaraldehyde (GA) in 0.1 M phosphate buffer, and spinal cords were dissected and fixed with 2% PFA, 2.5% GA in 0.1 M phosphate buffer. Antibodies directed against CD3 (rabbit IgG, Abcam, Cambridge, UK; RRID: AB_443425), Mac-3 (rat IgG, BD Biosciences; RRID: AB_394780), and Iba1 (rabbit, Wako, Richmond, USA; RRID: AB_839504) were visualized by avidin-biotin technique with 3,3-diaminobenzidine (DAB, Sigma, St.Louis, USA) according to standard procedures of the UKE Mouse Pathology Facility using the ultraView Universal DAB Detection Kit (Ventana, Oro Valley, USA). For complex IV/cytochrome c oxidase (COX) histochemistry, sections were incubated for 60 min at 37°C with COX reaction media (diaminobenzidine tetrahydrochloride, cytochrome c and bovine catalase [all from Sigma] in 0.2 M phosphate buffer) and embedded with Aquatex (Merck). Images of tissue sections at ×200 magnification were scanned using Zeiss MIRAX MIDI Slide Scanner (Carl Zeiss, MicroImaging GmbH, Germany). CD3+, IBA1+, and MAC-3+ infiltrating cells were analyzed with ImageJ software (NIH, Bethesda, USA), with the same settings across all experimental groups. CD3+ cells were counted in entire spinal cord sections. For IBA1+ and MAC3+ quantification, we calculated the percentage of the positively stained area in the spinal cord. For quantification of complex IV activity, background was subtracted and mean gray value intensities of COX reactivity in complete gray matter or the ventral horn were measured by ImageJ. COX reactivity was normalized to corresponding neuronal count.

Immunohistochemistry (IHC)

Request a detailed protocol

Sections were incubated in blocking solution (10% normal donkey serum in PBS) containing 0.05% Triton X-100 at room temperature for 45 min, incubated in 0.05% Triton X-100 containing a Fab Fragment anti-mouse IgG (goat, 1:200; Jackson; RRID: AB_2338476) for 60 min in case of staining anti-mouse primary antibodies to prevent unspecific binding, and subsequently stained them overnight at 4°C with antibodies against the following structures: phosphorylated neurofilaments (SMI 31, mouse, 1:500; Covance, Princeton, USA; RRID: AB_10122491), non-phosphorylated neurofilaments (SMI 32, mouse, 1:500; Covance; RRID: AB_2564642), neuronal nuclei (NeuN, chicken 1:500; Millipore, Burlington, USA; RRID: AB_11205760), neuronal protein (HuC/D, mouse 1:500; Thermo Fisher Scientific, Waltham, USA; RRID: AB_221448), PGC-1α (PGC-1α, rabbit, 1:100; Novus Biologicals, Littleton, USA; RRID: AB_1522118), and FLAG sequence (FLAG, mouse, 1:200; Sigma; RRID: AB_259529). As secondary antibodies we used Alexa Fluor 488-coupled donkey antibodies recognizing chicken IgG (1:800, Jackson; RRID: AB_2340375), Cy3-coupled donkey antibodies recognizing mouse IgG (1:800, Jackson; RRID: AB_2340816), and Alexa Fluor 647-coupled donkey antibodies recognizing rabbit IgG (1:800, Abcam; RRID: AB_2752244). We analyzed the sections with a Zeiss LSM 700 confocal microscope. For quantification of neurons and axons, tile scans of each animal were taken. Numbers of neurons were manually counted in two defined areas (250 µm × 250 µm) from the central gray matter or ventral horn. For absolute numbers of neurons in the ventral horn, all neurons in both ventral horns per animals were counted. Axons were quantified in three defined areas (90 µm × 90 µm) with ImageJ software using fixed threshold intensity across experimental groups for each type of tissue examined.

Immunocytochemistry (ICC) 

Request a detailed protocol

Cortical neurons (DIV14) were fixed with 4% PFA for 30 min at room temperature, permeabilized with 0.05% Triton and blocked with 10% normal donkey serum in PBS. Cells were stained overnight at 4°C with antibodies directed against PGC-1α (PGC-1α, rabbit, 1:200; Novus Biologicals; RRID: AB_1522118) and microtubule-associated protein 2 (MAP2, chicken, 1:2500; Abcam; RRID: AB_2138153). As secondary antibodies we used Alexa Fluor 488-coupled donkey antibodies recognizing chicken IgG (1:800, Jackson; RRID: AB_2340375), Cy3-coupled donkey antibodies recognizing mouse IgG (1:800, Jackson; RRID: AB_2340816), and Alexa Fluor 647-coupled donkey antibodies recognizing rabbit IgG (1:800, Abcam; RRID: AB_2752244). Images were taken with a Zeiss LSM 700 confocal microscope.

Transmission electron microscopy

Request a detailed protocol

After dissection and fixation, spinal cords were cut in 1 mm transverse sections and post-fixed in 1% osmium tetroxide (OsO4) for 1 hr. Hereafter, samples were dehydrated in serial dilutions of alcohol and embedded with Epon (LX‐112 Resin, Ladd Research, USA). Next, ultrathin (50‐85 nm) sections were collected on 200 mesh copper grids and counterstained with uranyl acetate and lead citrate. Images of individual motor neurons were taken at ×5000 magnification using a JEM 1010 transmission electron microscope (JEM 1010, Jeol USA). From each motor neuron, we quantified the surface area of neuronal cytoplasm and all cytoplasmic mitochondria using ImageJ (NIH), which were used to calculate mitochondrial density per neuron.

Primary neuronal culture

Request a detailed protocol

Primary hippocampal and cortical cultures were prepared from E16.5 embryos. Hippocampus and cortex were harvested, cut into smaller pieces, and incubated in 0.05% Trypsin-EDTA (Gibco) for 6 min at 37°C. Trypsination was stopped by DMEM-F12 containing 10% fetal calf serum (FCS). Afterwards, tissue was dissociated in Hank's balanced salt solution (HBSS) and centrifuged for 2 min at 500 × g. The pellet was resuspended in primary growth medium (PGM), and cells were plated at 5 × 104 per cm2 (hippocampal neurons) or 7.5 × 104 per cm2 (cortical neurons) on poly-d-lysine-coated cell culture plates. We maintained cells in PNGM (Primary Neuron Growth Medium BulletKit, Lonza) or neurobasal plus medium (supplemented with B27, penicillin, streptomycin and L-glutamine; Gibco) at 37°C, 5% CO2, and a relative humidity of 98%. To inhibit glial proliferation, we added cytarabine (Sigma, 5 µM) or floxuridine/uridine (Sigma, 10 µM) at 3–4 days in vitro (DIV) at 5 μM and maintained cultures for 14–21 days in vitro (DIV14 or DIV21).

Calcium imaging

Request a detailed protocol

Cortical primary neurons were transduced with genetically encoded cytosolic calcium indicator GCaMP6f (Addgene 100837) via adeno-associated virus (AAV7) at day 7 in vitro. Calcium imaging was performed from cPNC from E16.5 embryos (DIV15). Alternatively, chemical calcium indicator Fluo4-AM (Thermo Fisher, F14201) was used according to the manufacturer’s instructions. Briefly, cultures were incubated in optic buffer (ddH2O supplemented with 10 mM glucose, 140 mM NaCl, 1 mM MgCl2, 5 mM KCl, 20 mM HEPES, 2 mM CaCl2, pH 7.3 adjusted with NaOH) with 4 µM Fluo4-AM for 30 min at room temperature, washed and imaged in optic buffer. Live cell imaging was performed at day 15 in vitro with a Zeiss LSM 700 confocal microscope, 20× magnification (objective) and temporal resolution of 478 ms. Transduced cultures were placed to incubation chamber (33°C, 5% CO2) 20 min prior to imaging and then recorded. Ionomycin (8 µM) was added at the end of each experiment for signal normalization and cultures were imaged for another 15 min to allow development of maximum signal intensity. Additional normalization with optic buffer without Ca2+ and with 50 µM BAPTA-AM (Thermo Fisher) was done for the experiments using Fluo4-AM as calcium indicator to determine absolute calcium concentration (Yasuda et al., 2004). The 478 s of spontaneous Ca2+ transients before application of ionomycin were used for analysis. Time-series images were generated for further analysis. Manual image segmentation and generation of traces of the intensity from the single neuronal soma was performed in ImageJ. Normalization, calcium spikes, base line detection, and analysis of the calcium clearance rate were performed with the custom-made script written on Python 3.6 (https://github.com/scriptcalcium/PGC1alpha; Rosenkranz, 2021; copy archived at swh:1:rev:af3c206a43f8b6e9fdbd7707c9b4601287d5968b) (Shaposhnykov, 2020). Clearance time (T), amplitude (A), mean signal intensity (Mean), and numbers of calcium transients = firing rate (N) were defined as follows:

Ni=nif(F(n)≥F(a),wherea∈[n−δ…n+δ])and(F(n)>1+γ2θ+1∑j=n−θn+θF(j)),

Firing rate (Ni): N: calcium transient count; F: normalized signal intensity; i and n: indexes for defined frame (n) and cell (i); parameters δ, θ, and γ were set as 3, 7, and 0.25 for all analyzed cultures by manual assessment of effective pick detection >80%

Ti=1ki∑j=1kiTj

Clearance time (Ti): clearance time for the ith cells with detected k transients, from maximum to half of the amplitude

Ai=1ki∑j=1kiFpij

Amplitude (Ai): amplitude for the ith cell with detected k transients; Fpi – amplitude of individual transients of ith cell

Meana,b=1b-a∑j=abF(j)

Mean[a,b]: mean of recorded signal from frame a till frame b, F(j) – signal extracted from jth frame; parameters a and b were determined for each experiment individually for each experiment with a constant analyzed interval; last frame was recorded before ionomycin addition.

Live cell metabolic assay

Request a detailed protocol

Hippocampal neurons were seeded in a poly-d-lysine-coated XF 96-well cell culture microplate (Seahorse Bioscience, Copenhagen, Denmark) in triplicate at 5 × 105 cells per well in 1 ml neuronal growth medium and then incubated at 37°C in 5% CO2. At DIV14, media from neurons was removed, replaced by 180 µl of assay media (Assay Media from Bioscience with 25 mM glucose, 1 mM sodium pyruvate, and 2 mM L-glutamine; pH 7.4), and incubated in a CO2-free incubator at 37°C for 1 hr. Compounds (2 µM oligomycin, 1 µM FCCP, 0.5 µM rotenone/antimycin A, all in assay media) were added into the appropriate ports of a hydrated sensor cartridge. For baseline measurement, control ports were left without compound addition. Cell plate and cartridge were then placed into the XFe96 Analyzer and results analyzed by WAVE Software.

Cell viability assay (calcein)

Request a detailed protocol

Hippocampal neurons were seeded in a poly-d-lysine-coated XF 96-well cell culture microplate (Seahorse Bioscience, Copenhagen, Denmark) in quadruplicate at 5 × 105 cells per well in 1 ml neuronal growth medium and then incubated at 37°C in 5% CO2. At DIV14, calcein-AM (Sigma) was added to the cultures (2 µM), incubated at 37°C in 5% CO2 for 30 min, washed in optic buffer (ddH2O supplemented with 10 mM glucose, 140 mM NaCl, 1 mM MgCl2, 5 mM KCl, 20 mM HEPES, 2 mM CaCl2, pH 7.3 adjusted with NaOH) at room temperature, and fluorescence intensity was measured according to the manufacturer’s instructions.

Mitochondrial membrane potential assay

Request a detailed protocol

Primary neurons were seeded on µ-dishes (ibidi) and TMRE assay (Abcam) was performed on DIV14 according to the manufacturer’s protocol. Cells were treated with 10 nM TMRE for 20 min in PGM, washed twice with HBSS w/o phenol red (Gibco), and then equilibrated for 10 min in HBSS w/o phenol red and live imaged at 555 nm with a Zeiss LSM 700 confocal microscope. For internal control, 20 µM FCCP was added 10 min prior to TMRE. Images were taken with a Zeiss LSM 700 confocal microscope. Total fluorescence of five cells per biological replicate was analyzed.

Translating ribosome affinity purification

Request a detailed protocol

Chat-L10a-eGFP mice were anesthetized with ketamin/xylazin and perfused with 10 ml ice-cold dissection buffer (1 × HBSS, 2.5 mM HEPES-KOH pH 7.4, 35 mM glucose, 4 mM NaHCO3) over 1 min. Cervical spinal cords were dissected in ice-cold dissection buffer containing 100 µg/ml cycloheximide, and three spinal cords were pooled for homogenization in lysis buffer (20 mM HEPES-KOH pH 7.4, 150 mM KCl, 5 mM MgCl2, 0.5 mM dithiothreitol, 100 µg/ml cycloheximide, 40 U/ml Rnasin, 20 U/ml Superasin) using a glass homogenizer. Homogenates were centrifuged at 2000 × g for 10 min at 4°C to remove large cell debris, supernatant was transferred to a new tube, and NP-40 detergent solution (Thermo Fisher Scientific) and 1,2-diheptanoyl-sn-glycero-3-phosphocholine (Avanti Polar Lipids) were added to final concentrations of 1% and 30 mM, respectively. After 5 min incubation on ice, lysates were centrifuged at 20,000 × g for 10 min at 4°C. Ten percent of the supernatant was saved as input control, the remaining 90% were incubated with monoclonal GFP antibody (Htz-GFP19C8 and Htz-GFP19F7; Memorial Sloan Kettering Cancer Center Monoclonal Antibody Core Facility)-coated magnetic beads (Streptavidin MyOne T1 Dynabeads, Invitrogen pre-coupled to biotinylated Protein L, Pierce) with end-over-end rotation overnight at 4°C. Beads were subsequently collected on a magnetic rack, washed four times with high-salt wash buffer (20 mM HEPES, 350 mM KCl, 5 mM MgCl2, 0.5 mM, 1% NP-40, 0.5 mM dithiothreitol, 100 μg/ml cycloheximide), and immediately subjected to Trizol/chloroform-based RNA extraction (Invitrogen). RNA was precipitated with sodium acetate and Glycoblue (Ambion, Thermo Fisher Scientific) in isopropanol overnight at –80°C, washed twice with 70% ethanol, resuspended in water, and further purified using the RNeasy Micro Kit (Qiagen, Venlo, The Netherlands) with on-column DNaseI digestion. For higher RNA yields, all steps were carried out in non-stick microfuge tubes (Ambion).

Fluorescence-activated cell sorting (FACS)

Request a detailed protocol

Mice were anesthetized and killed by inhalation of CO2, and spleens were excised with sterile instruments and collected in ice-cold PBS. Pooled single-cell suspensions from spleen were prepared by homogenization through a 40 μm cell strainer. Cells were pelleted by centrifugation (300 × g, 10 min, 4°C), and lysis of splenic erythrocytes was initiated by RBC lysis buffer (0.15 M NH4Cl, 10 mM KHCO3, 0.1 mM Na2EDTA, pH = 7.4) for 2.5 min at 4°C and stopped with MACS buffer (PBS, 0.5% bovine serum albumin (BSA), 2 mM EDTA). For fluorescence-activated cell sorting, cells were washed with PBS and stained with Fixable Viability Stain 700 (BD Biosciences) in PBS for 20 min at 4°C in the dark to exclude dead cells. Cell suspension was washed in PBS. Cells were stained with CD45-APC/Cy7 (clone 30F11, BioLegend; RRID: AB_312981), CD3-BV605 (clone 17A2, BioLegend; RRID: AB_2562039), CD4-FITC (clone GK 1.5, BioLegend; RRID: AB_312691), CD8-Pacific Blue (clone 53-6.7, BioLegend; RRID: AB_493425), and CD19-PE/Cy7 (clone 1D3, BD; RRID:AB_10894021) or with CD45-APC/Cy7 (clone 30F11, BioLegend; RRID: AB_312981), CD3-BV605 (clone 17A2, BioLegend; RRID: AB_2562039), CD19-BV605 (clone 6D5, BioLegend; RRID: AB_11203538), Ly6G-BV711 (clone 1A8, BD; RRID:AB_2738520), F4/80-BV421 (clone T45-2342, BD; RRID: AB_2734779), NK1.1- PE/Cy7 (clone PK136, BioLegend; RRID: AB_389364), and CD11b-FITC (clone M1/70, BioLegend; RRID: AB_312789) in FACS buffer (PBS, 0.5% BSA, 0.02% NaN3) supplemented with Fc-Block (True Stain anti-mouse CD16/32, clone 93; BioLegend; RRID: AB_1574975) for 30 min at 4°C in the dark, washed with PBS, and filtered and resuspended in PBS with 10 µM EDTA. CD4+ T cells (CD45+CD3+CD4+), CD8+ T cells (CD45+CD3+CD8+), B cells (CD45+CD19+), and macrophages (CD45+CD3-CD19- Ly6G-NK1.1-CD11b+ F4/80+) were sorted into collection tubes coated with FCS and filled with complete RPMI 1640 medium (1% penicillin, streptomycin, 0.1% 2-ME) with 20% FCS using a FACSAria device (BD Biosciences). Purity of sorted populations was routinely above 95%. Then cells were pelleted by centrifugation at 300 × g for 10 min at 4°C, dry frozen in liquid nitrogen, and stored at –80°C until RNA isolation. For isolation of microglia, mice were intracardially perfused with ice-cold PBS immediately after killing by inhalation of CO2 to remove blood from intracranial vessels. Brain and spinal cord were excised with sterile instruments, and mechanically dissected and incubated with agitation in RPMI medium 1640 (PAN-Biotech) containing 1 mg/ml collagenase A (Roche) and 0.1 mg/ml DNaseI (Merck) for 60 min at 37°C. Tissue was triturated through a 40 μm cell strainer and washed with PBS (300 × g, 10 min, 4°C). Homogenized tissue was resuspended in 30% isotonic Percoll (GE Healthcare, Chicago, USA) and underlaid with 78% isotonic Percoll. After gradient centrifugation (1500 × g, 30 min, 4°C), CNS-immune cells were recovered from the gradient interphase and washed twice in ice-cold PBS. For fluorescence-activated cell sorting, cell suspensions were stained with Fixable Viability Stain 700, followed by staining of surface antigens with CD45-APC/Cy7 (clone 30F11, Biolegend; RRID: AB_312981), and CD11b-PerCPCy5.5 (clone M1/70, Biolegend; RRID: AB_312981) and Fc-Block as described above. Microglia (CD45med CD11b+) were sorted and processed for RNA isolation as described above.

Quantitative real-time PCR

Request a detailed protocol

RNA was purified using the RNeasy Mini Kit (Qiagen) and reverse transcribed to cDNA with the RevertAid H Minus First Strand cDNA Synthesis Kit (Thermo Fisher) according to the manufacturer’s instructions. Gene expression was analyzed by quantitative real-time PCR performed in an ABI Prism 7900 HT Fast Real-Time PCR System (Applied Biosystems, Waltham, USA) using TaqMan Gene Expression Assays (Thermo Fisher) for Ppargc1a (Pgc-1α; Mm00464452_m1), Alas (Mm01235914_m1), Cox4i1 (Mm01250094_m1), Cs (Mm00466043_m1), Nrf1 (Mm01135606_m1), Tfam (Mm00447485_m1), and Tbp (Mm01277042_m1). Gene expression was calculated as 2−ΔCT relative to Tbp as endogenous control.

mtDNA copy number quantification

Request a detailed protocol

DNA was purified using the DNeasy Blood and Tissue Kit (Qiagen) according to the manufacturer’s instructions. To destroy RNA, samples were treated with RNAse (Qiagen). Mitochondrial DNA copy numbers relative to diploid chromosomal DNA content were analyzed by quantitative real-time PCR performed in an ABI Prism 7900 HT Fast Real-Time PCR System (Applied Biosystems) using TaqMan Gene Expression Assays (Thermo Fisher) for Cox2I (Mm03294838_g1) and β-actin (Mm_00607939_s1). MtDNA copy numbers were quantified as 2−ΔCT relative to β-actin.

Ppargc1a DNA copy number quantification

Request a detailed protocol

DNA was lysed with 50 µl QuickExtract DNA Extraction Solution (Lucigen, Middleton, USA). Copy numbers were analyzed by quantitative real-time PCR performed in an ABI Prism 7900 HT Fast Real-Time PCR System (Applied Biosystems) using TaqMan Copy Number Assays (Thermo Fisher) for Ppargc1a (Pgc-1α, Mm00164544_cn, FAM) and Tfrc (TaqMan Copy Number reference assay for mouse, VIC) in a duplex PCR. Ppargc1a copy numbers were quantified as 2−ΔCT relative to Tfrc.

Immunoblot (Western Blot, WB)

Request a detailed protocol

After dissection, 15 mg of mouse spinal cord were lysed in 500 μl RIPA buffer supplemented with 50× protease inhibitor (complete, Sigma-Aldrich) and 2% 50x phosphatase (PhosSTOP, Sigma Aldrich), homogenized and incubated at 4°C for 30 min on a rotating wheel (GLW Storing Systems GmbH), and subsequently centrifuged at 3200 × g for 5 min at 4°C. Respective total protein amount of supernatant was determined with bicinchoninic acid protein assay. Only for validation of antibodies lambda protein phosphatase (New England BioLabs Inc, Ipswich, USA) was added to the samples. Samples were denatured by boiling 12 μg protein in a mixture of 15 μl 4× NuPAGE sample buffer (novex), 6 μl 10× Bolt sample reducing agent (novex), and RIPA buffer in a total volume of 60 μl for 5 min at 95°C. Samples were then loaded onto a Bolt 4–12% Bis-Tris Plus Gel (Invitrogen) in a Mini Gel Tank (Life Technologies, Carlsbad, USA). Chambers were filled with 1× Bolt MOPS SDS running buffer. Proteins were separated at 165 V for 1 hr. Spectra Multicolor High Range Protein Ladder (Thermo Fisher) was used as a protein standard. Proteins were then transferred to a nitrocellulose membrane at 10 V for 1 hr. For immunodetection, membrane was first blocked in 5% BSA in 1× TBS-T for 1 hr at room temperature and then incubated overnight at 4°C on a rotation wheel in 5% BSA in 1× TBS-T containing the primary antibodies against the following proteins: PGC-1α (rabbit 1:2000; Novus Biologicals; RRID: AB_1522118), phosphorylated PGC-1αS570 (rabbit 1:1000; R and D Systems, Minneapolis, USA; RRID: AB_10890391), and β-actin (rabbit 1:1000; Cell Signaling Technology, Cambridge, UK; RRID: AB_330288). The membrane was washed thrice with 1× TBS-T for 5 min each and subsequently incubated for 1 hr at RT on a shaker in 5% BSA in 1× TBS-T supplemented with a horseradish-peroxidase conjugated secondary antibody (goat 1:15000; LI-COR Biosciences, Lincoln, USA; RRID: AB_2721264). The membrane was washed again three times in 1× TBS-T at RT for 5 min. Bound antibodies were detected by chemiluminescence using the WesternSure ECL Substrate (LI-COR Biosciences) using the CCD camera-based ImageQuant LAS 4000 Mini system (GE Healthcare).

Statistics

No statistical methods were used to predetermine sample sizes; our sample sizes are similar to those reported in previous publications (Schattling et al., 2019; Schattling et al., 2012). Experimental data were analyzed using Prism 8 software (GraphPad) and are presented as mean values ± s.e.m. Statistical analyses were performed using the appropriate test indicated in the figure legends. Outlier identification was first performed via Grubb’s test with p=0.05. Shapiro–Wilk test was used to analyze normality. In normally distributed data, differences between two experimental groups were determined by unpaired, two-tailed Student's t-test; in non-normally distributed data, we tested differences between two experimental groups by unpaired, two-tailed Mann–Whitney test. Differences between three experimental groups were determined by multiple comparisons test following one-way ANOVA. Significant results are indicated by asterisk: *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Data availability

All current data have been provided. All individual data are reported within the figure graphs as scatter plots. Numerical data for Figure 3E, 5A and 5C can be found as Source Data. Enrichment analysis was performed using the function 'gseGO' of the R package clusterProfiler; plotting was performed with the R packages ggplot2, clusterProfiler and tidyheatmaps. Normalization, calcium spikes, base line detection, and analysis of the calcium clearance rate were performed with the custom-made script written on Python 3.6 (https://github.com/scriptcalcium/PGC1alpha; copy archived at https://archive.softwareheritage.org/swh:1:rev:af3c206a43f8b6e9fdbd7707c9b4601287d5968b/).

The following previously published data sets were used

References

    1. Adams HH
    2. Hibar DP
    3. Chouraki V
    4. Stein JL
    5. Nyquist PA
    6. Rentería ME
    7. Trompet S
    8. Arias-Vasquez A
    9. Seshadri S
    10. Desrivières S
    11. Beecham AH
    12. Jahanshad N
    13. Wittfeld K
    14. Van der Lee SJ
    15. Abramovic L
    16. Alhusaini S
    17. Amin N
    18. Andersson M
    19. Arfanakis K
    20. Aribisala BS
    21. Armstrong NJ
    22. Athanasiu L
    23. Axelsson T
    24. Beiser A
    25. Bernard M
    26. Bis JC
    27. Blanken LM
    28. Blanton SH
    29. Bohlken MM
    30. Boks MP
    31. Bralten J
    32. Brickman AM
    33. Carmichael O
    34. Chakravarty MM
    35. Chauhan G
    36. Chen Q
    37. Ching CR
    38. Cuellar-Partida G
    39. Braber AD
    40. Doan NT
    41. Ehrlich S
    42. Filippi I
    43. Ge T
    44. Giddaluru S
    45. Goldman AL
    46. Gottesman RF
    47. Greven CU
    48. Grimm O
    49. Griswold ME
    50. Guadalupe T
    51. Hass J
    52. Haukvik UK
    53. Hilal S
    54. Hofer E
    55. Hoehn D
    56. Holmes AJ
    57. Hoogman M
    58. Janowitz D
    59. Jia T
    60. Kasperaviciute D
    61. Kim S
    62. Klein M
    63. Kraemer B
    64. Lee PH
    65. Liao J
    66. Liewald DC
    67. Lopez LM
    68. Luciano M
    69. Macare C
    70. Marquand A
    71. Matarin M
    72. Mather KA
    73. Mattheisen M
    74. Mazoyer B
    75. McKay DR
    76. McWhirter R
    77. Milaneschi Y
    78. Mirza-Schreiber N
    79. Muetzel RL
    80. Maniega SM
    81. Nho K
    82. Nugent AC
    83. Loohuis LM
    84. Oosterlaan J
    85. Papmeyer M
    86. Pappa I
    87. Pirpamer L
    88. Pudas S
    89. Pütz B
    90. Rajan KB
    91. Ramasamy A
    92. Richards JS
    93. Risacher SL
    94. Roiz-Santiañez R
    95. Rommelse N
    96. Rose EJ
    97. Royle NA
    98. Rundek T
    99. Sämann PG
    100. Satizabal CL
    101. Schmaal L
    102. Schork AJ
    103. Shen L
    104. Shin J
    105. Shumskaya E
    106. Smith AV
    107. Sprooten E
    108. Strike LT
    109. Teumer A
    110. Thomson R
    111. Tordesillas-Gutierrez D
    112. Toro R
    113. Trabzuni D
    114. Vaidya D
    115. Van der Grond J
    116. Van der Meer D
    117. Van Donkelaar MM
    118. Van Eijk KR
    119. Van Erp TG
    120. Van Rooij D
    121. Walton E
    122. Westlye LT
    123. Whelan CD
    124. Windham BG
    125. Winkler AM
    126. Woldehawariat G
    127. Wolf C
    128. Wolfers T
    129. Xu B
    130. Yanek LR
    131. Yang J
    132. Zijdenbos A
    133. Zwiers MP
    134. Agartz I
    135. Aggarwal NT
    136. Almasy L
    137. Ames D
    138. Amouyel P
    139. Andreassen OA
    140. Arepalli S
    141. Assareh AA
    142. Barral S
    143. Bastin ME
    144. Becker DM
    145. Becker JT
    146. Bennett DA
    147. Blangero J
    148. van Bokhoven H
    149. Boomsma DI
    150. Brodaty H
    151. Brouwer RM
    152. Brunner HG
    153. Buckner RL
    154. Buitelaar JK
    155. Bulayeva KB
    156. Cahn W
    157. Calhoun VD
    158. Cannon DM
    159. Cavalleri GL
    160. Chen C
    161. Cheng CY
    162. Cichon S
    163. Cookson MR
    164. Corvin A
    165. Crespo-Facorro B
    166. Curran JE
    167. Czisch M
    168. Dale AM
    169. Davies GE
    170. De Geus EJ
    171. De Jager PL
    172. de Zubicaray GI
    173. Delanty N
    174. Depondt C
    175. DeStefano AL
    176. Dillman A
    177. Djurovic S
    178. Donohoe G
    179. Drevets WC
    180. Duggirala R
    181. Dyer TD
    182. Erk S
    183. Espeseth T
    184. Evans DA
    185. Fedko IO
    186. Fernández G
    187. Ferrucci L
    188. Fisher SE
    189. Fleischman DA
    190. Ford I
    191. Foroud TM
    192. Fox PT
    193. Francks C
    194. Fukunaga M
    195. Gibbs JR
    196. Glahn DC
    197. Gollub RL
    198. Göring HH
    199. Grabe HJ
    200. Green RC
    201. Gruber O
    202. Gudnason V
    203. Guelfi S
    204. Hansell NK
    205. Hardy J
    206. Hartman CA
    207. Hashimoto R
    208. Hegenscheid K
    209. Heinz A
    210. Le Hellard S
    211. Hernandez DG
    212. Heslenfeld DJ
    213. Ho BC
    214. Hoekstra PJ
    215. Hoffmann W
    216. Hofman A
    217. Holsboer F
    218. Homuth G
    219. Hosten N
    220. Hottenga JJ
    221. Hulshoff Pol HE
    222. Ikeda M
    223. Ikram MK
    224. Jack CR
    225. Jenkinson M
    226. Johnson R
    227. Jönsson EG
    228. Jukema JW
    229. Kahn RS
    230. Kanai R
    231. Kloszewska I
    232. Knopman DS
    233. Kochunov P
    234. Kwok JB
    235. Lawrie SM
    236. Lemaître H
    237. Liu X
    238. Longo DL
    239. Longstreth WT
    240. Lopez OL
    241. Lovestone S
    242. Martinez O
    243. Martinot JL
    244. Mattay VS
    245. McDonald C
    246. McIntosh AM
    247. McMahon KL
    248. McMahon FJ
    249. Mecocci P
    250. Melle I
    251. Meyer-Lindenberg A
    252. Mohnke S
    253. Montgomery GW
    254. Morris DW
    255. Mosley TH
    256. Mühleisen TW
    257. Müller-Myhsok B
    258. Nalls MA
    259. Nauck M
    260. Nichols TE
    261. Niessen WJ
    262. Nöthen MM
    263. Nyberg L
    264. Ohi K
    265. Olvera RL
    266. Ophoff RA
    267. Pandolfo M
    268. Paus T
    269. Pausova Z
    270. Penninx BW
    271. Pike GB
    272. Potkin SG
    273. Psaty BM
    274. Reppermund S
    275. Rietschel M
    276. Roffman JL
    277. Romanczuk-Seiferth N
    278. Rotter JI
    279. Ryten M
    280. Sacco RL
    281. Sachdev PS
    282. Saykin AJ
    283. Schmidt R
    284. Schofield PR
    285. Sigurdsson S
    286. Simmons A
    287. Singleton A
    288. Sisodiya SM
    289. Smith C
    290. Smoller JW
    291. Soininen H
    292. Srikanth V
    293. Steen VM
    294. Stott DJ
    295. Sussmann JE
    296. Thalamuthu A
    297. Tiemeier H
    298. Toga AW
    299. Traynor BJ
    300. Troncoso J
    301. Turner JA
    302. Tzourio C
    303. Uitterlinden AG
    304. Hernández MC
    305. Van der Brug M
    306. Van der Lugt A
    307. Van der Wee NJ
    308. Van Duijn CM
    309. Van Haren NE
    310. Van T Ent D
    311. Van Tol MJ
    312. Vardarajan BN
    313. Veltman DJ
    314. Vernooij MW
    315. Völzke H
    316. Walter H
    317. Wardlaw JM
    318. Wassink TH
    319. Weale ME
    320. Weinberger DR
    321. Weiner MW
    322. Wen W
    323. Westman E
    324. White T
    325. Wong TY
    326. Wright CB
    327. Zielke HR
    328. Zonderman AB
    329. Deary IJ
    330. DeCarli C
    331. Schmidt H
    332. Martin NG
    333. De Craen AJ
    334. Wright MJ
    335. Launer LJ
    336. Schumann G
    337. Fornage M
    338. Franke B
    339. Debette S
    340. Medland SE
    341. Ikram MA
    342. Thompson PM
    (2016) Novel genetic loci underlying human intracranial volume identified through genome-wide association
    Nature Neuroscience 19:1569–1582.
    https://doi.org/10.1038/nn.4398
    1. Kann O
    2. Kovács R
    (2007) Mitochondria and neuronal activity
    American Journal of Physiology-Cell Physiology 292:C641–C657.
    https://doi.org/10.1152/ajpcell.00222.2006
  63. Book
    1. Wickham H
    (2016)
    Ggplot2: Elegant Graphics for Data Analysis
    New York, United States: Springer-Verlag.

Decision letter

  1. Brandon K Harvey
    Reviewing Editor; NIDA/NIH, United States
  2. Satyajit Rath
    Senior Editor; Indian Institute of Science Education and Research (IISER), India

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The study by Rosenkranz and colleagues explores the relationship between mitochondrial activity and neurodegeneration associated with inflammatory lesions in the central nervous system, a scientifically and clinically important topic with direct relevance to multiple sclerosis (MS). Their results reinforce the therapeutic potential of the transcription factor PGC1α for treating neuroinflammation associated with neurodegeneration, specifically in multiple sclerosis.

Decision letter after peer review:

Thank you for submitting your article "Enhancing mitochondrial activity in neurons protects against neurodegeneration in CNS inflammation" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Satyajit Rath as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

The study by Rosenkranz and colleagues explores the relationship between mitochondrial activity and neurodegeneration in inflammatory CNS lesions, a scientifically and clinically important topic with direct relevance to multiple sclerosis. They found transcription of mitochondrial genes related to the electron transport chain (ETC) are decreased in motor neurons derived from EAE animals in line with a reduced complex IV activity that is observed throughout the spinal gray matter in EAE. They link these changes to the transcription factor PGC1α. Manipulating PGC1α levels modulated mitochondrial function and the pathological features of an EAE model in a manner consistent with PGC1α having therapeutic potential for neuroinflammation associated with neurodegeneration, specifically in multiple sclerosis.

Essential revisions:

1) The majority of lesions in most EAE models are located lumbar spinal cord and the clinical score indicates that this is also the case in this paper. However, the entire analysis was performed in the cervical spinal cord. As a result of this, the changes observed would be mostly distal to the most heavily inflamed areas of the spinal cord. It would be informative and important for the interpretation of data to know whether the key findings of the study and in particular the beneficial effects of Pgc -1α overexpression on complex IV activity and neuronal survival (in the absence of an effect on the local inflammatory reaction) are also present in the lumbar spinal cord.

2) The identification of Pgc1α as a therapeutic target is derived from the reduced expression of Pgc-1α-dependent ETC genes in the EAE spinal cord (Figure 2). If possible, it would be helpful to show that (at least some of) these changes to neuronal gene expression are reversed by Pgc-1α overexpression in the EAE spinal cord and provide further support for PGC1α as a therapeutic approach to MS.

3) NeuN is used a marker for visualizing neurons, but the expression of NeuN can be modulated, particularly under inflammatory conditions. An additional reference marker should be considered before concluding differences between EAE and control animals.

4) Both conditional Pgc-1α deficient and Pgc-1α overexpressing mice show an altered density of neurons in the GM and the ventral spinal cord in the context of EAE, confirm the neuronal densities in these areas do not differ in the corresponding age-matched healthy mice.

5) In Figure 3E, the images are labeled as "representative" but do not appear to represent the data from hPNC. A quick analysis of the images suggests 5-8 fold increase (not the 1.5 -2.5 indicated by the MFI). Are images from hPNC or cPNC? In the graph, it appears there may be a bimodal distribution of cells for hPNC from PGC1α group (possibly cPNC as well), is there any technical reason that might account for clustering? For cPNC, it appears there are a few outliers. Why was t-test used not ANOVA?

6) The authors claim that there is no difference in immune response between wild type and PGC-1αTT mice, however in Figure 5D, the micrograph for IBA does not support this claim, and appears to contradict the graph. There is clearly more IBA in the chosen tissue section from the PGC-1αTT mice, compared to wild type, but the graph suggests a decrease (although does not appear to be significant).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Enhancing mitochondrial activity in neurons protects against neurodegeneration in a mouse model of multiple sclerosis" for further consideration by eLife. Your revised article has been evaluated by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Satyajit Rath as the Senior Editor.

Essential revision:

The manuscript has been improved but there is a remaining issue that need to be addressed before acceptance, as outlined below:

The authors address the appropriateness of the model by referring to a study showing that clinical scores correlate better with cervical spinal cord lesions in EAE, however, they did not incorporate this reference nor address the concern of approach in the revised manuscript. Given this was part of essential revision, the authors should consider addressing choice of focusing on the cervical region of SC rather than lumbar in the manuscript proper.

Other concerns are adequately addressed.

https://doi.org/10.7554/eLife.61798.sa1

Author response

Essential revisions:

1) The majority of lesions in most EAE models are located lumbar spinal cord and the clinical score indicates that this is also the case in this paper. However, the entire analysis was performed in the cervical spinal cord. As a result of this, the changes observed would be mostly distal to the most heavily inflamed areas of the spinal cord. It would be informative and important for the interpretation of data to know whether the key findings of the study and in particular the beneficial effects of Pgc -1α overexpression on complex IV activity and neuronal survival (in the absence of an effect on the local inflammatory reaction) are also present in the lumbar spinal cord.

We thank the reviewers for their comment and welcome the opportunity to explain our rationale by first drawing their attention to previous findings, in which it was shown that clinical scores correlate better with cervical spinal cord lesions in EAE (Philippe et al., 2017). Moreover, in our own previous studies (Friese et al., 2007; Schattling et al., 2012, 2019; Rosenkranz et al., 2020) we characterized immune cell infiltration, demyelination and neuronal loss in the cervical spinal cord that also showed robust correlation with the clinical phenotype. While we agree that the lesions in EAE are similarly present in the lumbar spinal cord, out of the above reasons we believe that our data sufficiently back our interpretations. We hope that the reviewers regard our approach as appropriate.

2) The identification of Pgc1α as a therapeutic target is derived from the reduced expression of Pgc-1α-dependent ETC genes in the EAE spinal cord (Figure 2). If possible, it would be helpful to show that (at least some of) these changes to neuronal gene expression are reversed by Pgc-1α overexpression in the EAE spinal cord and provide further support for PGC1α as a therapeutic approach to MS.

We thank the reviewer for pointing this out and agree that this would be an interesting analysis. Thus, we performed a qPCR analysis of the PGC-1α-regulated ETC genes, which we previously detected to be downregulated in EAE, in wild-type and Thy1-Ppargc1a primary hippocampal neurons. Importantly, we could observe a significant increase of 11 out of 17 analyzed genes (new Figure 3A), supporting a therapeutic approach. We agree that analysis of these genes in neurons during EAE would have been a nice addition, however due to Corona restrictions we were limited in our breeding capacity and could not cross our Chat-L10-eGFPBactrap mice to Ppargc1a-overexpressing animals. However, we believe that our new data set sufficiently supports our claims.

3) NeuN is used a marker for visualizing neurons, but the expression of NeuN can be modulated, particularly under inflammatory conditions. An additional reference marker should be considered before concluding differences between EAE and control animals.

We agree with the reviewer that NeuN staining can be diminished in EAE, thereby compromising our analysis. Following the reviewer’s suggestion, we performed stainings with HuC/HuD, an alternative neuronal marker that now served us as reference for normalization of neuronal counts. By doing so, we revealed that the reduction of COX activity at day 30 was partly driven by neuronal loss, which is now shown in the new Figure 1G. Furthermore, we added an analysis of the EAE tissue from Thy1-Ppargc1a vs. wild-type and Ppargc1aflx/flx × Eno2cre+ vs. Ppargc1aflx/flx at day 40 post immunization by using HuC/HuD staining as neuronal marker. In comparison to the NeuN staining we detected higher overall neuronal numbers with the HuC/HuD staining in all tissues, however the significant differences between Thy1-Ppargc1a vs. wild-type and Ppargc1aflx/flx × Eno2cre+ vs. Ppargc1aflx/flx were not affected by using the different stainings (new Figure 5—figure supplement 1G and I).

4) Both conditional Pgc-1α deficient and Pgc-1α overexpressing mice show an altered density of neurons in the GM and the ventral spinal cord in the context of EAE, confirm the neuronal densities in these areas do not differ in the corresponding age-matched healthy mice.

We agree that this is an important control as the initial numbers of neurons could influence our EAE outcome measures. Therefore, we performed stainings and analyses of healthy Ppargc1aflx/flx × Eno2cre+ vs. Ppargc1aflx/flx and Thy1-Ppargc1a vs. wild-type animals and could confirm that the different genotypes did not differ in neuronal counts in the GM or VH. This is now shown in the new Figure 5—figure supplement 1H and J.

5) In Figure 3E, the images are labeled as "representative" but do not appear to represent the data from hPNC. A quick analysis of the images suggests 5-8 fold increase (not the 1.5 -2.5 indicated by the MFI). Are images from hPNC or cPNC? In the graph, it appears there may be a bimodal distribution of cells for hPNC from PGC1α group (possibly cPNC as well), is there any technical reason that might account for clustering? For cPNC, it appears there are a few outliers. Why was t-test used not ANOVA?

We thank the reviewer for this advice and replaced the representative image of the WT hPNC (new Figure 3F). As Thy1 expression shows heterogeneity this might be the explanation of the detected range of TMRE in Thy1-Ppargc1a mice. However, as indicated in the statistical methods we performed an outlier analysis and none of the data points shown were detected as outlier in our analysis. T-tests were used to compare the two groups of interest with each other – Thy1-Ppargc1a vs. wild-type.

6) The authors claim that there is no difference in immune response between wild type and PGC-1αTT mice, however in Figure 5D, the micrograph for IBA does not support this claim, and appears to contradict the graph. There is clearly more IBA in the chosen tissue section from the PGC-1αTT mice, compared to wild type, but the graph suggests a decrease (although does not appear to be significant).

We agree that the figure was not ideally chosen and replaced the representative image of the Iba1 stainings in Figure 5D.

References:

Philippe A, Quenault A, Martinez S, Lizarrondo D, Gauberti M, Defer G (2017) Prediction of disease activity in models of multiple sclerosis by molecular magnetic resonance imaging of P-selectin. PNAS 114:6116–6121.Rosenkranz SC, Shaposhnykov A, Schnapauff O, Epping L, Vieira V, Heidermann K, Schattling B, Tsvilovskyy V, Liedtke W, Meuth SG, Freichel M, Gelderblom M, Friese MA (2020) TRPV4-Mediated Regulation of the Blood Brain Barrier Is Abolished During Inflammation. Front Cell Dev Bio 8:1–9.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Essential revision:

The manuscript has been improved but there is a remaining issue that need to be addressed before acceptance, as outlined below:

The authors address the appropriateness of the model by referring to a study showing that clinical scores correlate better with cervical spinal cord lesions in EAE, however, they did not incorporate this reference nor address the concern of approach in the revised manuscript. Given this was part of essential revision, the authors should consider addressing choice of focusing on the cervical region of SC rather than lumbar in the manuscript proper.

We thank the reviewers for their positive and encouraging feedback and apologize for the oversight not addressing this point in our manuscript. We now included the following sentence in the Materials and methods section and cited the respective reference: “Cervical spinal cord was used as it was shown that clinical scores correlate better with cervical than lumbar spinal cord lesions in EAE (Fournier et al., 2017).”

https://doi.org/10.7554/eLife.61798.sa2

Article and author information

Author details

  1. Sina C Rosenkranz

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5228-4266

  2. Artem A Shaposhnykov

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Conceptualization, Data curation, Software, Investigation, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6772-6074

  3. Simone Träger

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared

  4. Jan Broder Engler

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Data curation, Software, Formal analysis, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3169-2076

  5. Maarten E Witte

    1. Department of Pathology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
    2. Department of Molecular Cell Biology and Immunology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
    Contribution
    Data curation, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1407-6220

  6. Vanessa Roth

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared

  7. Vanessa Vieira

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0205-9669

  8. Nanne Paauw

    Department of Molecular Cell Biology and Immunology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared

  9. Simone Bauer

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared

  10. Celina Schwencke-Westphal

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5105-4290

  11. Charlotte Schubert

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2967-4290

  12. Lukas Can Bal

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4731-2311

  13. Benjamin Schattling

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Conceptualization, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8809-9073

  14. Ole Pless

    Fraunhofer ITMP ScreeningPort, Hamburg, Germany
    Contribution
    Conceptualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1468-316X

  15. Jack van Horssen

    Department of Molecular Cell Biology and Immunology, Amsterdam UMC, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
    Contribution
    Conceptualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4078-7402

  16. Marc Freichel

    Institute of Pharmacology, Heidelberg University, Heidelberg, Germany
    Contribution
    Conceptualization, Resources, Formal analysis, Funding acquisition, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1387-2636

  17. Manuel A Friese

    Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration
    For correspondence
    manuel.friese@zmnh.uni-hamburg.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6380-2420

Funding

Stifterverband (Clinician-Scientist Fellowship)

  • Sina C Rosenkranz

Gemeinnützige Hertie-Stiftung (Hertie Network of Excellence in Clinical Neuroscience)

  • Sina C Rosenkranz
  • Jan Broder Engler

Deutsche Forschungsgemeinschaft (FR1638/3-1)

  • Marc Freichel

Deutsche Forschungsgemeinschaft (FR1638/3-2)

  • Marc Freichel

Deutsche Forschungsgemeinschaft (FR1720/9-1)

  • Manuel A Friese

Deutsche Forschungsgemeinschaft (FR1720/9-2)

  • Manuel A Friese

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

Acknowledgements

We thank the UKE Mouse Pathology Facility for histopathology of EAE mice and the UKE Vector Facility for generating the calcium indicator viruses and the animal facility. We thank Hilmar Bading and Peter Bengtson for advice on calcium imaging. Funding SCR is supported by a Clinician-Scientist Fellowship from the Stifterverband für die Deutsche Wissenschaft and the Hertie Network of Excellence in Clinical Neuroscience of the Gemeinnützige Hertie-Stiftung. This work was supported by grants of the Deutsche Forschungsgemeinschaft (DFG) to MF (FR1638/3-1, FR1638/3-2) and MAF (FR1720/9-1, FR1720/9-2), which are part of the DFG FOR2289.

Ethics

Animal experimentation: All animal care and experimental procedures were performed according to institutional guidelines and conformed to requirements of the German Animal Welfare Act. All animal experiments were approved by the local ethics committee (Behörde für Soziales, Familie, Gesundheit und Verbraucherschutz in Hamburg; G22/13 and 122/17). We conducted all procedures in accordance with the ARRIVE guidelines (Kilkenny et al., 2010).

Senior Editor

  1. Satyajit Rath, Indian Institute of Science Education and Research (IISER), India

Reviewing Editor

  1. Brandon K Harvey, NIDA/NIH, United States

Publication history

  1. Received: August 5, 2020
  2. Accepted: February 10, 2021
  3. Accepted Manuscript published: February 10, 2021 (version 1)
  4. Version of Record published: March 25, 2021 (version 2)
  5. Version of Record updated: February 2, 2022 (version 3)

Copyright

© 2021, Rosenkranz 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.

Metrics

  • 3,682
    Page views
  • 484
    Downloads
  • 18
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Sina C Rosenkranz
  2. Artem A Shaposhnykov
  3. Simone Träger
  4. Jan Broder Engler
  5. Maarten E Witte
  6. Vanessa Roth
  7. Vanessa Vieira
  8. Nanne Paauw
  9. Simone Bauer
  10. Celina Schwencke-Westphal
  11. Charlotte Schubert
  12. Lukas Can Bal
  13. Benjamin Schattling
  14. Ole Pless
  15. Jack van Horssen
  16. Marc Freichel
  17. Manuel A Friese
(2021)
Enhancing mitochondrial activity in neurons protects against neurodegeneration in a mouse model of multiple sclerosis
eLife 10:e61798.
https://doi.org/10.7554/eLife.61798

Categories and tags

Research organism

Further reading

    1. Immunology and Inflammation

    A lncRNA identifies Irf8 enhancer element in negative feedback control of dendritic cell differentiation

    Huaming Xu, Zhijian Li ... Martin Zenke
    Research Article Updated

    Transcription factors play a determining role in lineage commitment and cell differentiation. Interferon regulatory factor 8 (IRF8) is a lineage determining transcription factor in hematopoiesis and master regulator of dendritic cells (DC), an important immune cell for immunity and tolerance. IRF8 is prominently upregulated in DC development by autoactivation and controls both DC differentiation and function. However, it is unclear how Irf8 autoactivation is controlled and eventually limited. Here, we identified a novel long non-coding RNA transcribed from the +32 kb enhancer downstream of Irf8 transcription start site and expressed specifically in mouse plasmacytoid DC (pDC), referred to as lncIrf8. The lncIrf8 locus interacts with the lrf8 promoter and shows differential epigenetic signatures in pDC versus classical DC type 1 (cDC1). Interestingly, a sequence element of the lncIrf8 promoter, but not lncIrf8 itself, is crucial for mouse pDC and cDC1 differentiation, and this sequence element confers feedback inhibition of Irf8 expression. Taken together, in DC development Irf8 autoactivation is first initiated by flanking enhancers and then second controlled by feedback inhibition through the lncIrf8 promoter element in the +32 kb enhancer. Our work reveals a previously unrecognized negative feedback loop of Irf8 that orchestrates its own expression and thereby controls DC differentiation.

    1. Immunology and Inflammation

    Cell circuits between leukemic cells and mesenchymal stem cells block lymphopoiesis by activating lymphotoxin beta receptor signaling

    Xing Feng, Ruifeng Sun ... Joao Pedro Pereira
    Research Article Updated

    Acute lymphoblastic and myeloblastic leukemias (ALL and AML) have been known to modify the bone marrow microenvironment and disrupt non-malignant hematopoiesis. However, the molecular mechanisms driving these alterations remain poorly defined. Using mouse models of ALL and AML, here we show that leukemic cells turn off lymphopoiesis and erythropoiesis shortly after colonizing the bone marrow. ALL and AML cells express lymphotoxin α1β2 and activate lymphotoxin beta receptor (LTβR) signaling in mesenchymal stem cells (MSCs), which turns off IL7 production and prevents non-malignant lymphopoiesis. We show that the DNA damage response pathway and CXCR4 signaling promote lymphotoxin α1β2 expression in leukemic cells. Genetic or pharmacological disruption of LTβR signaling in MSCs restores lymphopoiesis but not erythropoiesis, reduces leukemic cell growth, and significantly extends the survival of transplant recipients. Similarly, CXCR4 blocking also prevents leukemia-induced IL7 downregulation and inhibits leukemia growth. These studies demonstrate that acute leukemias exploit physiological mechanisms governing hematopoietic output as a strategy for gaining competitive advantage.

    1. Immunology and Inflammation

    Characterisation of the immune repertoire of a humanised transgenic mouse through immunophenotyping and high-throughput sequencing

    Eve Richardson, Špela Binter ... Simon J Watson
    Research Article

    Immunoglobulin loci-transgenic animals are widely used in antibody discovery and increasingly in vaccine response modelling. In this study, we phenotypically characterised B-cell populations from the Intelliselect® Transgenic mouse (Kymouse) demonstrating full B-cell development competence. Comparison of the naïve B-cell receptor (BCR) repertoires of Kymice BCRs, naïve human, and murine BCR repertoires revealed key differences in germline gene usage and junctional diversification. These differences result in Kymice having CDRH3 length and diversity intermediate between mice and humans. To compare the structural space explored by CDRH3s in each species’ repertoire, we used computational structure prediction to show that Kymouse naïve BCR repertoires are more human-like than mouse-like in their predicted distribution of CDRH3 shape. Our combined sequence and structural analysis indicates that the naïve Kymouse BCR repertoire is diverse with key similarities to human repertoires, while immunophenotyping confirms that selected naïve B-cells are able to go through complete development.