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Glycolytic preconditioning in astrocytes mitigates trauma-induced neurodegeneration

  1. Rene Solano Fonseca
  2. Patrick Metang
  3. Nathan Egge
  4. Yingjian Liu
  5. Kielen R Zuurbier
  6. Karthigayini Sivaprakasam
  7. Shawn Shirazi
  8. Ashleigh Chuah
  9. Sonja LB Arneaud
  10. Genevieve Konopka
  11. Dong Qian
  12. Peter M Douglas  Is a corresponding author
  1. Department of Molecular Biology, University of Texas Southwestern Medical Center, United States
  2. Department of Mechanical Engineering, University of Texas at Dallas, United States
  3. O’Donnell Brain Institute, University of Texas Southwestern Medical Center, United States
  4. Department of Neuroscience, University of Texas Southwestern Medical Center, United States
  5. Department of Integrative Biology, University of California, Berkeley, United States
  6. Hamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, United States
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Cite this article as: eLife 2021;10:e69438 doi: 10.7554/eLife.69438

Abstract

Concussion is associated with a myriad of deleterious immediate and long-term consequences. Yet the molecular mechanisms and genetic targets promoting the selective vulnerability of different neural subtypes to dysfunction and degeneration remain unclear. Translating experimental models of blunt force trauma in C. elegans to concussion in mice, we identify a conserved neuroprotective mechanism in which reduction of mitochondrial electron flux through complex IV suppresses trauma-induced degeneration of the highly vulnerable dopaminergic neurons. Reducing cytochrome C oxidase function elevates mitochondrial-derived reactive oxygen species, which signal through the cytosolic hypoxia inducing transcription factor, Hif1a, to promote hyperphosphorylation and inactivation of the pyruvate dehydrogenase, PDHE1α. This critical enzyme initiates the Warburg shunt, which drives energetic reallocation from mitochondrial respiration to astrocyte-mediated glycolysis in a neuroprotective manner. These studies demonstrate a conserved process in which glycolytic preconditioning suppresses Parkinson-like hypersensitivity of dopaminergic neurons to trauma-induced degeneration via redox signaling and the Warburg effect.

eLife digest

Concussion is a type of traumatic brain injury that results from a sudden blow or jolt to the head. Symptoms can include a passing headache, dizziness, confusion or sensitivity to light, but experiencing multiple concussions can have drastic repercussions in later life.

Studies of professional athletes have shown that those who experience one or more concussions are prone to developing Alzheimer’s and Parkinson’s disease, two well-known neurodegenerative diseases. Both conditions involve the progressive loss or breakdown of nerve cells, called neurons. But exactly how this so-called neurodegeneration of brain cells stems from the original, physical injury remains unclear.

Head trauma may cause damage to the structural support of a cell or disrupt the flow of electrical impulses through neurons. Energy use and production in damaged cells could shift into overdrive to repair the damage. The chemical properties of different types of brain cells could also make some more vulnerable to trauma than others. Besides neurons, star-shaped support cells in the brain called astrocytes, which may have some protective ability, could also be affected.

To investigate which cells may be more susceptible to traumatic injuries, Solano Fonseca et al. modelled the impacts of concussion-like head trauma in roundworms (C. elegans) and mice. In both animals, one type of neuron was extremely vulnerable to cell death after trauma. Neurons that release dopamine, a chemical involved in cell-to-cell communication and the brain’s reward system, showed signs of cell damage and deteriorated after injury. Dopaminergic cells, as these cells are called, are involved in motor coordination, and the loss of dopaminergic cells has been linked to both Alzheimer’s and Parkinson’s disease.

Astrocytes, however, had a role in reducing the death of dopaminergic neurons after trauma. In experiments, astrocytes appeared to restore the balance of energy production to meet the increased energy demands of impacted neurons. Single-cell analyses showed that genes involved in metabolism were switched on in astrocytes to produce energy via an alternative pathway. This energetic shift facilitated via astrocytes may help mitigate against some damage to dopamine-producing neurons after trauma, reducing cell death.

This work furthers our understanding of cellular changes in the concussed brain. More research will be required to better characterise how this immediate trauma to cells, and the subsequent loss of dopaminergic neurons, impacts brain health long-term. Efforts to design effective therapies to slow or reverse these changes could then follow.

Introduction

Selective vulnerability of different neuronal subtypes to dysfunction, degeneration, and death underlies all neurological diseases. Differential interplay between genetic and environmental factors contribute to this neuronal selectivity. Environmental stimuli have the capacity to trigger pathogenic cascades contingent on genetic factors. Such is the case for traumatic brain injury (TBI) and concussion, in which the initial biomechanical insult precipitates numerous cellular and systemic alterations with the potential to promote neurological dysfunction and progressive neurodegeneration. In particular, ionic imbalances, cytoskeletal disruption, aberrant proteostasis, genomic instability, and mitochondrial dysfunction within neurons and support cells initiate systemic abnormalities including metabolic impairment, vascular disruption, inflammation, blood-brain barrier defects, and reduced cerebral blood-flow (Giza et al., 2018). Yet, the stochastic nature of the mechanical insult has complicated our ability to define the underlying mechanisms of neurodegeneration. It remains unclear whether the inherent chemical properties of different neuronal subtypes confer varying degrees of vulnerability to trauma-induced neurodegeneration. Additionally, the genetic contribution to this widespread disorder is poorly understood, genetic effectors are likely to amplify the cellular and systemic aberrations initiated by the mechanical insult. Thereby, we hypothesize that genetic targeting and modulation may be sufficient to suppress the ensuing neurodegeneration after concussive brain injury.

Metabolic fluctuations in the brain occurring with age or injury have been linked with neurological health (Camandola and Mattson, 2017; Marino et al., 2007). The neurometabolic cascade resulting from concussion is characterized by erratic fluctuations in metabolism, which are hypothesized to accommodate the energetic demand required for neuronal repolarization and repair (Giza and Hovda, 2001). Brain energetics relies heavily on two metabolic pathways including mitochondrial oxidative phosphorylation and cytosolic glycolysis (Kasischke et al., 2004; Pellerin and Magistretti, 1994). The astrocyte-neuron lactate shuttle (ANLS) entails astrocytic production and transport of the glycolytic by-product, lactate, to neurons which preferentially oxidize it rather than glucose to meet their energetic demands (Magistretti et al., 1999). Immediately following injury, rodent brains display signs of decreased oxidative phosphorylation that can persist for weeks (Gilmer et al., 2009; Xiong et al., 1997). Conversely, the injured brain transiently elevates normoxic glycolysis in humans and animal models (Bergsneider et al., 1997; Sokoloff et al., 1977; Yoshino et al., 1991), suggesting an energetic reallocation through astrocytic means. This metabolic shift is a phenomena observed in cancer cells and is termed the Warburg effect, in which aerobic glycolysis is favored over oxidative phosphorylation (Warburg, 1956). Thus, the injured brain initially undergoes a Warburg-like response, but how this metabolic shift occurs within the astrocyte-neuron axis and its ability to impact neurological function and degeneration after concussion is not well understood.

Results

Conserved hypersensitivity of dopaminergic neurons to trauma-induced degeneration

In contrast to the cellular and network complexity of the human brain, the adult nematode, C. elegans, possesses 302 post-mitotic neurons (White et al., 1986). A simple nervous system offers the opportunity to rapidly screen different neural subtypes and gene products in pursuit of uncovering neurodegenerative mechanisms that translate to the mammalian brain. To this end, we developed a collision-based, rapid deceleration model of trauma in which high-frequency, multidirectional agitation delivers a well-calibrated injury to a large population of worms (Egge et al., 2021). To determine whether the inherent biochemical properties of different neural subtypes impart variable sensitivity to blunt force trauma, dopaminergic, GABAergic, glutamatergic, serotonergic, and cholinergic neurons were individually monitored using targeted GFP expression. Fluorescence retention in individual neurons housed in the central nerve ring was measured to assess viability at various time points post-trauma (Nass et al., 2002). When compared to age-matched non-injured counterparts, dopaminergic neurons displayed the greatest reduction in fluorescence with a 42.1% (+/−1.4%) loss in signal intensity within 24 hr after injury, indicative of elevated sensitivity to degeneration (Figure 1A). Microscopic examination of dopaminergic neurons revealed phenotypes characteristic of neuronal damage (Gennarelli, 1996; Kilinc et al., 2009) 24 hr after injury, including fluorescence beading in dendritic processes (Figure 1B).

Figure 1 with 1 supplement see all
Conserved vulnerability of dopaminergic neurons to trauma-induced degeneration.

(A) Post-trauma GFP retention in different C. elegans neuronal subtypes by large-particle flow cytometry. n=two sorts, n=1181 worms (dopaminergic), n=1265 worms (GABAergic), n=1115 worms (glutamatergic), n=3139 worms (serotonergic), and n=1117 worms (cholinergic). (B) Representative micrographs of GFP dopaminergic neuronal morphology in C. elegans pre-injury and 24 hr post-injury. Scale bar=20 μm. (C) Representative micrographs of tyrosine hydroxylase (TH; green) and cleaved caspase-3 (Casp3; gray) in the midbrain of 10 week-old mice subject to concussive injury. 1: VTA, 2: SNpc, 3: SNr. Scale bar=200 μm. (D) Quantification of cleaved caspase-3 staining in the midbrain. n=four uninjured, n=six injured. (E) Latency to fall off an accelerating rotating rod 7 days after concussive injury. n=five uninjured, n=eight injured. (F) Incidence of laser beam disruptions in cage per mouse over 24 hr, n=three per group. Data are mean ± SEM. *p ≤ 0.05, **p ≤ 0.01, ****p ≤ 0.0001.

To examine conservation of dopaminergic hypersensitivity to trauma-induced neurodegeneration, we administered a concussive close-head injury to mice. This injury impaired immediate righting reflex response, indicating temporary loss of consciousness (Figure 1—figure supplement 1A). Seven days after injury, dopaminergic neurons of the substantia nigra exhibited signs of increased apoptosis as evidenced by enhanced immunofluorescence staining of cleaved caspase-3, and a 59.06% (+/−3.03%) loss of tyrosine hydroxylase-positive neurons was observed 28 days post-trauma (Figure 1C and D; Figure 1—figure supplement 1B–D). Open-head models of rodent brain injury consistently report necrotic tissue loss at or near the lesion site (Hall et al., 2005). Yet, one study demonstrates neuronal loss within the substantia nigra (Liu et al., 2017). In contrast, closed-head brain injury without cranial fracture revealed no significant cleaved caspase-3 staining in the hippocampus, thalamus and visual cortex seven days post-injury (Figure 1—figure supplement 1E–I). Furthermore, neuroinflammation was not evident in the immunolabeling of microglia (IBA1) with CD68-positive inflammatory cytoplasmic granules or in the transcriptional activation of known inflammatory targets (Figure 1—figure supplement 1J–K). Dopaminergic neurons within the midbrain comprise the nigrostriatal circuit, which influences voluntary movement. In concussed mice, we observed reduced latency on the accelerating rotarod and decreased resting state ambulation coincident with dopaminergic degeneration (Figure 1E and F). Thus, modeling blunt force trauma in C. elegans and concussive injury in mice demonstrate conserved hypersensitivity of dopaminergic neurons to biomechanical insult.

To determine the physical strain on brain regions proximal and distal to the injury site, we developed a computational biophysical model of TBI. Specifications for our closed-head trauma device were incorporated into a detailed finite element model (FEM) with ~2.5 million 3D solid elements (Figure 2—figure supplement 1A and B). To capture stress and strain across the entire brain, six distinct material properties were assigned to accurately represent varying brain regions (Table 1). Biophysical modeling revealed a stress wave propagating throughout the mouse brain due to linear impact (Figure 2—figure supplement 1C-E; Video 1). By FEM analysis, a peak effective stress of 22.8 kPa (corresponding principal strain of 8.3%) first appeared in the cortex region at 0.31 ms. Then, stress propagated to 28.62 kPa (corresponding principal strain of 23.6% at the thalamus and 18.5% at the substantia nigra) at 0.85 ms post-injury in the thalamus and substantia nigra region. After impacting, a stress wave caused another peak effective stress of around 30 kPa (corresponding principal strain of 40.9% at the brainstem region and 27.0% at the thalamus region) at 1.64 ms (Figure 2A–C; Video 2). Thus, dopaminergic neurons may be sensitized to biomechanical insults due to inherent physiological properties since non-invasive, linear impact generates a strain within the midbrain comparable to brain regions closer to the impact site that lack evidence of neuronal death.

Figure 2 with 1 supplement see all
Concussive head trauma propagates strain throughout the brain.

Heat map of representative brain shows predicted maximum principal strain at (A) 0.85 ms and (B) 1.64 ms after initial impact. (C) Time history of maximum principal strain within the cortex (blue), thalamus (red), substantia nigra (black), cerebellum (green), and brainstem (magenta).

Table 1
Viscoelastic parameters for P56 mouse brain.
Brain regionG0 (Pa)g1g2glτ1 (ms)τ2 (ms)
Pons76430.5780.2670.16212182
Cortex63430.5680.2640.16814182
Cerebellum28070.5180.310.17114190
Thalamus26740.5780.2480.17415206
Medulla38590.520.30.1817226
Hippocampus54220.1870.5540.12126518
Video 1
Maximum principal stress propagation throughout the mouse brain after impact.
Video 2
Von Mises stress propagation throughout the mouse brain after impact.

Cytochrome C oxidase deficiency protects dopaminergic neurons against trauma-induced degeneration

Longitudinal studies from the clinic show that traumatic brain injury with a loss of consciousness presents a significant risk factor for development of Parkinson’s disease (PD), Parkinsonism, and Lewy body accumulation (Crane et al., 2016). Indeed, the pathology and motor deficits that we observed after concussive injury in mice are similar to those of individuals with PD. Based on familial links and environmental risk factors, mitochondrial components including the electron transport chain (ETC) have emerged as prominent drivers of PD (Perier and Vila, 2012). Utilizing our C. elegans trauma model, we found that reducing the complex IV cytochrome C oxidase activity of the ETC through cox-5b RNAi promotes a dose-dependent survival of dopaminergic neurons after injury (Figure 3A and B). Moreover, reduced expression of cox-5b exclusively in the nervous system was sufficient to promote survival of dopaminergic neurons after blunt force trauma (Figure 3—figure supplement 1A).

Figure 3 with 1 supplement see all
Conserved protection against trauma-induced neurodegeneration by reducing cytochrome C oxidase.

(A) Post-trauma GFP retention in dopaminergic neurons of C. elegans by large-particle flow cytometry with the respective RNAi for ETC subunit components. n=two sorts, n=3344 worms (nuo-2), n=2343 worms (sdhd-1), n=3825 (cyc-1), n=2973 (cox-5b), n=3265 (atp-3), n=1948 (EV). (B) Post-trauma GFP retention in dopaminergic neurons of C. elegans treated with cox-5b/EV RNAi dilutions. n=six sort, n=512 worms (0), n=827 worms (0.25), n=900 worms (0.5), n=832 worms (1). (C) Representative micrographs of tyrosine hydroxylase (TH, green) and cleaved caspase-3 (Casp3, gray) in the midbrain of 10-week-old Surf1+/+ or Surf1-/- mice subject to concussive injury. 1: VTA, 2: SNpc, 3: SNr. Scale bar=200 μm. (D) Quantification of cleaved caspase-3 staining in the midbrain. n=5 Surf1+/+, n=6 Surf1-/-. (E) Latency to fall off rotating rod 7 days after concussive injury (n=four per group). (F) Incidence of laser beam disruptions per mouse over 24 hr (n=three per group). Data are mean ± SEM. *p ≤ 0.05, **p ≤ 0.01, and ****p ≤ 0.0001.

To examine the conservation of cytochrome C oxidase-related neuroprotection, we examined mice lacking the complex IV assembly factor surfeit locus protein 1, SURF1 (Dell'agnello et al., 2007). Surf1 mutations reduce rather than ablate cytochrome C oxidase activity, leading to a 53.9% reduction in function (Figure 3—figure supplement 1B). Surf1-/- mice exhibited a 40% reduction in mortality rate following lethal head trauma and a 2.3-fold improvement in post-injury reflex recovery time compared to wild type littermates (Figure 3—figure supplement 1C and D), suggesting immediate protection against TBI. Cleaved caspase-3 signal intensity within the midbrain was reduced by 55% (+/−3.5%) after 7 days in concussed Surf1-/- mice (Figure 3C and D). Coincident with decreased neurodegeneration, Surf1-/-mice showed increased latency in high-speed rotarod analysis compared to wild-type littermates (Figure 3E; Figure 3—figure supplement 1E). This was accompanied by a 25.93% (+/−6.828%) dopaminergic loss 28 days post-injury, compared to the 59.06% (+/−3.03%) observed in wild-type counterparts (Figure 3—figure supplement 1F–H). Moreover, resting state ambulation was unaffected by trauma in Surf1-/- mice unlike deficits observed in wild-type littermates (Figure 3F). Thus, suppressing trauma-induced degeneration of dopaminergic neurons by reduced cytochrome C oxidase activity is evolutionarily conserved between C. elegans and mice.

Cytochrome C oxidase deficiency suppresses trauma-induced reactive oxygen species production

To understand the cause of neurodegeneration in our worm-to-mouse translational trauma model, we examined similarities reported for concussive brain injury and Parkinson’s disease. Reactive oxygen species (ROS) are toxic by-products of brain injury (Hall and Braughler, 1993) that originate in the mitochondrial inner membrane by partial reduction of oxygen. Upon accumulation, ROS initiate cellular damage and stress. In genetic and pharmacological models of Parkinson’s disease, mitochondrial dysfunction and ROS accumulation underlie dopaminergic neurodegeneration and subsequent disease progression (Blesa et al., 2015). In C. elegans, we observed a five fold increase in ROS production immediately after blunt force trauma, which was suppressed upon cox-5b RNAi (Figure 4A). Since cox-5b RNAi treatments in C. elegans are reported to reduce oxygen consumption (Kaufman and Crowder, 2015), we hypothesized that reduced reliance on mitochondrial respiration mitigates the likelihood of overwhelming respiratory output. Essentially, reducing the rate of electron transfer between ETC complexes reduces the rate of ROS generation and accumulation. In this model, cox-5b RNAi should fail to protect neurons against ectopically generated ROS. Indeed, elevating neuronal ROS via expression of the mitochondrial targeted oxidative photosensitizer, Tom20::KillerRed (Wojtovich and Foster, 2014), was sufficient to induce dopaminergic neurodegeneration in worms that could not be suppressed by cox-5b RNAi (Figure 4B). Thus, intra-neuronal ROS production is sufficient to drive dopaminergic degeneration, while neuroprotection conferred by reduced cytochrome C oxidase activity appears to act upstream of this ROS-induced neurotoxicity.

Figure 4 with 1 supplement see all
Reducing cytochrome C oxidase prevents trauma-induced ROS production.

(A) Time-course of peroxide levels in C. elegans post-trauma. n=three repeats per group. (B) GFP retention in dopaminergic neurons of C. elegans ectopically expressing the mitochondrial-targeted KillerRed fluorophore in neurons. n=three repeats per group. (C) Oxygen consumption rate, OCR, in Surf1+/+ and Surf1-/- mouse embryonic fibroblasts. n=3 Surf1+/+, n=2 Surf1-/-. (D) Representative micrographs of TOM20 (green) and DAPI (blue) in Surf1+/+ and Surf1-/-mouse embryonic fibroblasts. Scale bar=20 μm. (E) Trauma-induced superoxide production in mitochondria from Surf1+/+ and Surf1-/- brain single cell suspensions 2 hr post-injury. n=three per group. (F) Oxidized glutathione (GSSG) over glutathione (GSH) from metabolite analysis of Surf1+/+ and Surf1-/- brain tissue subject to concussive injury. n=three per group. Data are mean ± SEM. *p ≤ 0.0116, ns (not significant, p = 0.0524) and ****p ≤ 0.0001.

To evaluate the conservation of this neuroprotective mechanism in the mouse, we examined mitochondrial function and ROS production in the absence of Surf1. Despite no significant change in mitochondrial membrane potential (Figure 4—figure supplement 1A), Surf1-/- cells consumed two fold less oxygen and exhibited reduced reticular mitochondrial morphology (Figure 4C and D; Figure 4—figure supplement 1B), strongly suggesting reductions in oxidative phosphorylation (Galloway et al., 2012). Two hours after concussive injury in mice, a transient 69% increase of the ROS byproduct, superoxide, was observed in mitochondria isolated from brain tissue compared to a 6% increase in Surf1-/- mice (Figure 4E). Moreover, metabolite analysis from brain homogenates shows a 61% increase in glutathione oxidation within 2 hr of injury in wild-type animals, which was not observed in Surf1-/- mutants (Figure 4F). Thus, decreased cytochrome C oxidase activity suppresses transient trauma-induced ROS accumulation, a process conserved from worm to mouse. Notably, the basal mitochondrial superoxide levels and oxidized glutathione were significantly elevated in the Surf1-/- brains compared to wild-type littermates (Figure 4—figure supplement 1C). Therefore, minimizing transient production and accumulation of oxidative species immediately after trauma parallels neuroprotection.

Glycolytic preconditioning by reduced cytochrome C oxidase activity protects dopaminergic neurons against concussive injury

Traumatic brain injury is a costly energetic process in which neurons work to re-establish resting membrane potential and facilitate cellular repair (Giza and Hovda, 2001). Despite the apparent decrease in oxidative phosphorylation (Figure 4C), ATP levels in the Surf1-/- brain were not affected compared to wild type littermates (Figure 5—figure supplement 1A). Furthermore, respiratory exchange rate, food intake, body temperature, and overall body weight were also unaffected in Surf1-/- mice (Figure 5—figure supplement 1B–E). Thus, Surf1-/- mice likely accommodate their energetic needs through alternate means in response to suboptimal mitochondrial respiration. Post-trauma, the brain transiently increases glycolysis (Yoshino et al., 1991) and will readily metabolize the glycolytic by-product, lactate (Glenn et al., 2015). Surf1-/- animals appeared to undergo this Warburg-like increase in glycolysis. Consistent with elevated blood lactate reported in Surf1-/- mice (Pulliam et al., 2014), increased transcription of the lactate dehydrogenase, Ldhd, in the Surf1-/- brain correlated with the rapid acidification of growth media by Surf1-/- cells (Figure 5A; Figure 5—figure supplement 1F). Similar transcriptional upregulation of the lactate dehydrogenase, ldh-1, was also observed in worms treated with cox-5b RNAi (Figure 5—figure supplement 1G).

Figure 5 with 2 supplements see all
Impaired cytochrome C oxidase increases glycolysis in astrocytes through a Warburg-like effect.

(A) Heat map of transcriptional differences in Surf1+/+ versus Surf1-/- brains. n=4. Significance denoted next to gene. (B) Western blots of phosphorylated PDHE1α at Ser293 and αTubulin from Surf1+/+ and Surf1-/- brains, 7 days post-injury. (C) Dot plot of single nuclei RNAseq of glycolytic and Warburg-related genes in neuronal cell subtypes from Surf1+/+ and Surf1-/- brains. (D) Dot plot of single nuclei RNAseq of glycolytic genes in astrocytes from Surf1+/+ and Surf1-/- brains. (E) Quantification of phosphorylated PDHE1α at Ser293 and αTubulin from Surf1+/+ and Surf1-/- brains after NaCl and dichloroacetate (DCA) treatment. (F) Quantification of cleaved caspase-3 immunostaining in NaCl and DCA treated Surf1+/+ (n=3) and Surf1-/- (n=4) brains 7 days post-injury. (G) Post-trauma retention of GFP fluorescence in dopaminergic neurons of C. elegans treated with pdp-1 RNAi and measured by large-particle flow cytometry. n=1758 worms (EV RNAi), n=2307 worms (pdp-1 RNAi) across two independent trials. (H) Representative micrographs of GFP dopaminergic neuronal morphology in injured C. elegans treated with pdp-1 RNAi. Scale bar=20 μm. Data are mean ± SEM. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, and ****p ≤ 0.0001.

We hypothesized that neuroprotection by reduced cytochrome C oxidase is the result of preconditioning through metabolic reallocation. By preemptively shifting metabolism away from mitochondria respiration, the likelihood of overwhelming ETC function is reduced during the metabolic demand of TBI. To accommodate energetic needs, the brain relies more heavily on normoxic glycolysis in a process referred to as the Warburg effect (Warburg, 1956). In Surf1-/- brains, we observed transcriptional upregulation of several enzymes in the glycolytic pathway (Figure 5A; Figure 5—figure supplement 1H). As a critical regulator of the Warburg shunt, which facilitates the shift from mitochondrial respiration to cytosolic glycolysis, the pyruvate dehydrogenase complex (PDC) is inactivated by pyruvate dehydrogenase kinases, PDK1-4, through phosphorylation of its E1 alpha subunit (PDHE1α) (Kolobova et al., 2001; Patel and Korotchkina, 2006). In the brains of Surf1 mutants, we observed transcriptional upregulation of the brain-enriched Pdk2 in conjunction with the repression of its complementary PDH phosphatase, Pdp1 (Figure 5A; Figure 5—figure supplement 1H). PDHE1α phosphorylation has previously been reported after open-head brain injury and provides a potential molecular explanation for trauma-induced hyperglycolysis (Xing et al., 2009). Consistent with transcriptional changes in Surf1-/- brains, we observed increased PDHE1α phosphorylation both before and after injury in Surf1-/- compared to wild-type littermates (Figure 5B; Figure 5—figure supplement 2A), while total PDH levels are equivalent (Figure 5—figure supplement 2B and C). Thus, Surf1 mutants are preconditioned prior to injury into a Warburg-like state, which likely mitigates the transient ROS production observed immediately after concussion.

While Surf1 mutants possess a shift in their global transcriptome, it remained unclear how this neuroprotective response occurs at the cellular level within the astrocyte-neuron axis. To obtain this cellular resolution, we employed single nuclei RNA sequencing, which allowed transcriptomic analysis of individual cells from the midbrain and striatum (Figure 5—figure supplement 1M–O). Consistent with the astrocyte-neuron lactate shuttle hypothesis, the brain-enriched Warburg shunt regulator, Pdk2, as well as every activated glycolytic gene except for Gpi1, was elevated in astrocytes (Figure 5C). Indirect immunofluorescence within the midbrain confirmed that steady-state expression of the PDK2 protein was confined to astrocytes (Figure 5—figure supplement 1P and Q). Moreover, glycolytic transcripts were elevated within Surf1 mutant astrocytes compared to wild-type littermates (Figure 5D). Through cellular resolution of this glycolytic preconditioning mechanism, we provide evidence that the neuroprotective Warburg shift initiated by electron transport impairment originates within astrocytes.

To understand the molecular mechanisms linking the Surf1 mutation with the Warburg effect, we examined the hypoxia inducible factor, Hif1a, which promotes aerobic glycolysis by activating expression of PDK and glycolytic genes (Kim et al., 2006). Mitochondria act as oxygen sensors which can signal to cytosolic HIF1α through ROS production (Guzy et al., 2005). We confirmed that elevated ROS production caused by paraquat treatment was sufficient to stabilize HIF1α in culture (Figure 5—figure supplement 1J). We hypothesized that elevated basal ROS levels observed in Surf1-/- brains (Figure 4—figure supplement 1C) reduce mitochondrial respiration in favor of glycolysis through Hif1a activation. Pharmacological inhibition of trauma-induced Hif1a activation has previously been shown to enhance necrotic lesion formation after brain trauma (Umschweif et al., 2013), suggesting a neuroprotective role for Hif1a activation in concussive injury. Consistent with previous studies in C. elegans (Lee et al., 2010), reducing cytochrome C oxidase activity through cox-5b RNAi increased hif-1 activity as evidenced by induction of the established hif-1 transcriptional target in C. elegans, nhr-57 (Figure 5—figure supplement 1L). Supporting this conserved response, we observed HIF1α protein stabilization in Surf1-/-mouse embryonic fibroblasts (Figure 5—figure supplement 1K) as well as transcriptional upregulation of established glycolytic Hif1a targets in Surf1-/- brains, including critical enzymes in the Warburg shunt (Figure 5A).

Our data indicates that mild elevation of ROS caused by reduced ETC function, promotes a Hif1a mediated Warburg-like effect in the brain, which reduces the chance of overwhelming mitochondrial respiratory capacity under the strong energetic demand of TBI. To examine the necessity of this Warburg shift in our neuroprotective model, we administered the pharmacological Warburg inhibitor, dichloroacetate (DCA), and observed that Surf1 mutants were no longer protected against trauma-induced neurodegeneration (Figure 5E and F; Figure 5—figure supplement 1R). Consistent with earlier reports of rodent brain injury (Lazzarino et al., 2019) and SURF1 mutants (Figure 5A), pdp-1 expression was repressed in C. elegans after blunt force injury (Figure 5—figure supplement 1I). To further validate the Warburg shift as a neuroprotective mechanism, we employed genetic means in C. elegans and observed that pdp-1 RNAi suppressed degeneration of dopaminergic neurons after trauma (Figure 5G and H). Albeit to a lesser degree, reduced expression of pdp-1 exclusively in the nervous system was sufficient to promote survival of dopaminergic neurons after blunt force trauma (Figure 5—figure supplement 2D). Thus, modulating PDC activity through the key regulatory factors is sufficient to suppress trauma-induced neurodegeneration in both mice and worms.

Discussion

The molecular details of TBI and its immediate and long-term consequences can be discovered using a translational animal model approach. To this end, we describe a comparative animal model to identify conserved molecular mechanisms operating in TBI. The worm C. elegans is amenable to high throughput genetic, biochemical, and behavioral studies, while the mouse is used to further uncover and validate conserved genetic targets and molecular mechanisms. These complementary models allowed us to characterize an evolutionary conserved, hypersensitive neuronal subtype to physical insult. Rather than being a stochastic mechanism of progressive tissue degeneration dictated by the type and site of injury, the physiological properties and regulatory mechanisms inherent to different neuronal subtypes likely determine their vulnerability to trauma. This is evidenced by sensitivity of dopaminergic neurons to biomechanical insult in both our randomized, high-frequency trauma model in worms and concussive injury in mice. Furthermore, these complementary models were used to demonstrate that genetic modification of a single gene can suppress degeneration of this neuronal subtype following concussive injury.

Pharmacological induction of Parkinsonian phenotypes, including death of dopaminergic neurons in the midbrain, requires highly oxidative drugs that irreversibly inhibit mitochondrial ETC complexes I and III (Subramaniam and Chesselet, 2013). Yet, our studies demonstrate that ETC complex IV insufficiency protects dopaminergic neurons from trauma-induced degeneration. We attribute these neuroprotective effects to a metabolic shift away from mitochondrial respiration, which minimizes or even abolishes transient oxidative stress in the neuron and suppresses dopaminergic degeneration. Although often thought of as unavoidable toxic byproducts of oxidative phosphorylation, ROS can have beneficial roles in the cell as a signaling molecule (Guzy et al., 2005). In our complementary models of blunt force injury, we believe that both toxic and protective mechanisms of ROS are operating, which historically has been difficult to discern.

Trauma-induced hyperglycolysis was reported decades ago yet its cellular resolution and pathophysiological role have remained unclear (Bergsneider et al., 1997; Carpenter et al., 2015). The astrocyte-neuron lactate shuttle is a crucial mechanism by which the brain maintains energetic homeostasis. Perturbations to this system have been reported in neurodegenerative disorders and contribute to disease progression (Mason, 2017). Our data suggests that preemptively repressing mitochondrial respiration initiates a Warburg-like response in astrocytes as an attempt to restore homeostatic energetics. Thus, the injured brain likely induces hyperglycolysis in astrocytes as a protective mechanism to mitigate the oxidative damage resulting from over-activated mitochondrial respiration. Cerebral metabolic preconditioning presents a plausible prophylactic method for individuals at higher risk for TBI. Interestingly, neuroinflammation does not correlate with the rapid emergence of dopaminergic neurodegeneration in our study. However, neuroinflammation and other factors likely contribute to the later stages of this neurodegenerative condition (McKee et al., 2015). Therefore, longitudinal studies are required to further characterize disease progression, including neuroinflammation and proteotoxicity, after the initial loss of dopaminergic neurons in the midbrain.

Materials and methods

Mice

Adult mice of at least 8 weeks of age were used in this study. Surf1+/+ and Surf1-/- (RRID:MGI:3698949) were obtained from Massimo Zeviani (University of Cambridge). Littermates were obtained from Surf1 heterozygous crossing and housed in groups of 5 regardless of phenotype with food and water ad libitum. All mouse studies were approved by the University of Texas Southwestern Medical Center Institutional Animal Care and Use Committee (Protocol No. 2016–101750) and performed in accordance with institutional and federal guidelines.

Closed-head traumatic brain injury

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The closed-head traumatic brain injury device consisted of an upright, railed-guided weight drop composed of an aluminum body and equipped with a speed monitor device to measure the velocity of the weight drop when it lands on a brass impactor with a slightly concave nylon tip. The modular weight system consisted of a 50 ml polypropylene conical tube filled with lead buckshot, which weighted 220 g when filled. Before injury, mice were anesthetized with vaporized isoflurane/oxygen in a chamber connected to an inhalation anesthesia system (Cat. No. 901810; VetEquip). Vaporizer settings were set to 3% USP grade isoflurane and 2.5 lpm USP grade oxygen. After being anesthetized, mice were placed on a thick memory foam cushion resting on a height-adjustable platform. Once the desired site of impact was localized on the head of mice, the impactor tip was placed directly in contact with that site and the weight was dropped by activating an electronic trigger. After mice were injured, they were placed on their backs to record their righting reflex recovery time and allowed to recover. After recovery, mice were placed back in their cage with food and water ad libitum. Control mice received only anesthesia and were not subjected to impact, but their righting reflex recovery time was also recorded in the same manner as the injured mice.

Finite element analysis

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Biomechanical responses of the mouse brain under impact were analyzed using the commercial finite element (FE) code ABAQUS (Dessault Systemes Simulia Corp., Providence RI). More specifically, a 2D geometric model of the sagittal section of the mouse brain tissue was first generated based on a partition scheme defined previously (MacManus et al., 2016) and the 2D mouse brain image obtained from the experiment. This model also contains a layer of skull of thickness of 1.5 mm. The 2D model was meshed with 5067 4-noded quadrilateral elements, which was then converted to 3D by extruding the 2D meshed plane orthogonally that gives a layer thickness of 16 mm. The 3D geometric model of the impactor was constructed based on the measured dimensions shown in (Figure 2—figure supplement 1A), which is meshed with 40,320 8-noded brick elements (C3D8R). The final 3D FE model of the brain was modeled with 2,546,048 8-noded brick elements (C3D8R) (Figure 2—figure supplement 1B). The brain tissue was partitioned into six regions (MacManus et al., 2016), and 6 sets of viscoelastic parameters from P56 mice (MacManus et al., 2017) were applied to model the different viscoelastic properties of corresponding regions. Viscoelastic parameters from adult rat (Finan et al., 2012) had to be used to define the hippocampus due to lack of characterization in the mouse. Values of these parameters are listed in Tables 1 and 2.

Table 2
Material parameters for the impactor.
ComponentDensityYoung’s ModulusPoisson’s Ratio
Brass (impactor body, head)8480 kg/m3100 GPa0.34
Nylon (impactor nut)1120 kg/m32.3 GPa0.4

To start the simulation, the impactor was placed at 0.9 mm above the skull with initial velocity of 4.54 m/s. Dynamic responses of the mouse brain were computed with the standard explicit time integration scheme with a time step of 2 ms and total steps of 200. Interaction between the impactor and skull was modeled by the frictionless and surface-to-surface contact algorithm. Contact between the skull and brain tissue was treated with tie constraint. Five points in cortex, thalamus, substantia nigra, cerebellum, and brainstem were selected to monitor the time history of the principal strain and effective stress during impact (Figure 2—figure supplement 1B). Points for the cortex, thalamus, and substantia nigra were respectively located 2.4 mm, 4.1 mm, and 8.2 mm in vertical distance to the point of impact. Points for the cerebellum and brainstem were located 4.7 mm posterior to the three previous points, 4.1 mm and 8.2 mm in vertical distance to the point of impact. The impactor contained brass and nylon parts, both of which were modeled as isotropic linear elastic material. Material properties of the two are provided in Table 2. The skull is also modeled as isotropic linear elastic material with Young’s Modulus of 1 GPa and Poisson’s ratio of 0.33 (Unnikrishnan et al., 2019). Mass density skull is 1710 kg/m3 (Hua et al., 2015).

Mass density of the brain tissue is 1040 kg/m3 and it is modeled as a viscoelastic material (Hua et al., 2015) in which stress is evaluated through the introduction of the relaxation function G(t):

(1) τ(t)=0tG(ts)γ˙(s)ds

 in which γ is the shear strain and superimposed dot representing the time derivative. The relaxation function G(t) takes the form of the Prony series, given as

(2) G(t)=G+i=1NGiet/τi

in which t is time, G is the long-term relaxation modulus, τi is the characteristic relaxation time and Gi is the corresponding relaxation modulus. The number of the terms N included in this series depends on fitting of the model predictions to the experiments (Finan et al., 2012).

Immunohistochemistry

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Mice were perfused with cold 1x PBS and 4% paraformaldehyde. Brains were harvested and sectioned to a thickness of 40–50 µm with the VF-310-0Z Compresstome Vibrating Microtome (Precisionary Instruments, Greenville, NC). All staining was performed on free floating brain sections on a 24-well plate. Incubation in primary antibodies was performed in blocking solution (10% normal donkey serum) and 2% triton at 4°C for 48 hr. Primary antibodies used were: rabbit anti-Iba1 (Cat. No. 019–19741; Wako Chemicals; RRID:AB_839504) at 1:200, rat anti-CD68 (Cat. No. MCA1957; AbD Serotec; RRID:AB_322219) at 1:250, chicken anti-tyrosine hydroxylase (Cat. No. TYH; Aves Labs Inc; RRID:AB_10013440) at 1:1000; rabbit anti-cleaved caspase-3 (Cat. No. 9661S; Cell Signaling Technologies; RRID:AB_2341188) at 1:400. Species-specific secondary antibodies (Jackson ImmunoResearch/Invitrogen) were used at a concentration of 1:250 at 4°C for 24 hr. Nissl staining (Neurotrace 530/615; Cat. No. N21482; Invitrogen) was performed according to manufacturer’s instructions.

Metabolic phenotyping

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The metabolic phenotyping experiments were run by the Metabolic Phenotyping Core of UTSW Medical Center in metabolic cages using a TSE Systems, Inc (Chesterfield, MO) indirect calorimetry system. Briefly, the day following a single concussive brain injury, mice were singly housed in shoebox-sized cages with wood chip bedding to acclimate for 5 days, followed by 5 days of experimental recording. O2 consumption and CO2 production were measured to determine energy expenditure and respiratory exchange ratio. Overall physical activity was determined through x, y, and z beam breaks. Body heat production, food intake, and water consumption were also measured. Data was normalized according to body weight.

Metabolome extraction and profiling

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Metabolomics profiling was performed by the Metabolomics Facility at the Children’s Medical Center Research Institute at UT Southwestern. Briefly, after concussive brain injury, brains were extracted, midbrains were dissected and flash frozen in liquid nitrogen. Tissue was then homogenized in a Precellys homogenizer at 5000 rpm for 30 s (2x) with a 20 s pause at 4°C. The metabolome was then extracted with an 80% ice cold solution of methanol and ddH2O. The metabolite containing supernatant was transferred into a clean centrifuge tube and desiccated for 12 hr. Metabolome profiling was done via LC-MS, normalized to total ion current and SIMCA analyzed.

Protein extraction and western blotting

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Tissue or cells were homogenized in a Precellys homogenizer at 4 °C with ceramic beads at 5,000 rpm for 30 s (2x) with a 20 s pause using RIPA lysis buffer (150 mM NaCl, 5 mM EDTA, 50 mM Tris, 1% NP-40, 0.5% sodium deoxycholate, 1% sodium dodecyl sulfate (SDS), final pH 8) supplemented with cOmplete EDTA-free mini-protease inhibitor cocktail (Cat. No. 11836170001; Roche). Extracts were created in the presence of the 1% SDS detergent to ensure protein linearization and inactivation of phosphatases (Stinson, 1984). Protein samples were centrifuged for 10 m at 10,000 rcf at 4 °C and the supernatant was used for protein quantification via Pierce BCA protein assay (Cat. No. 23225; Thermo Fisher Scientific). All sample concentrations were standardized and diluted in sample buffer. Samples were boiled at 90°C for 10 m, resolved by SDS-PAGE, transferred to nitrocellulose membranes and subject to western blot analysis. SDS-PAGE gels (10%) were prepared the night before and stored at 4°C. The EZ-Run pre-stained protein ladder (Cat. No. BP3603500; Thermo Fisher Scientific) was loaded and electrophoresis was performed at 100 V until the 40 kDa protein standard reached the bottom of the gel. All antibodies were prepared in 5% BSA/PBST. Mouse anti-αTubulin (Cat. No. T6074; Sigma; RRID:AB_477582) was used at 1:10000, rabbit anti-phospho-PDHEα1 (Cat. No. 31866S; Cell Signaling Technologies; RRID:AB_2799014) was used at 1:1000, and rabbit anti-HIF1α (Cat. No. 3716S; Cell Signaling Technologies; RRID:AB_2116962) was used at 1:1000. Western blots were quantified using Image Studio software (LI-COR Biosciences, Lincoln, NE). Quantified bands of interest were standardized based on band signal intensity of αTubulin.

ATP levels measurement

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ATP levels were measured with the CellTiter-Glo luminescence assay (Cat. No. G7570; Promega) according to manufacturer’s instructions. Brains were harvested, enzymatically dissociated with papain, and passed through a cell strainer (45 µm). Myelin was removed with the Debris Removal Solution (Cat. No. 130-109-398; Miltenyi Biotec). Freshly dissociated cells (100,000 cells) were resuspended in 100 µl of 1x PBS and transferred to a 96-well plate. A total of 100 µl of CellTiter-Glo reagent was added to make a final volume of 200 µl. The 96-well plate was placed on an orbital shaker for 2 m to induce cell lysis. After a 10 m incubation at room temperature, the plate was read for luminescence (0.25 s per well) in a CLARIOstar Plus microplate reader (BMG LABTECH, Ortenberg, Germany).

Reactive oxygen species detection

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Mitochondrial superoxide in mouse brain single cell suspension was measured with the MitoSOX Red superoxide indicator (Cat. No. M36008; Invitrogen) according to manufacturer’s instructions. Briefly, brains were harvested, enzymatically dissociated with papain, and passed through a cell strainer (45 μm). Myelin was removed with the Debris Removal Solution (Cat. No. 130-109-398; Miltenyi Biotec). Freshly dissociated cells were then incubated in a 5 μM MitoSOX solution in a 96-well plate for 10 m at 37°C. Each well was measured for fluorescence (Ex/Em: 510/580 nm). Reactive oxygen species were measured in C. elegans with the Amplex Red Hydrogen Peroxide/Peroxidase Assay Kit (Cat. No. A22188; Invitrogen). Worms were grown to Day one adults, transferred to a 2 ml centrifuge tube containing 500 µl of M9, and submitted to traumatic injury. Immediately after traumatic injury, equal number of worms (50 µl total M9 volume) were transferred to a 96-well plate (three technical repeats) and 50 µl of 100 µM Amplex Red reagent/0.2 U/ml horseradish peroxidase was added to a final concentration of 50 µM Amplex Red reagent/0.1 U/ml horseradish peroxidase. Each well was measured for fluorescence (Ex/Em: 530–560/~590 nm) every 92 s for 1.5 hr on a CLARIOstar Plus microplate reader (BMG LABTECH).

C. elegans strains

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C. elegans were maintained as previously described (Brenner, 1974). Worm strains were expanded on nematode growth media (NGM) plates supplemented with Escherichia coli OP50. Worm strains that were used for experimental purposes were grown on NGM plates supplemented with HT115 E. coli at 20°C (see RNAi Administration). For temperature sensitive strains, worms were grown at 25°C. The following C. elegans strains were generated in our laboratory and used in this study: PMD13 (egIs1[dat-1p::GFP]; rrf-3(b26); fem-1(hc17)), PMD74 (unc-119p::TOM20::KillerRed; egIs1[dat-1p::GFP]; rrf-3(b26); fem-1(hc17)), PMD63 (egIs1[dat-1p::GFP]; rrf-3(b26); fem-1(hc17); uIs69 [pCFJ90 (myo-2p::mCherry) + unc-119p::sid-1]; sid-1(pk3321)).

C. elegans trauma

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Trauma was administered to C. elegans as previously described (Egge et al., 2021). Briefly, age-synchronized worms were grown on empty vector (EV) or the respective RNAi until day 1 of adulthood. Temperature-restrictive strains were grown at 25°C to avoid generation of progeny. Worms were rinsed off growth plates in liquid M9 buffer (M9) and pelleted by centrifugation at 1000 x g for 30 s. 100 µl worm pellets were then transferred to 2 ml Precellys tubes (Cat. No. 02-682-556; Thermo Fisher Scientific) in a total volume of 500 µl of M9. Worms were subject to high-frequency, multidirectional agitation in a Precellys Evolution homogenizer (Cat. No. P000062-PEVO0-A.0; Bertin Instruments) for 16 s at 8600 rpm. Worms were pelleted at 1000 x g for 30 s and transferred to recovery plates containing the respective RNAi or EV on which they were cultured prior to trauma. Control, non-injured worms were suspended in M9 for comparable lengths of time and then transferred to recovery plates without having received trauma.

Confocal imaging

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Worms were paralyzed with 1 mM levamisole and mounted on microscope slides with M9 buffer. Brain sections were mounted in microscope slides with Fluoromount-G with DAPI (Cat. No. 00-4959-52; Invitrogen). Confocal images were collected with a Leica SP8 confocal microscope equipped with one photomultiplier tube, two super-sensitive hybrid HyD detectors, and highly stable lasers (UV/405 nm DMOD compact, 488 nm, 552 nm, 638 nm). The following Leica PL APO CS2 objectives were used to collect images: air 10x/0.40 NA, oil 40x/1.30 NA, oil 63x/1.40 NA. Super-resolution imaging was achieved with the Leica LIGHTING module built into the Leica LAS X software. Brain parenchyma images were collected as Z-stacks with 1 µm steps as follows: midbrain micrographs 20 µm/10x objective/5 tile Z-stacks, cortex micrographs 10 µm/40x objective/5 tile Z-stacks, hippocampus micrographs 20 µm/10x objective/8 tile Z-stacks, thalamus micrographs 5 µm/40x objective/2 tile Z-stacks. Number of tiles to be collected was determined by completely covering the brain structure of interest (i.e. midbrain 1x5 tiles, hippocampus 2x4 tiles). Tiled images were stitched with the Leica LAS X software. Cell culture micrographs were collected from cells grown on glass coverslips with a total of 10 fields collected with a 40x objective for image processing and analysis. Brain parenchyma images are displayed as maximal intensity projections of Z-stacks. Cell culture images are displayed as single plane micrographs. All confocal images were processed and analyzed with ImageJ (NIH, Bethesda, MD; RRID:SCR_003070).

Motor analysis (Rotarod)

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Motor deficits in mice were measured by Rotarod (Cat. No. 76–0770; Harvard Apparatus) with a rod diameter of 3 cm and a rod height of 20 cm. Animals were not trained on a rotating rod but rather exposed to a static Rotarod machine 2 days before testing for 2 m, three times daily. For testing, animals were placed on the rod with accelerating speed (0–24 rpm in 120 s) for all experiments. Latency to fall was recorded electronically in seconds by the apparatus and values were averaged per group for reporting. The clock was stopped if an animal held to the rod on two consecutive rotations and if the animal failed to fall after 120 s. Animals were returned to their cage after each trial. Ambulation was measured via laser beam breaks for 5 days in mice housed individually in cages equipped with laser beams and other probes meant to determine changes in metabolism.

RNA extraction

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Four biological repeats of age-matched adult Surf1+/+ and Surf1-/- brains were collected for each of the following conditions: Surf1+/+ uninjured, Surf1+/+ injured, Surf1-/- uninjured, Surf1-/- injured at time-points 2 hr post-injury and 7 days post-injury. Mouse brains were harvested in TRIzol (Cat. No. 15596018; Thermo Fisher Scientific), flash-frozen in liquid nitrogen, and stored at −80°C. Brains were triturated with syringes and freeze-thawed three times, followed by a chloroform/isopropanol extraction process. The RNA pellets were washed twice with ethanol, air-dried, and reconstituted in 20 μl ddH2O. RNA quality (260/280, 260/230 ratio) and concentration was determined using the DS-11 FX+ spectrophotometer (DeNovix, Inc, Wilmington, DE).

Illumina sequencing RNAseq analysis

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Quality control, mRNA purification, and paired-end 150 bp Illumina sequencing were performed by Novogene (Sacramento, CA). mRNA was enriched using oligo(dT) beads, randomly fragmented in fragmentation buffer, and reverse transcribed to cDNA using random hexamers. Following first-strand synthesis, Illumina sysnthesis buffer was added with dNTPs, RNase H, and E. coli polymerase I to synthesize the second strand by nick-translation. The cDNA library was purified, underwent terminal repair, A-tailing, and ligation of adapters before PCR enrichment. The cDNA library concentration was quantified with a Qubit 2.0 fluorometer (ThermoFisher Scientific) and sized with an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA). RNAseq statistical analysis was performed using CLC software (version 9.0, CLC Bio, Aarhus, Denmark). Data is presented as reads per kilobase million (RPKM) with values normalized to control levels and made relative to 1.

Single nuclei isolation, purification, and RNA sequencing

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Single nuclei isolation methods were adapted from 10x Genomics protocols CG000212 Rev B and CG000124 Rev D. Surf1+/+ and Surf1-/- brains were harvested under cold sterile 1x Dulbecco’s PBS (without Ca2+ and Mg2+). Immediately after harvesting, the cortex, olfactory bulbs and cerebellum were removed on an ice block and discarded. Brains were then triturated and lysed using the Nuclei PURE Prep Isolation Kit (Cat. No. NUC-201, Sigma). Briefly, brains were homogenized under cold Nuclei PURE Lysis Buffer and mechanically triturated with sterile pipettes and pipette tips of decreasing sizes until creating a uniform suspension. Suspensions were then filtered through 70 μm, 40 μm, and 30 μm cell strainers to remove cell debris. Myelin removal proceeded using the Debris Removal Solution (Cat. No. 130-109-398, Miltenyi) according to manufacturer’s instructions. Single nuclei were isolated and purified via density gradient centrifugation with the Nuclei PURE sucrose cushion solution (Cat. No. NUC-201, Sigma) at a centrifugation speed of 13,000 rcf. Nuclei was resuspended in 1x Dulbecco’s PBS supplemented with 1% bovine serum albumin and 20 U/μl RNase inhibitor (Cat. No. AM2694, Invitrogen). Single nuclei suspensions were submitted to the Next Generation Sequencing Core at UT Southwestern Medical Center for sequencing and library preparation. Briefly, nuclei were loaded with Single Cell 3’ Gel Beads into a Next GEM Chip G and run on the Chromium Controller. GEM emulsions were incubated and then broken. Silane magnetic beads were used to clean up GEM reaction mixture. Read one primer sequence was added during incubation and full-length, barcoded cDNA was then amplified by PCR after cleanup. Sample size was checked on the Agilent Tapestation 4200 using the DNAHS 5000 tape and concentration was determined by the Qubit 4.0 Fluorimeter (ThermoFisher) using the DNA HS assay. Samples were enzymatically fragmented and underwent size selection before proceeding to library construction. During library preparation, Read two primer sequence, sample index, and both Illumina adapter sequences were added. Subsequently, samples were cleaned up using Ampure XP beads and post library preparation quality control was performed using the DNA 1000 tape on the Agilent Tapestation 4200. Final concentration was ascertained using the Qubit DNA HS assay. Samples were loaded at 1.6 pM and run on the Illumina NextSeq500 High Output Flowcell using V2.5 chemistry. Run configuration was 28x98x8.

Single nuclei RNA-seq data analysis

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Raw gene counts were obtained by aligning the FASTQ files to Mus musculus mm10 as reference genome using CellRanger Software (v5.0.0) and then analyzed using Seurat-3.2.0 (Stuart et al., 2019). Genes detected in <0.2% of the nuclei and from mitochondrial and sex chromosomes (X and Y) were filtered out. Nuclei that had >12,000 UMI, <300 genes, and/or >12% mitochondrial content were further excluded. After quality control, 5140 nuclei (primary dataset) were further analyzed for their gene expression profiles. Post filtering, the expression values were log-normalized, scaled with a factor of 10,000 and regressed to covariates (percent mitochondrial content and number of genes per nuclei), with Seurat's SCTransform method and integrated. The nuclei were then assessed by Principal Component Analysis (PCA) dimensionality reduction, followed by shared nearest neighbor (SNN) modularity optimization (Louvain algorithm) based clustering algorithm to identify the clusters. The clusters were visualized using the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction technique. Cell types were assigned to clusters based on enrichment of marker genes from top expressed genes as follows: Excitatory neurons (Rbfox3), GABAergic neurons (Gad1,Gad2), Dendrocytes (Cacng4), Astrocytes (Gaj1, Aqp4), and Dopaminergic neurons (Slc6a3, Slc18a2, Ddc). Astrocyte and neuronal clusters were subclustered (secondary dataset) and then exclusively studied using the same approach as described above. Pairwise differential gene expression analysis tests were performed with Wilcox test within Seurat. The expression of specific genes within clusters were visualized using the DotPlot function in Seurat. The average expression of these genes in the dot plot was calculated by using the normalized gene counts, natural-log transforming them using log1p, and then scaling them using a z-transformation.

RNAi administration

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Worm strains were grown on HT115 E. coli harboring RNAi constructs from either the Ahringer or Vidal RNAi libraries (Rual et al., 2004). The L4440 empty vector (EV) RNAi construct was used for control treatments. RNAi strains were grown in small cultures before inoculating larger cultures in Terrific broth (TB) and grown for 15 hr on an orbital shaker at 37°C. After 15 hr, cultures were treated with 1 mM IPTG and incubated for an additional 4 hr at 37°C to induce expression. Cultures were then centrifuged at 4000 x g and bacterial pellets were re-suspended to specific concentrations in TB before being spread on 100 mm NGM plates containing a final concentration of 1 mM IPTG and 0.1 mg/ml carbenicillin. Optical density (OD600) of RNAi-producing bacterial cultures was used to standardized and equalize cell number for multiple RNAi construct combinations. For titrated single RNAi construct treatments, induced HT115 bacteria expressing a single RNAi construct was diluted with L4440 empty vector HT115 bacteria.

Large-particle flow cytometry

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Large-particle flow cytometry was performed as previously described (Egge et al., 2019). Briefly, flow cytometry of C. elegans was performed on a COPAS FP-250 flow cytometer (Union Biometrica, Holliston, MA) and an automated sample introduction system LP Sampler (Union Biometrica) fitted with 96-well plates. M9 buffer was utilized as the sample solution for worm flow. The COPAS GP sheath reagent (PN: 200-5070-100, Union Biometrica) was used as sheath solution. Prior to every run, laser power and flow rates are calibrated by use of GP control particles (Cat. No. 310-5071-001; COPAS Biosorter, Union Biometrica) as recommended by the manufacturer. From experiment to experiment, we maintain a consistent PTM laser power for each respective fluorophore. Flow data was collected in FlowPilot software (Union Biometrica). Details regarding analysis and normalization of data obtained from large-particle cytometry are found under the ‘Statistical analysis’ methods section.

Cell culture

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HEK293t cells were obtained from ATCC (Cat. No. CRL-3216; RRID:CVCL_0063) and cultured in DMEM/F-12 (Cat. No. 11320033; Gibco) supplemented with 10% Fetal Bovine Serum and 1x Penicillin-Streptomycin (Cat. No. P4333; Sigma). Mouse embryonic fibroblasts (MEF) were obtained via timed mating by detection of a vaginal plug was used to obtain embryonic day 13.5 embryos. Embryos were dissected in cold 1x PBS to remove the head and all inner organs, leaving only the carcass. After finely mincing the carcass in cold 1x PBS, the pieces were allowed to settle, and PBS was aspirated. The tissue was then enzymatically dissociated with 1 ml of 0.25% trypsin/EDTA at 37°C for 5 m. The tissue was then mechanically dissociated using a 1 ml pipette tip until homogeneous and passed through a nylon cell strainer (45 μm). Cells were cultured in DMEM-Hi glucose (Sigma-Aldrich; 4.5 g/L glucose, supplemented with L-glutamine and sodium pyruvate), 10% fetal bovine serum (heat inactivated), and supplemented with penicillin streptavidin. All cultures were confirmed to be negative for mycoplasma with the MycoAlert Mycoplasma Detection Kit (Cat. No. LT07-218; Lonza).

Oxygen consumption rate

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Surf1+/+ and Surf1-/- mouse embryonic fibroblasts were plated on a clear bottom 96-well plate (100,000 cells/well). Each well was treated with 100 ng/ml of phorbol myristate acetate (PMA) for 1 hr. The PMA containing medium was then replaced with fresh medium and the basal oxygen consumption rate was measured with the Cayman Chemical Oxygen Consumption Rate Assay Kit (Cat. No. 600800) according to manufacturer’s instructions.

Media acidification assay

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Mouse embryonic fibroblasts (MEF) were cultured in a 96-well plate. Each well contained equal MEF numbers cultured in DMEM/F-12 (Cat. No. 11320033; Gibco) supplemented with 10% Fetal Bovine Serum. DMEM/F-12 used contains phenol red as a pH indicator with pH ranges of 6.8 (yellow) to 8 (red). Cells were cultured for 5 d in a cell culture incubator, at 37°C with 5% CO2. After 5 days, the Optical Density was immediately measured in a CLARIOstar Plus spectrometer (BMG LABTECH) by performing a spectral scan between 220 nm and 1000 nm with a 1 nm resolution. Data was normalized to 1x105 cells for analysis.

LDH activity assay

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The maximum LDH activity was measured with the Pierce LDH Cytotoxicity Assay Kit (Cat. No. 88953; Thermo Fisher Scientific) according to manufacturer instructions. Briefly, 20,000 MEF were cultured overnight in a flat bottom, 96-well plate at 37 °C, 5% CO2. Cells were lysed with lysis buffer provided in the kit and 50 μl of supernatant was transferred to a fresh 96-well flat bottom plate. The kit’s proprietary reaction solution was added to the supernatant and incubated at room temperature for 30 m. After stopping the reaction, absorbance was measured at 490 nm and 680 nm in a CLARIOstar Plus microplate reader (BMG LABTECH). LDH activity was determined by subtracting the 680 nm from the 490 nm absorbance values.

Statistical analysis

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All statistical analyses were performed using Prism eight software (GraphPad, San Diego, CA) unless noted. Student’s t-test was used to compare means between two normal populations. Mann-Whitney U test was used to compare differences in the dependent variable between two groups. Post-hoc analysis performed after ANOVA included Dunnett’s multiple comparison (to compare means from several experimental groups against a control group mean). Tukey’s multiple comparisons test (to compare all possible pairs of means).

Statistical analysis of large-particle flow cytometry data was performed in Prism and Excel (Egge et al., 2021). To evaluate large worm numbers across biological repeats, error propagation was performed according to the general formula:

δR=(RX*δX)2+(RY*δY)2+

where δR, the total error within each independent repeat, is a function of each independent variable (X, Y …). For the particular case of error propagation for the Dopaminergic GFP Index within each of multiple biological repeats, error is further added in quadrature and the function becomes:

δR=X(δX1-EV)2

where δX represents the error and X contains all biological repeats. EV is a constant determined by the relative loss of fluorescence observed in injured worms grown on EV bacteria during that experiment. Lastly, the total error within each repeat was added to the error between repeats in quadrature.

Data availability

All datasets are submitted to GEO under accession numbers GSE173431 and GSE179905.

The following data sets were generated
    1. Douglas PM
    2. Fonseca RS
    (2021) NCBI Gene Expression Omnibus
    ID GSE173431. Glycolytic preconditioning in astrocytes mitigates trauma-induced neurodegeneration.
    1. Douglas P
    2. Konopka G
    3. Sivaprakasam K
    4. Fonseca RS
    (2021) NCBI Gene Expression Omnibus
    ID GSE179905. Glycolytic preconditioning in astrocytes mitigates trauma-induced neurodegeneration.

References

    1. Brenner S
    (1974)
    The genetics of Caenorhabditis elegans
    Genetics 77:71–94.
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    1. Giza C
    2. Greco T
    3. Prins ML
    (2018)
    Concussion: pathophysiology and clinical translation
    In: Koehler P. J, editors. Handbook of Clinical Neurology. Elsevier. pp. 51–61.
    1. Giza CC
    2. Hovda DA
    (2001)
    The neurometabolic cascade of concussion
    Journal of Athletic Training 36:228–235.
    1. Hall ED
    2. Braughler JM
    (1993)
    Free radicals in CNS injury
    Research Publications - Association for Research in Nervous and Mental Disease 71:81–105.
    1. White JG
    2. Southgate E
    3. Thomson JN
    4. Brenner S
    (1986) The structure of the nervous system of the nematode Caenorhabditis elegans
    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 314:1–340.
    https://doi.org/10.1098/rstb.1986.0056

Decision letter

  1. Scott F Leiser
    Reviewing Editor; University of Michigan, United States
  2. Matt Kaeberlein
    Senior Editor; University of Washington, United States

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

Acceptance summary:

The results presented in this work will be of broad interest to readers in the field of neurodegeneration. The authors detail a neuronal subtype specific response to blunt force trauma in two different animal models. They also uncover a conserved shift in cellular metabolism that plays a protective role against neuronal death following injury.

Decision letter after peer review:

Thank you for submitting your article "Glycolytic preconditioning in astrocytes mitigates trauma-induced neurodegeneration" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Matt Kaeberlein as the Senior Editor. The reviewers have opted to remain anonymous.

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

Summary:

The reviewers and I are in agreement that the subject of the manuscript is of high interest and that the data largely support the assertions, but some assertions are less well supported and methodological details and some controls are insufficient. We believe this is a worthwhile manuscript for publication in eLife, but should be revised with some additional data and controls. In terms of additional experiments/data, we would ask that you focus on those that are crucial to the claims made within the text. These include the essential revisions listed below. While many of these are textual changes, some of these will require new data including data the first 4 points listed below. It was also pointed out that the n was very low (n=3) for many experiments, and while it is not feasible to ask to increase the n across all experiments, please very clearly note in the text where the low n could affect interpretation or reproducibility of the results. All of the reviewer comments are also listed below, and we would also strongly recommend making changes for the additional clarity and typographical changes recommended in the reviews that would improve the manuscript.

Essential revisions:

Additional experiments:

1. Caspase 3 staining following injury should be done for thalamus and cortex of injured mice to show that the neurodegeneration is midbrain dopaminergic neuron specific, rather than just the hippocampus, which is farther than the midbrain from the site of impact.

2. Clarification or modification/retesting of the worm cox5b RNAi protocol: As written, worms are injured and then placed into RNAi lawns to suppress de novo translation of cox5b to the effect of mitigating ROS production. However, this does not make sense, since ROS production should be able to be generated with existing mitochondrial protein complexes and not necessarily require new protein. This would suggest the vast literature of "tired old mitochondrial proteins are the cause of ROS" is collectively wrong. Resolve this apparent conflict. Possible methods may use cycloheximide to halt general translation or use of tet on/off systems controlling target protein expression.

3. Restaining and/or reimaging figure 1C (see below).

4. Panel 5B: Please run all samples on a single western blot and show results as a single boxed area. When western blots are presented such as this it is difficult to determine an effect of "injury".

Textual revisions: I strongly recommend reading reviewer 3's thorough review and verifying/modifying all of the typographical / nomenclature/ clarity changes requested.

1. Line 46: Unclear statement. Clarify

2. Line 113-115: please temper statement

3. Line 139: modify for clarity.

4. Line 159 to 161: modify for clarity.

5. Line 219 to 221: temper statement.

6. Line 82: Change "degeneration" to "injury".

7. Line 175/190: Was a correlation statistically evaluated? If so, please report results. If not, change wording. Perhaps use "corresponded".

8. Line 203: Replace "activation" with something more precise such as "upregulation" or "increased transcription". The word activation implies signaling or physiological response.

9. Line 212: Change "spatial" to "cellular".

10. Line 219 to 221: soften claim.

Figure/Table Revisions:

Please provide timepoints for all relevant figures where not listed. Also please carefully label control and TBI mice in each panel where relevant, and provide n where not clear.

Table 1. Better describe and clarify if parameters are for mouse brain or rat brain. If they are for rat, then provide the correct parameters for mouse brain. If the title is in error, then correct the title.

Figure 1. Panel C: Requires a reference map or annotations directly on the image to determine the precise anatomical location(s) pictured in the micrographs. If this is the midbrain Substantia nigra, please label SNp, SNr, VTA, etc. If this is not precisely in that area, additional tissue staining may be required to identify cell types. TH (tyrosine hydroxylase) is a marker of catecholaminergic cells and not specifically a marker of dopaminergic cells. Staining with TH will therefore include dopaminergic and noradrenergic cells which vary in their distribution throughout the midbrain to pons. If needed use dopamine β-hydroxylase (DBH) in addition to TH to differentiate dopaminergic cells from noradrenergic cells.

Panel E: It is unusual for uninjured mice to fall off the rotorod so quickly, especially when they were previously trained on the task two days prior. Provide data for all tested speeds if available.

Both Table 1 and Figure S2 indicate the finite element model includes or can include analysis of cerebellum and brainstem. Data on brainstem and cerebellum should be supplied as they are significant areas of innervation to and projections from the areas under study. Further, they are intimately involved in both righting reflex and locomotion and may lend to the interpretation of this and future studies.

Figure 3C: Similar to Figure 1, provide outlines of regional anatomy and quantitation of TH vs "other cells". For 3C-F, baseline results should be reported.

Figure 4a: Include latency information.

Figure S1: Provide information and details/controls for Panel F (see reviewer 3 comment).

Methods Revisions:

Details in the methods section need to be better described throughout, examples of which include:

– The C. elegans blunt force trauma model is not well described in the methods section. Additionally, since N. Egge et al., 2021 already performed an RNAi screen of dopaminergic neuron GFP retention following injury (but used a different quantification metric), it would be informative to know how strong the effect of cox5b RNAi is in comparison to the top hits in the previous screen.

– In Figure S1C, how were the neurons counted? Is there a co-stain?

– In Table 1, the material properties of rat brain regions are listed, is this a fair comparison to make while modeling the mouse brain?

– Surf KO mice were described to have rotarod defects in C. Dell'agnello et al., 2007, so in figure 3E and F, it would be useful to know the baseline levels of performance prior to injury.

– Unclear how the experiment in Figure S3E was performed, and the conclusions derived from this experiment are also confounding.

– Single nuclei isolation, purification, and RNA sequencing: The reported dissection method will produce single nuclei representing subcortical / diencephalic structures, midbrain, pontine and medullary tissues. Interpretation of snRNAseq results cannot therefore be attributed to represent midbrain or striatal cell populations.

– Statistical Analysis: It is inadequate to state "Post-hoc analysis was determined by Prism recommendation". Please provide details on the types and specific uses of post-hoc analyses.

– Motor Analysis: What make, model and manufacturer is the Rotorod being reported? On the testing day, how many times were the mice tested and does the reported "latency to fall" represent the average? If it does not represent the average, what does it represent?

– Confocal imaging: Was a single image plane acquired or were they z-stack images? How were the images controlled between subjects / slices? If they were z-stacks, what was the step size? How were the images displayed in the manuscript- were they single planes or z-stacks? Were all the image volumes identical?

– Protein extraction and western blotting: Reported methods do not appear to include the use of a phosphatase inhibitor. Therefore western blots showing any changes in phosphorylation may be related more to target protein abundance and not differential phosphorylation. Westerns blots therefore ought to include probes against the non-phosphorylated "total" target protein then in order to help interpret apparent expression changes.

– Media acidification assay: Product numbers and formulation of DMEM is critical for the interpretation of the reported method. At present the method description appears inadequate to provide the type of data desired or reported. Further, cell counts must be performed after the assay is performed since mitochondrial mutations are known to alter population growth kinetics. Therefore acidification results ought to be normalized against population numbers to control for this confound and allow an accurate interpretation of the results.

– Large-particle flow cytometry: How were data calibrated and normalized?

– Closed-head traumatic brain injury: What % isoflurane, what flow rate oxygen, were the mice connected to isoflurane when impacted or not, was the scalp intact (as assumed to be but not explicitly stated)?

– Provide additional experimental details and OCR profiles for Figure 4C OCR measurement.

Reviewer #1:

Fonseca et al., cleverly utilize C. elegans and mouse models of blunt force trauma to present a elcohesive picture of the signaling pathways underlying neurodegeneration following concussion.

The first major conclusion of the paper is that trauma induced neurodegeneration is neuron subtype specific, with dopaminergic neurons being the most susceptible after injury. This claim is convincingly validated in a C. elegans model of blunt force trauma that they previously developed (N. Egge et al., 2021). The authors use a weighted drop model of traumatic brain injury in mice to find a similar hypersensitivity to neurodegeneration in dopaminergic midbrain neurons, but it is unclear if this is specific as other relevant brain areas like the thalamus and cortex have not been assayed.

The authors also identify a key neuroprotective role for ETC complex IV inhibition following injury. Inhibition of ETC Complex IV results in an astrocyte mediated Warburg like shift in metabolism from oxidative phosphorylation to glycolysis, which is sufficient to counteract an increase in toxic ROS production in injured neurons. These observations are reached using an impressive array of experimental techniques, ranging from metabolomic profiling to single nuclei RNA sequencing, with the authors providing multiple lines of evidence in both C. elegans and mouse models in support of these novel findings.

Additional experiments:

– Caspase 3 staining following injury must be done for thalamus and cortex of injured mice to show that the neurodegeneration is midbrain dopaminergic neuron specific, rather than just the hippocampus, which is farther than the midbrain from the site of impact.

– Is the Warburg shift in C. elegans dependent on non-neuronal cells? If possible, could you perform cell specific RNAi for cox5b and pdp-1 in neurons vs glial sheath cells or the epidermis to test?

Experimental methods:

– The C. elegans blunt force trauma model is not described in the methods section. Additionally, since N. Egge et al., 2021 already performed an RNAi screen of dopaminergic neuron GFP retention following injury (but used a different quantification metric), it would be informative to know how strong the effect of cox5b RNAi is in comparison to the top hits in the previous screen.

– Representative images of the GABAergic, cholinergic and serotonergic C. elegans neurons following injury would be helpful.

– In Figure S1C, how were the neurons counted? Is there a co-stain?

– Image quality is poor in Figure S1E.

– In Table 1, the material properties of rat brain regions are listed, is this a fair comparison to make while modeling the mouse brain?

– Surf KO mice were described to have rotarod defects in C. Dell'agnello et al., 2007, so in figure 3E and F, it would be useful to know the baseline levels of performance prior to injury.

– Unclear how the experiment in Figure S3E was performed, and the conclusions derived from this experiment are also confounding.

– A major experimental choice that is not explained is the reason why two different components of complex IV are used in C. elegans and mouse.

– Unclear about what is being compared in Figure 4F.

– Label uninjured vs injured in Figure 5 E and F.

Writing:

– In the introduction section, a paragraph summarizing your findings would be helpful in orienting the reader to the relevance of the work in relation to previous studies.

– There are a lot of references to Parkinsons disease mediated dopaminergic neurodegeneration, but the link to the current findings is somewhat tenuous, especially as the behavioral deficits observed in the mouse injury model are only a small subset of the motor and cognitive deficits seen in Parkinsons models.

Reviewer #2:

This study identified a neuroprotective mechanism using experimental models of blunt force trauma in C. elegans and concussion in mice. Interestingly, reducing Mitochondrial CIV function elevates mitochondrial-derived reactive resulted in neuroprotective effect via ROS and HIF1 mediated metabolic transition. Mechanism studies on ROS and metabolic regulation are interesting. The worm and mouse models are well established. The mouse model to reduce rather than ablate cytochrome C oxidase activity is a good choice.

1. Line 124, the authors may add a few more words to describe how cox-5b is identified, as a small-scale screening has been performed.

2. Figure 3C-F, baseline results should be provided.

3. The authors may need to determine whether nuo-2, sdhd-1, cyc-1, atp-3 RNAi treatments reduce ROS. Not sure if ROS suppression is cox-5b RNAi specific.

4. If there is a selective vulnerability of different neural subtypes, results using MEFs in figure 4 may not explain the "selective vulnerability" phenotype. Are dopaminergic neurons hypersensitive to ROS after brain injury? The mechanisms need to be better explained.

5. In figure 5, if HIF1 is the "key" regulator, does HIF1 RNAi prevent glycolytic preconditioning and neuroprotection? Similarly, does hypoxia exhibit neuroprotective effect?

Reviewer #3:

Traumatic brain injury (TBI) is a significant source of global behavioral, neurological and neurodegenerative morbidity. Though risk factors for poor outcomes following TBI, such as those thought to precipitate Alzheimer's disease are intensely studies, conserved mechanisms of injury and resiliency factors with the potential to prevent poor outcomes remain largely unknown. In this study by Fonseca and colleagues, the study team leverages a combination of model systems (e.g. C. elegans, mice, cell systems) to identify the selective vulnerability of dopaminergic neurons following physical trauma. Through a series of well-reasoned and articulate experiments, the study team presents evidence that suppression of function at the mitochondrial electron transport chain complex IV may reduce the loss of dopaminergic neurons following TBI, in part by driving astrocyte-mediated lactate shuttling to reduce oxidative phosphorylation in vulnerable neurons. This is potentially a very significant finding with wide ranging implications not only for acute injury care but potentially chronic neurodegenerative disease as well. A clear strength of the study is its approach to identifying evolutionarily-conserved mechanisms of selective dopaminergic vulnerability to TBI, which will likely be critical to identifying actionable targets in human TBI. The study is well written with an interesting and thoughtful narrative. The weakness of the paper is primarily the under-reporting of methodological details essential to reproducing the work, inadequate controls to verify results, and a paucity of data supporting major claims and conclusions.

Recommendations for the authors:

Line 46: Unclear statement. Clarify.

Line 82: Change "degeneration" to "injury".

Line 97 to 100: TBI-induced deficits in voluntary movement are well established and may have multiple causes unrelated to SN / DA injury. Interestingly, changes in "disinhibition and risk seeking", another classical Parkinsonian trait and more cleanly associated with dopaminergic dysfunction, has been reported in TBI in human subjects (self-report) and mouse TBI models (using novel object recognition tests). Provide results of novel object recognition tests to demonstrate unencumbered behavioral / functional impairment related to dopaminergic injury / degeneration in this mouse model. Evaluate the correlation between behavioral results with the degree of dopaminergic loss across SNr, SNc, VTA, etc.

Line 113-115: Caution and conservativeness is warranted in this closing statement. This study has not adequately compared and contrasted the profiles of neuronal injury across the SNc/Midbrain with those of the thalamus and therefore its inappropriate to suggest that the inherent physiological properties of dopaminergic neurons sensitizes them to biomechanical insults [as compared with the thalamus]. Indeed several studies have noted substantial thalamic alterations associated with repeated TBI, whereas few have identified dopaminergic pathology.

Line 139: As written, your statement makes it sound as though though evolution has selected for the protection of DA neurons against TBI by reduction of cytochrome c oxidase activity, which I do not think you intend to convey. Perhaps, "suppressing trauma-induced degeneration of dopaminergic neurons through reduction in cytochrome C oxidase activity is sufficient to conserve dopaminergic function in both C. elegans to mice."?

Major concern Line 150 to 151: If I am understanding correctly… As written, worms are injured and then placed into RNAi lawns to suppress de novo translation of cox5b to the effect of mitigating ROS production. However, this does not make sense, since ROS production should be able to be generated with existing mitochondrial protein complexes and not necessarily require new protein. This would suggest the vast literature of "tired old mitochondrial proteins are the cause of ROS" is collectively wrong. Resolve this apparent conflict. Possible methods may use cycloheximide to halt general translation or use of tet on/off systems controlling target protein expression.

Line 159 to 161: Unclear / confusing. For clarity, please reword or break into separate sentences.

Line 175: Was a correlation statistically evaluated? If so, please report results. If not, change wording. Perhaps use "corresponded".

Line 190: "correlated". Same comment as Line 175.

Line 203: typo "Pdk2". Change to "pdk2" (standard gene symbols are all lower case by convention). Replace "activation" with something more precise such as "upregulation" or "increased transcription". The word activation implies signaling or physiological response.

Line 204: typo "Pdp1". Change to "pdp1" (standard gene symbols are all lower case by convention).

Line 212: Change "spatial" to "cellular". snRNAseq provides cellular data and though cellular nuclei are physically separate from one another, snRNAseq does not preserve accepted information on spatial relationships such as that provided by microscopy.

Line 219 to 221: "Through spatial resolution…" Identifying a pro-Warburg shift in astrocytes driven by Surf1 KO, does not demonstrate that this is a pathway for naturally occuring Warburg phenomenon. Suggest, softening by changing text to read "…may be initiated by electron…". Further, though the presented data indicates the Warburg shift is princpally manifest in astrocytes, it does not rule out the initiation of this process by extrinsic factors such as exosomes.

Line 229: Hif1alpha… gene or protein?

Line 236: Hif1alpha… gene or protein?

Line 239: Hif1alpha… gene or protein?

Line 245: change "pdp-1" to "pdp1".

Line 249: change to "… trauma-induced neurodegeneration of dopaminergic neurons…"

Table 1.

Are these parameters for mouse brain or rat brain? If they are for rat, then provide the correct parameters for mouse brain. If the title is in error, then correct the title.

Provide a descriptor as to what each parameter is and what higher or lower numbers indicate for the lay reader.

Figure 1.

Panel C: Requires a reference map or annotations directly on the image to determine the precise anatomical location(s) pictured in the micrographs in order to inform the reader as to the principle cell populations involved. If this is the midbrain Substantia nigra, please label SNp, SNr, VTA, etc. If this is not precisely in that area, additional tissue staining may be required to identify cell types. TH (tyrosine hydroxylase) is a marker of catecholaminergic cells and not specifically a marker of dopaminergic cells. Staining with TH will therefore include dopaminergic and noradrenergic cells which vary in their distribution throughout the midbrain to pons. If needed use dopamine β-hydroxylase (DBH) in addition to TH to differentiate dopaminergic cells from noradrenergic cells.

Use of cleaved caspase-3 staining is at insufficient resolution to determine whether TH cells (or an unrelated phenotype) are positive for cleaved caspase-3. Quantitation of colocalized cleaved caspase-3 across TH+, astrocytes, vs other cell types should be supplied.

Use of additional markers are required to verify the apoptotic cells are dopaminergic neurons and not simply other cell types such as infiltrated peripheral immune cell subsets (which are known to express tyrosine hydroxylase and apoptosize during the early phases of brain injury resolution).

Panel C: At what time point?

Panel D: At what time point? How many slices per animal were examined?

Panel E: It is unusual for uninjured mice to fall off the rotorod so quickly, especially when they were previously trained on the task two days prior. Were the animals placed on the rod "backwards"? Provide data for all tested speeds if available.

Figure 2.

Between A and B: There is an illustrated "block". I am assuming this is either an illustrated "typo" or the impactor head. Please define or remove this "block".

C. Both Table 1 and Figure S2 indicate the finite element model includes or can include analysis of cerebellum and brainstem. Data on brainstem and cerebellum should be supplied as they are significant areas of innervation to and projections from the areas under study. Further, they are intimately involved in both righting reflex and locomotion and may lend to the interpretation of this and future studies.

Figure 3.

Panel A and B: At what time point? Also, provide either quantitative real-time PCR or Western blot quanitation of relative RNAi efficiency.

Panel C: Similar to Figure 1, provide outlines of regional anatomy and quantitation of TH vs "other cells" costaining along with "zoomed in" insets to verify colocalization of cleaved caspase-3 with dopaminergic neurons.

Panel C: At what time point are the brain slices representing?

Panel D: Are these TBI or control mice?

Panel F: Are these TBI or Control mice? If TBI, how long after TBI are the mice given this test?

Figure 4.

Title: Change "Reducing cytochrome C…" to "Decreasing cytochrome C oxidase expression…". This helps avoid confusion between electrochemical "reductions" involving cytochrome C oxidase and changes in cytochrome C protein expression levels, such as the use of RNAi intends to cause.

Panel A: Was there no latency between when the trauma occured and when they began peroxide measurements? If so, include that in the timescale.

Panel C: Data lines are unlabeled as to the reagent used to test OCR. Graph should consist of control, actinomycin, FCCP and Oligomycin treatment conditions for each genotype. It is impossible to conclude that this even has living cells without providing more data.

Recommend using a dynamic Seahorse ECAR / OCR assay. Please provide the full Seahorse Glycolytic ECAR and Mitochondrial respiratory OCR profiles.

Panel E: n=3. Is this technical or biological replicates? How was this measured (mention in legend)?

Figure 5.

Panel B: Please run all samples on a single western blot and show results as a single boxed area. When western blots are presented such as this it is difficult to determine an effect of "injury". As it is, the reader cannot conclude that there's any difference between conditions other than a main effect of genotype.

Figure Legend Text for Panel C: remove the word "neuronal" or replace with "neural".

Panel C (figure): Gene symbols are not capitalized by convention.

Panel F: Requires single cell quantitation across DA neurons, astrocytes, others to determine treatment effects differentially affect apoptosis in a partcular target cell type.

Panel G and H: Change pdp-1 to pdp1.

Figure S1.

Panel C: How many slices per mouse were examined?

Panel D and E: What study timepoint do these micrographs represent? How many slices per mouse were examined?

Panel F: It is unusual to detect no change across a number of microglia enriched genes in areas that are experiencing neuronal apoptosis, especially in CD68 which would be under greater use in the elimination of cellular debris. How was this data normalized? Was it library normalized per cell? If not, consider that analytical approach. What type of RNAseq and where was the material collected from? (I am assuming snRNAseq from the described subdissection) Provide positive and negative controls to prove the assay worked. Ensure the microglia being reported on are from the area under study and not from a broadly mixed tissue source, which may have originally come from "uninjured" locations by sheer random chance and may not represent the SN. Microscopy or morphometric analyses may be required to ensure proximity.

Figure S2.

No comments.

Figure S3.

Panel A: Are these Injury or control mice?

Panel B: Mortality should be represented as a curve across increasing impact force. For a first demonstration of mortality curves, ideally data from at least three different cohorts, with each cohort injured separately from one another on different days, should be supplied to allow the reader to determine the degree of reproducibility for both the execution of the method and the experimental measure being made.

Panels D and E: Panel D shows an apparent loss of DA neurons in injured mice with a compensatory upregulation of TH by remaining neurons 28 days following injury. Panel E quantifies the DA neuron loss for both Surf wt and KO mouse strains. What is the average immunofluorescent expression of TH by remaining DA neurons after injury and does it differ by strain?

Figure S4.

No comments

Figure S5.

Panel A: Are these control mice?

Panel B to E: At what timepoint after injury does this data represent?

Panel F and G: these panels appear to be misidentified in the figure legend. Please check.

Panel M: It does not seem reasonable to acheive a snRNAseq of midbrain and striatal brain tissue without obtaining microglia and endothelial cells. Please reconcile this and report these cell types.

Methods:

Single nuclei isolation, purification, and RNA sequencing: The reported dissection method will produce single nuclei representing subcortical / diencephalic structures, midbrain, pontine and medullary tissues. Interpretation of snRNAseq results cannot therefore be attributed to represent midbrain or striatal cell populations.

Closed-head traumatic brain injury: What % isoflurane, what flow rate oxygen, were the mice connected to isoflurane when impacted or not, was the scalp intact (as assumed to be but not explicitly stated)?

Media acidification assay: Product numbers and formulation of DMEM is critical for the interpretation of the reported method. At present the method description appears inadequate to provide the type of data desired or reported. Further, cell counts must be performed after the assay is performed since mitochondrial mutations are known to alter population growth kinetics. Therefore acidification results ought to be normalized against population numbers to control for this confound and allow an accurate interpretation of the results. Confirmation of acidification assay should also be performed using a secondary method such as pH meter.

Statistical Analysis: It is inadequate to state "Post-hoc analysis was determined by Prism recommendation". Please provide details on the types and specific uses of post-hoc analyses.

RNAi administration: RNAi sequences and suppliers and concentrations must be provided.

Large-particle flow cytometry: How were data calibrated and normalized?

Motor Analysis: What make, model and manufacturer is the Rotorod being reported? On the testing day, how many times were the mice tested and does the reported "latency to fall" represent the average? If it does not represent the average, what does it represent?

Confocal imaging: Was a single image plane acquired or were they z-stack images? How were the images controled between subjects / slices? If they were z-stacks, what was the step size? How were the images displayed in the manuscript- were they single planes or z-stacks? Were all the image volumes identical?

Protein extraction and western blotting: Reported methods do not appear to include the use of a phosphatase inhibitor. Therefore western blots showing any changes in phosphorylation may be related more to target protein abundance and not differential phosphorylation. Westerns blots therefore ought to inculde probes against the non-phosphorylated "total" target protein then in order to help interpret apparent expression changes.

General comment: The study generally uses n=3 mice per group for many experiments. n=3 subjects per group are typically only acceptable with rare materials / subjects. I would argue that n=3 is inadequate for any mouse study.

https://doi.org/10.7554/eLife.69438.sa1

Author response

Additional experiments:

1. Caspase 3 staining following injury should be done for thalamus and cortex of injured mice to show that the neurodegeneration is midbrain dopaminergic neuron specific, rather than just the hippocampus, which is farther than the midbrain from the site of impact.

We have included new indirect immunofluorescence micrographs and quantifications for the respective brain regions (thalamus and cortex). This additional data complements our existing analysis of the midbrain and hippocampus. In brief, there was no significant change in immunostained cleaved caspase 3 signal intensity seven days post-concussion in both the thalamus or the cortex. These new data are included in the revised manuscript in Figure 1—figure supplement 1.

2. Clarification or modification/retesting of the worm cox5b RNAi protocol: As written, worms are injured and then placed into RNAi lawns to suppress de novo translation of cox5b to the effect of mitigating ROS production. However, this does not make sense, since ROS production should be able to be generated with existing mitochondrial protein complexes and not necessarily require new protein. This would suggest the vast literature of "tired old mitochondrial proteins are the cause of ROS" is collectively wrong. Resolve this apparent conflict. Possible methods may use cycloheximide to halt general translation or use of tet on/off systems controlling target protein expression.

To resolve the confusion regarding the timeline of RNAi administration and injury, we have included additional text in the revised methods section on worm trauma. All RNAi treatments are applied to the animal throughout their life. Thus, animals have been preconditioned prior to the injury. For cox5b RNAi, animals have reduced translation of the COX5b protein leading up to the injury. In this case, we proposed that reduced reliance on mitochondria for energetics mitigates ROS production resulting from an overwhelmed ETC.

3. Restaining and/or reimaging figure 1C (see below).

We appreciate the recommendation. We have taken the advice of the reviewer and labeled the different regions of the midbrain (1, 2 and 3), which are now described in more depth in the figure legend of the revised manuscript.

4. Panel 5B: Please run all samples on a single western blot and show results as a single boxed area. When western blots are presented such as this it is difficult to determine an effect of "injury".

All protein extracts were resolved on a single western blot and included in Figure 5—figure supplement 2 of the revised manuscript. Whether prior to (indicating a preconditioning) or at the onset of trauma-induced dopaminergic apoptosis (day 7 post injury), SURF1 mutant animals consistently show elevated PDHE1 phosphorylation, suggesting that they are favoring cytosolic glycolysis prior to the injury or coming out of the injury. This timing is consistent with previous reports, which examined the effects of open-head, controlled cortical impact (CCI)1.

Textual revisions: I strongly recommend reading reviewer 3's thorough review and verifying/modifying all of the typographical / nomenclature/ clarity changes requested.

We appreciate the thorough review and have carefully gone through all the recommended corrections as stated below.

1. Line 46: Unclear statement. Clarify.

We have adjusted the text in the revised manuscript to simplify the message.

2. Line 113-115: please temper statement.

We have reworded the respective sentence to temper the statement as a hypothesis (now line 122-125).

3. Line 139: modify for clarity.

After trying a few different versions of this concluding sentence (now line 155-157), we decided to keep the original text but are happy to entertain alternate text suggestions for this process being conserved.

4. Line 159 to 161: modify for clarity.

In the revised text, we reworded the respective sentence to clarify the epistasis analysis (now line 177-179).

5. Line 219 to 221: temper statement.

This text was adjusted to state “we provide evidence” rather than a definitive statement (now line 244-246).

6. Line 82: Change "degeneration" to "injury".

We changed the respective “neurodegeneration” to “neural damage”.

7. Line 175/190: Was a correlation statistically evaluated? If so, please report results. If not, change wording. Perhaps use "corresponded".

We have adjusted the text to say “corresponded”.

8. Line 203: Replace "activation" with something more precise such as "upregulation" or "increased transcription". The word activation implies signaling or physiological response.

We have adjusted the text to say “upregulation” versus “activation”.

9. Line 212: Change "spatial" to "cellular".

We have made the respective change in the revised manuscript.

10. Line 219 to 221: soften claim.

We have included the respective text “our data suggests that” to soften the claim and state that this is not a definitive statement but rather a suggestion.

Figure/Table Revisions:

Please provide timepoints for all relevant figures where not listed. Also please carefully label control and TBI mice in each panel where relevant, and provide n where not clear.

Table 1. Better describe and clarify if parameters are for mouse brain or rat brain. If they are for rat, then provide the correct parameters for mouse brain. If the title is in error, then correct the title.

In the original manuscript, parameters were defined for the rat brain. Unlike the rat brain, viscoelastic properties for some mouse brain regions (the hippocampus in particular) have not been defined or reported in the literature. To address this, we performed the respective analysis with updated mouse brain parameters with the exception of the hippocampus. In this case, we superimposed the viscoelastic parameters of the rat hippocampus into the mouse model. These new properties for the revised model have been updated in Table 1 and additional text included in the revised methods section. Regardless of the rat brain injury model or the mouse/rat hybrid model, the conclusion remains the same. TBI promotes a wave of mechanical strain, which propagates through the whole brain.

Figure 1. Panel C: Requires a reference map or annotations directly on the image to determine the precise anatomical location(s) pictured in the micrographs. If this is the midbrain Substantia nigra, please label SNp, SNr, VTA, etc. If this is not precisely in that area, additional tissue staining may be required to identify cell types.

In the revised manuscript, we have included labels (1, 2 and 3) in the Figure to highlight the respective regions of the midbrain.

TH (tyrosine hydroxylase) is a marker of catecholaminergic cells and not specifically a marker of dopaminergic cells. Staining with TH will therefore include dopaminergic and noradrenergic cells which vary in their distribution throughout the midbrain to pons. If needed use dopamine β-hydroxylase (DBH) in addition to TH to differentiate dopaminergic cells from noradrenergic cells.

It is worth noting that any cell with TH will synthesize dopamine. However, noradrenergic cells convert the dopamine to norepinephrine and this occurs in the locus coeruleus, which is not in the brain section that we examined. Although noradrenergic cells invert part of the midbrain, no NA cell bodies have been reported in this brain region2.

Panel E: It is unusual for uninjured mice to fall off the rotorod so quickly, especially when they were previously trained on the task two days prior. Provide data for all tested speeds if available.

We did not use a rotarod training paradigm. Rather we sought to perform a more naïve motor coordination analysis. In brief, mice were placed in the chamber 2 days prior to injury without turning on the machine. This time was used to familiarize the mice with their surroundings and enabled us to get more consistent readings after trauma. We have included additional text in the revised methods section to better clarify the assay and analysis.

Both Table 1 and Figure S2 indicate the finite element model includes or can include analysis of cerebellum and brainstem. Data on brainstem and cerebellum should be supplied as they are significant areas of innervation to and projections from the areas under study. Further, they are intimately involved in both righting reflex and locomotion and may lend to the interpretation of this and future studies.

This is a good point and we have now included analysis on the additional brain regions (cerebellum and brainstem) in the revised text.

Figure 3C: Similar to Figure 1, provide outlines of regional anatomy and quantitation of TH vs "other cells".

In the revised manuscript, we have included labels in the figure to mark all the respective regions of the midbrain (1, 2 and 3).

For 3C-F, baseline results should be reported.

We have included baseline readings for the respective non-injured animals in the corresponding supplemental figures.

Figure 4a: Include latency information.

We are uncertain what latency information is being requested for H2O2 production. Both control and cox5b RNAi treated animals are provided with and without injury. All animals are age matched.

Figure S1: Provide information and details/controls for Panel F (see reviewer 3 comment).

The respective transcriptomic data was from RNAseq analysis of the whole brain. In this case, abundance for each inflammatory-linked transcript is normalized to reads per kilobase million (RPKM) as we have done in previous publications3,4. For ease of viewing, we have made age-matched control transcript levels relative to 1 and adjusted injured timepoints (2 hours and 7 days) accordingly. We have included additional text in the methods section to explain this normalization by RPKMs. Since this is a new method of closed-head injury, which does not immediately compromise the blood-brain barrier, many of the transcriptional targets that might serve as positive controls are not applicable since we did not perform the craniotomy surgery. Moreover, age-matched control animals which did not receive the injury were still anesthetized with isoflurane and allowed to recover. All these details are described in the methods section of the revised manuscript.

Methods Revisions:

Details in the methods section need to be better described throughout, examples of which include:

– The C. elegans blunt force trauma model is not well described in the methods section. Additionally, since N. Egge et al., 2021 already performed an RNAi screen of dopaminergic neuron GFP retention following injury (but used a different quantification metric), it would be informative to know how strong the effect of cox5b RNAi is in comparison to the top hits in the previous screen.

In the Egge et al., 2021 Nature Communication manuscript, we utilized both GFP retention within the dopaminergic neurons (the quantification metric utilized in the present manuscript under review) as well as the trauma index. In this previous publication3, the trauma index was formulated for ease of comparisons across the various RNAi conditions (over 50 conditions). With regards to cox-5b RNAi, we observe a trauma index of 0.70 compared to what we previously reported for vhp-1 RNAi with an index of 0.75. Thus, cox-5b RNAi is comparable to vhp-1 RNAi with regards to providing robust neuroprotection for dopaminergic neurons in C. elegans after blunt force injury.

– In Figure S1C, how were the neurons counted? Is there a co-stain?

The revised method section provides more in-depth detail for how we collected and quantified confocal micrographs. In brief, TH positive cells were co-stained with DAPI and Nissl prior to counting and comparisons across groups.

– In Table 1, the material properties of rat brain regions are listed, is this a fair comparison to make while modeling the mouse brain?

As describe above, the viscoelastic properties for the mouse brain regions (aside from the hippocampus) were used in the revised brain injury model. Overall, these minor changes in viscoelasticity between rat and mouse provide similar results in the TBI model.

– Surf KO mice were described to have rotarod defects in C. Dell'agnello et al., 2007, so in figure 3E and F, it would be useful to know the baseline levels of performance prior to injury.

In the revised manuscript, we include baseline readings for naïve mice both wild type and Surf1 mutants. Although C. Dell’agnello et al., 2007 report rotarod defects in Surf1 mutants, we do not observe significant differences in latency between wild type animals and the mutants. It is worth noting that our laboratory utilizes a different rotarod paradigm, making it difficult to draw comparisons between the different experimental setups. These baseline readings are included in the revised manuscript in Figure 3—figure supplement 1.

– Unclear how the experiment in Figure S3E was performed, and the conclusions derived from this experiment are also confounding.

Confocal images were collected and quantified as performed in Figure 1—figure supplement 1C. Neuronal counts in the substantia nigra were compared across groups and statistically analyzed following the details in the methods section of the manuscript. The conclusions from this experiment show enhanced survival of dopaminergic neurons in the Surf1 mutants 28 days after closed head injury. This data corresponds with Surf1 mutant animals showing significantly less cleaved caspase 3 staining in the same brain region 7 days post-concussion.

– Single nuclei isolation, purification, and RNA sequencing: The reported dissection method will produce single nuclei representing subcortical / diencephalic structures, midbrain, pontine and medullary tissues. Interpretation of snRNAseq results cannot therefore be attributed to represent midbrain or striatal cell populations.

This is a good point and exactly why we leveraged single nuclei sequencing techniques. In collaboration with established experts on single cell analysis (laboratory of Dr. Konopka), we were able to distinguish dopamine producing cells from other cell types and analyze transcriptional differences within the defining sub-groups or “clusters” via genetic profiling (Figure 5—figure supplement 1N).

– Statistical Analysis: It is inadequate to state "Post-hoc analysis was determined by Prism recommendation". Please provide details on the types and specific uses of post-hoc analyses.

In the methods of the revised text, we have included more extensive details regarding our Post-hoc analysis. In brief, student’s t-test was used to compare means between two normal populations. Mann-Whitney U test was used to compare differences in the dependent variable between two groups. Post-hoc analysis performed after ANOVA included Dunnett’s multiple comparison (to compare means from several experimental groups against a control group mean) and Tukey’s multiple comparisons test (to compare all possible pairs of means). These details are provided in the revised figure legends.

– Motor Analysis: What make, model and manufacturer is the Rotorod being reported? On the testing day, how many times were the mice tested and does the reported "latency to fall" represent the average? If it does not represent the average, what does it represent?

In the methods section of the revised manuscript, we have included the make, model, and manufacturer of our laboratory’s rotarod. Moreover, we provide a detailed description of the procedure. In brief, motor deficits in mice were measured by Rotarod (Cat. No. 76-0770; Harvard Apparatus) with a rod diameter of 3 cm and a rod height of 20 cm. Animals were not trained on a rotating rod prior to testing, but rather exposed to the Rotarod machine chamber for 2 minutes (3 times daily). For testing, animals were placed on the rod with accelerating speed (0-24 rpm in 120 s) for all experiments. Latency to fall was recorded electronically in seconds by the apparatus and values were averaged per group for reporting. The clock was stopped if an animal held to the rod on two consecutive rotations and if the animal failed to fall after 120 s. Animals were returned to their cage after each trial.

– Confocal imaging: Was a single image plane acquired or were they z-stack images? How were the images controlled between subjects / slices? If they were z-stacks, what was the step size? How were the images displayed in the manuscript- were they single planes or z-stacks? Were all the image volumes identical?

In the methods section of the revised manuscript, we have included additional text to provide more details regarding the confocal microscopy. Briefly, brain parenchyma images were collected as Z-stacks with 1 µm steps as follows: midbrain micrographs 20 µm/10x objective/5 tile Z-stacks, cortex micrographs 10 µm/40x objective/5 tile Z-stacks, hippocampus micrographs 20 µm/10x objective/8 tile Z-stacks, thalamus micrographs 5 µm/40x objective/2 tile Z-stacks. Number of tiles to be collected was determined by completely covering the brain structure of interest (i.e. midbrain 1x5 tiles, hippocampus 2x4 tiles). Tiled images were stitched with the Leica LAS X software. Cell culture micrographs were collected from cells grown on glass coverslips with a total of 10 fields collected with a 40x objective for image processing and analysis. Brain parenchyma images are displayed as maximal intensity projections of Z-stacks. Cell culture images are displayed as single plane micrographs.

– Protein extraction and western blotting: Reported methods do not appear to include the use of a phosphatase inhibitor. Therefore western blots showing any changes in phosphorylation may be related more to target protein abundance and not differential phosphorylation. Westerns blots therefore ought to include probes against the non-phosphorylated "total" target protein then in order to help interpret apparent expression changes.

All cell extracts were created in the presence of the SDS detergent, which will linearize folded proteins and inactivate enzymes including phosphatases and kinases. Even alkaline phosphatases, which are known to be resistant to lower concentrations of SDS, can be inactivated at 1% SDS5, which is the concentration used in our lysis buffer. This point has been added to the revised methods section. Regardless, we have now included additional western blots of total PDH and confirm that its steady-state levels are not significantly altered in the different conditions.

– Media acidification assay: Product numbers and formulation of DMEM is critical for the interpretation of the reported method. At present the method description appears inadequate to provide the type of data desired or reported. Further, cell counts must be performed after the assay is performed since mitochondrial mutations are known to alter population growth kinetics. Therefore acidification results ought to be normalized against population numbers to control for this confound and allow an accurate interpretation of the results.

In the revised methods section of the manuscript, we provide additional details regarding this particular assay. We specify the use of DMEM/F-12 (Cat. No. 11320033; Gibco), which contains phenol red with a pH indicator ranging of 6.8 to 8.2 according to the Merck Index, 13th edition, 7329. Methods used in the paper were adapted from a 2018 Resource-Application note from BioTek titled, “Using phenol red to assess pH in Tissue culture media”. The link is provided below. Moreover, data was normalized to 1x105 cells for analysis.

(https://www.biotek.com/resources/application-notes/using-phenol-red-to-assess-ph-in-tissue-culture-media/).

– Large-particle flow cytometry: How were data calibrated and normalized?

Prior to every run, laser power and flow rates are calibrated by use of GP control particles (COPAS Biosorter, 310-5071-001) as recommended by the company. From experiment to experiment, we maintain a consistent PTM laser power for each respective fluorophore. Moreover, we run an empty vector (EV) and control (both injured and non-injured) which we use to normalize all our experimental values.

– Closed-head traumatic brain injury: What % isoflurane, what flow rate oxygen, were the mice connected to isoflurane when impacted or not, was the scalp intact (as assumed to be but not explicitly stated)?

In the methods section of the revised manuscript, we provide all these specific details. In brief, we use 3% USP grade isoflurane and 2.5 lpm USP grade oxygen. The procedure takes under 10 seconds to set up the mice and administer the injury, so it is not necessary for the mice to remain connected to isoflurane. The scalp remained intact on these mice.

– Provide additional experimental details and OCR profiles for Figure 4C OCR measurement.

Since we did not use a Seahorse efflux analyzer to determine OCR, no OCR profiles (which are typical outputs for the Seahorse) were obtained. Rather, we used a commercially available kit (Cayman Chemicals) to determine OCR measurement. This and catalog numbers for the reagents are now clearly stated in the revised methods section.

References

1. Watson, W. D. et al., Impaired cortical mitochondrial function following TBI precedes behavioral changes. Front Neuroenergetics 5, 12, doi:10.3389/fnene.2013.00012 (2013).

2. Jenkins, P. O., Mehta, M. A. and Sharp, D. J. Catecholamines and cognition after traumatic brain injury. Brain 139, 2345-2371, doi:10.1093/brain/aww128 (2016).

3. Egge, N. et al., Trauma-induced regulation of VHP-1 modulates the cellular response to mechanical stress. Nat Commun 12, 1484, doi:10.1038/s41467-021-21611-8 (2021).

4. Egge, N. et al., Age-Onset Phosphorylation of a Minor Actin Variant Promotes Intestinal Barrier Dysfunction. Dev Cell 51, 587-601 e587, doi:10.1016/j.devcel.2019.11.001 (2019).

5. Stinson, R. A. Size and stability to sodium dodecyl sulfate of alkaline phosphatases from their three established human genes. Biochim Biophys Acta 790, 268-274, doi:10.1016/0167-4838(84)90031-1 (1984).

6. Lee, S. J., Hwang, A. B. and Kenyon, C. Inhibition of respiration extends C. elegans life span via reactive oxygen species that increase HIF-1 activity. Curr Biol 20, 2131-2136, doi:10.1016/j.cub.2010.10.057 (2010).

7. Hekimi, S., Wang, Y. and Noe, A. Mitochondrial ROS and the Effectors of the Intrinsic Apoptotic Pathway in Aging Cells: The Discerning Killers! Front Genet 7, 161, doi:10.3389/fgene.2016.00161 (2016).

8. Crane, P. K. et al., Association of Traumatic Brain Injury With Late-Life Neurodegenerative Conditions and Neuropathologic Findings. JAMA Neurol 73, 1062-1069, doi:10.1001/jamaneurol.2016.1948 (2016).

Reviewer #1:

Fonseca et al., cleverly utilize C. elegans and mouse models of blunt force trauma to present a elcohesive picture of the signaling pathways underlying neurodegeneration following concussion.

The first major conclusion of the paper is that trauma induced neurodegeneration is neuron subtype specific, with dopaminergic neurons being the most susceptible after injury. This claim is convincingly validated in a C. elegans model of blunt force trauma that they previously developed (N. Egge et al., 2021). The authors use a weighted drop model of traumatic brain injury in mice to find a similar hypersensitivity to neurodegeneration in dopaminergic midbrain neurons, but it is unclear if this is specific as other relevant brain areas like the thalamus and cortex have not been assayed.

The authors also identify a key neuroprotective role for ETC complex IV inhibition following injury. Inhibition of ETC Complex IV results in an astrocyte mediated Warburg like shift in metabolism from oxidative phosphorylation to glycolysis, which is sufficient to counteract an increase in toxic ROS production in injured neurons. These observations are reached using an impressive array of experimental techniques, ranging from metabolomic profiling to single nuclei RNA sequencing, with the authors providing multiple lines of evidence in both C. elegans and mouse models in support of these novel findings.

Additional experiments:

– Caspase 3 staining following injury must be done for thalamus and cortex of injured mice to show that the neurodegeneration is midbrain dopaminergic neuron specific, rather than just the hippocampus, which is farther than the midbrain from the site of impact.

We have included new indirect immunofluorescence micrographs and quantifications for the respective brain regions (thalamus and cortex). This additional data complements our existing analysis of the midbrain and hippocampus. In brief, there was no significant change in immunostained cleaved caspase 3 signal intensity seven days post-concussion in both the thalamus or the cortex. These new data are included in the revised manuscript in Figure 1—figure supplement 1.

– Is the Warburg shift in C. elegans dependent on non-neuronal cells? If possible, could you perform cell specific RNAi for cox5b and pdp-1 in neurons vs glial sheath cells or the epidermis to test?

This is a good point. Transgenic C. elegans strains, which enabled tissue specific RNAi exclusively in the nervous system, were used to show that cox-5b and pdp-1 RNAi are both able to suppress trauma-induced loss of dopaminergic GFP. This new data is now included in the Figure 3—figure supplement 1 and Figure 5—figure supplement 2 of the revised manuscript. Attempts to construct a sheath glia-specific RNAi worm strain were not successful. This remains an important question for further studies.

Experimental methods:

– The C. elegans blunt force trauma model is not described in the methods section. Additionally, since N. Egge et al., 2021 already performed an RNAi screen of dopaminergic neuron GFP retention following injury (but used a different quantification metric), it would be informative to know how strong the effect of cox5b RNAi is in comparison to the top hits in the previous screen.

In the Egge et al., 2021 Nature Communication manuscript, we utilized both GFP retention within the dopaminergic neurons (the quantification metric utilized in the present manuscript under review) as well as the trauma index. In this previous publication3, the trauma index was formulated for ease of comparisons across the various RNAi conditions (over 50 conditions). With regards to cox-5b RNAi, we observe a trauma index of 0.70 compared to what we previously reported for vhp-1 RNAi with an index of 0.75. Thus cox-5b RNAi is comparable to vhp-1 RNAi with regards to providing robust neuroprotection for dopaminergic neurons in C. elegans after blunt force injury.

– Representative images of the GABAergic, cholinergic and serotonergic C. elegans neurons following injury would be helpful.

GABAergic images with and without injury were previously reported and analyzed in Egge et al., Nature Comm, 2021. Due to no observable changes in fluorescence in cholinergic and serotonergic neurons, we did not include the respective images. It is worth noting that serotonergic neurons are the focus of another paper under review elsewhere.

– In Figure S1C, how were the neurons counted? Is there a co-stain?

The revised methods section provides more in-depth detail for how we collected and quantified confocal micrographs. In brief, TH positive cells were co-stained with DAPI and Nissl prior to counting by eye and comparisons across groups.

– Image quality is poor in Figure S1E.

In the revised manuscript, we have completely reworked the figure and now include high magnification images of the micrograph (now Figure 1—figure supplement 1J).

– In Table 1, the material properties of rat brain regions are listed, is this a fair comparison to make while modeling the mouse brain?

In the original manuscript, parameters were defined for the rat brain. Unlike the rat brain, viscoelastic properties for some mouse brain regions (the hippocampus in particular) have not been defined or reported in the literature. To address this, we performed the respective analysis with updated mouse brain parameters with the exception of the hippocampus. In this case, we superimposed the viscoelastic parameters of the rat hippocampus into the mouse model. These new properties for the revised model have been updated in Table 1 and additional text included in the revised methods section. Regardless of the rat brain injury model or the mouse/rat hybrid model, the conclusion remains the same. TBI promotes a wave of mechanical strain, which propagates through the whole brain.

– Surf KO mice were described to have rotarod defects in C. Dell'agnello et al., 2007, so in figure 3E and F, it would be useful to know the baseline levels of performance prior to injury.

In the revised manuscript, we include baseline readings for naïve mice both wild type and Surf1 mutants. Although C. Dell’agnello et al., 2007 report rotarod defects in Surf1 mutants, we do not observe significant differences in latency between wild type animals and the mutants. It is worth noting that our laboratory utilizes a different rotarod paradigm, making it difficult to draw comparisons between the different experimental setups. These baseline readings are now included in the revised manuscript in Figure 3—figure supplement 1.

– Unclear how the experiment in Figure S3E was performed, and the conclusions derived from this experiment are also confounding.

Confocal images were collected and quantified as performed in Figure 1—figure supplement 1C. Neuronal counts in the substantia nigra were compared across groups and statistically analyzed following the details in the methods section of the manuscript. The conclusions from this experiment show enhanced survival of dopaminergic neurons in the Surf1 mutants 28 days after closed head injury. This data corresponds with Surf1 mutant animals showing significantly less cleaved caspase 3 staining in the same brain region 7 days post concussion.

– A major experimental choice that is not explained is the reason why two different components of complex IV are used in C. elegans and mouse.

This decision was based largely on the available of validated reagents as well as phenotypic consistencies between both worms and mice. Both cox-5b RNAi in worms and Surf1 mutant mice were shown to be beneficial and long-lived. Both are assembly factors for the same ETC complex IV and impairment of either protein, reduces complex IV activity without its full ablation.

– Unclear about what is being compared in Figure 4F.

In this graph, we analyze metabolite datasets from excised midbrain regions and ratio the product (Oxidized glutathione or GSSG) over the precursor (glutathione or GSH). From this analysis, we observe elevated ROS in the Surf1 mutant mice compared to age-matched wild type litter mates.

– Label uninjured vs injured in Figure 5 E and F.

No injury was administered in Figure 5E. In this case, we are characterizing how Surf1 mutant mice have altered metabolism prior to the injury, which our data suggests is a neuroprotective preconditioning process. Figure 5F represents all injured brains (7 days post injury). We have included additional text within the figure legend to clarify.

Writing:

– In the introduction section, a paragraph summarizing your findings would be helpful in orienting the reader to the relevance of the work in relation to previous studies.

We provided the journal with a summary of our results written in lay terms. This will be submitted to eLife Digest.

– There are a lot of references to Parkinsons disease mediated dopaminergic neurodegeneration, but the link to the current findings is somewhat tenuous, especially as the behavioral deficits observed in the mouse injury model are only a small subset of the motor and cognitive deficits seen in Parkinsons models.

This point is well taken and we agree that additional behavioral analysis needs to be performed (preferably in the context of addiction and reward). It is noteworthy that we do recapitulate the most characteristic phenotype of the disease from a pathology standpoint (being loss of dopamine neurons). Several genetic models of Parkinson’s disease in mice do exhibit some of the behavioral phenotypes but lack the hallmark loss of dopaminergic neurons in the substantia nigra.

Reviewer #2:

This study identified a neuroprotective mechanism using experimental models of blunt force trauma in C. elegans and concussion in mice. Interestingly, reducing Mitochondrial CIV function elevates mitochondrial-derived reactive resulted in neuroprotective effect via ROS and HIF1 mediated metabolic transition. Mechanism studies on ROS and metabolic regulation are interesting. The worm and mouse models are well established. The mouse model to reduce rather than ablate cytochrome C oxidase activity is a good choice.

1. Line 124, the authors may add a few more words to describe how cox-5b is identified, as a small-scale screening has been performed.

We have included text to examine previously reported beneficial mitochondrial mutants.

2. Figure 3C-F, baseline results should be provided.

We have included baseline readings for the respective non-injured animals in the corresponding supplemental figures.

3. The authors may need to determine whether nuo-2, sdhd-1, cyc-1, atp-3 RNAi treatments reduce ROS. Not sure if ROS suppression is cox-5b RNAi specific.

Several other groups have already shown that RNAi for almost all of these ETC components increase basal ROS levels6,7. In the rodent, we observed a higher resting or basal ROS levels in Surf1 mutants but reduced ROS induction after trauma in the Surf1 mutant. We hypothesize that mild increases in ROS prior to injury can initiate a neuroprotective metabolic shift to glycolysis. Upon the metabolic demand of injury, cells rely less on mitochondrial respiration and do not overwhelm the ETC. Thus, we would predict that mitochondrial perturbations would increase basal levels of ROS and as long as they do not reach toxic levels, they can promote a protective metabolic switch. In this manner, it is a hermetic response. It remains to be determined whether these various ETC RNAi constructs can mitigate trauma-induced ROS fluctuations.

4. If there is a selective vulnerability of different neural subtypes, results using MEFs in figure 4 may not explain the "selective vulnerability" phenotype. Are dopaminergic neurons hypersensitive to ROS after brain injury? The mechanisms need to be better explained.

This is true regarding the MEFs, which we utilized to examine mitochondrial morphology resulting from mutating SURF1. However, we do not use the MEF cells at any point in the manuscript to assess “selective vulnerability” since no viability assays were performed on these cultured cells. The MEFs were more amenable for super-resolution analysis and provided a much clearer demonstration of how loss of SURF1 can impact mitochondrial morphology. We do include a Figure 4B showing that expression of the ROS-producing KillerRed probe exclusively in the nervous system is sufficient to kill dopaminergic neurons.

5. In figure 5, if HIF1 is the "key" regulator, does HIF1 RNAi prevent glycolytic preconditioning and neuroprotection? Similarly, does hypoxia exhibit neuroprotective effect?

We have tempered the text and replaced “key” with “important”. Due to its pleotropic nature, we were hesitant to perform the respective experiments involving the knockdown of hif-1 expression. However, we are very interested in performing the recommended hypoxia experiments but lack the appropriate environmental chambers to perform such experiments.

Reviewer #3:

Traumatic brain injury (TBI) is a significant source of global behavioral, neurological and neurodegenerative morbidity. Though risk factors for poor outcomes following TBI, such as those thought to precipitate Alzheimer's disease are intensely studies, conserved mechanisms of injury and resiliency factors with the potential to prevent poor outcomes remain largely unknown. In this study by Fonseca and colleagues, the study team leverages a combination of model systems (e.g. C. elegans, mice, cell systems) to identify the selective vulnerability of dopaminergic neurons following physical trauma. Through a series of well-reasoned and articulate experiments, the study team presents evidence that suppression of function at the mitochondrial electron transport chain complex IV may reduce the loss of dopaminergic neurons following TBI, in part by driving astrocyte-mediated lactate shuttling to reduce oxidative phosphorylation in vulnerable neurons. This is potentially a very significant finding with wide ranging implications not only for acute injury care but potentially chronic neurodegenerative disease as well. A clear strength of the study is its approach to identifying evolutionarily-conserved mechanisms of selective dopaminergic vulnerability to TBI, which will likely be critical to identifying actionable targets in human TBI. The study is well written with an interesting and thoughtful narrative. The weakness of the paper is primarily the under-reporting of methodological details essential to reproducing the work, inadequate controls to verify results, and a paucity of data supporting major claims and conclusions.

Recommendations for the authors:

Line 46: Unclear statement. Clarify.

Clarified.

Line 82: Change "degeneration" to "injury".

Text adjusted accordingly.

Line 97 to 100: TBI-induced deficits in voluntary movement are well established and may have multiple causes unrelated to SN / DA injury. Interestingly, changes in "disinhibition and risk seeking", another classical Parkinsonian trait and more cleanly associated with dopaminergic dysfunction, has been reported in TBI in human subjects (self-report) and mouse TBI models (using novel object recognition tests). Provide results of novel object recognition tests to demonstrate unencumbered behavioral / functional impairment related to dopaminergic injury / degeneration in this mouse model. Evaluate the correlation between behavioral results with the degree of dopaminergic loss across SNr, SNc, VTA, etc.

We had previously performed the respective novel object recognition studies and observed no difference 7 days after concussion between the control and injured groups. We did not report this negative data since it did not contribute to the overall story.

Line 113-115: Caution and conservativeness is warranted in this closing statement. This study has not adequately compared and contrasted the profiles of neuronal injury across the SNc/Midbrain with those of the thalamus and therefore its inappropriate to suggest that the inherent physiological properties of dopaminergic neurons sensitizes them to biomechanical insults [as compared with the thalamus]. Indeed several studies have noted substantial thalamic alterations associated with repeated TBI, whereas few have identified dopaminergic pathology.

While a majority of the TBI field has historically relied on open-head animal models, we anticipate many differences as we transition to a more physiologically relevant model of TBI, and more so concussion. To this point, allow me to highlight a large and well-controlled clinical study, which looks at over 7000 participants and accounts for over 45,000 years of clinical follow up8. In this study, they draw a correlation between concussion with a loss of consciousness and the progression of Parkinson’s disease, Parkinsonisms, and Lewy body dementia.

Line 139: As written, your statement makes it sound as though though evolution has selected for the protection of DA neurons against TBI by reduction of cytochrome c oxidase activity, which I do not think you intend to convey. Perhaps, "suppressing trauma-induced degeneration of dopaminergic neurons through reduction in cytochrome C oxidase activity is sufficient to conserve dopaminergic function in both C. elegans to mice."?

Our intension was to summarize the experimental results, which show that dopamine neurons in mice and worms are highly sensitive to trauma-induced death (when compared to other brain regions and neuronal subtypes). Thus, this process or mechanism of selective vulnerability appears to be conserved between these species.

Major concern Line 150 to 151: If I am understanding correctly… As written, worms are injured and then placed into RNAi lawns to suppress de novo translation of cox5b to the effect of mitigating ROS production. However, this does not make sense, since ROS production should be able to be generated with existing mitochondrial protein complexes and not necessarily require new protein. This would suggest the vast literature of "tired old mitochondrial proteins are the cause of ROS" is collectively wrong. Resolve this apparent conflict. Possible methods may use cycloheximide to halt general translation or use of tet on/off systems controlling target protein expression.

To resolve the confusion regarding the timeline of RNAi administration and injury, we have included additional text in the revised methods section on worm trauma. All RNAi treatments are applied to the animal throughout their life. Thus, animals have been preconditioned prior to the injury. For cox5b RNAi, animals have reduced translation of the COX5b protein leading up to the injury. In this case, we proposed that reduced reliance on mitochondria for energetics mitigates ROS production resulting from an overwhelmed ETC.

Line 159 to 161: Unclear / confusing. For clarity, please reword or break into separate sentences.

Adjusted text accordingly.

Line 175: Was a correlation statistically evaluated? If so, please report results. If not, change wording. Perhaps use "corresponded".

Changed text.

Line 190: "correlated". Same comment as Line 175.

Text adjusted.

Line 203: typo "Pdk2". Change to "pdk2" (standard gene symbols are all lower case by convention).

Please see our comments on standard nomenclature for each organism above.

Replace "activation" with something more precise such as "upregulation" or "increased transcription". The word activation implies signaling or physiological response.

Text was adjusted to say upregulated.

Line 204: typo "Pdp1". Change to "pdp1" (standard gene symbols are all lower case by convention).

Please see our comments on standard nomenclature for each organism above.

Line 212: Change "spatial" to "cellular".

Adjusted text.

snRNAseq provides cellular data and though cellular nuclei are physically separate from one another, snRNAseq does not preserve accepted information on spatial relationships such as that provided by microscopy.

Regarding single nuclei sequencing techniques, we do lose spatial elements upon tissue dissociation but we can pinpoint the nature of each cell during sequencing analysis by identifying select transcripts, which are specific for the respective cell type. For example, we can identify dopamine specific transcripts such as the dopamine transport, SLC6A3 or DAT, in combination with the VMAT2 (SLC18A2) and the dopa-decarboxylase (DDC) to confidently designate these cells as dopaminergic. This new technology has become widely used and accepted within the research community. Therefore, we formed a collaboration with established experts on single cell analysis (laboratory of Dr. Konopka) to perform this respective analysis appropriately.

Line 219 to 221: "Through spatial resolution…" Identifying a pro-Warburg shift in astrocytes driven by Surf1 KO, does not demonstrate that this is a pathway for naturally occuring Warburg phenomenon. Suggest, softening by changing text to read "…may be initiated by electron…". Further, though the presented data indicates the Warburg shift is princpally manifest in astrocytes, it does not rule out the initiation of this process by extrinsic factors such as exosomes.

Unclear on this comment and the term “naturally occurring Warburg”. We observe that the transcriptional changes occur in astrocytes versus the dopamine neurons. Yet how these transcriptional changes in the astrocytes communicate to the dopamine neurons remains to be determined. Potentially, it occurs through exosomes.

Line 229: Hif1alpha… gene or protein?

Line 236: Hif1alpha… gene or protein?

Line 239: Hif1alpha… gene or protein?

Depending on the instance, if the word follows the gene nomenclature guidelines found below, then we intend to reference the gene. If instead, all the letters are uppercase and not italicized, then we intend to reference the protein (in accordance with the guidelines mentioned below).

- Mouse: Only the first letter uppercase, italicized (in accordance with MGI guidelines).

- Human: All letters uppercase, italicized (in accordance with HCNC guidelines).

- C. elegans: All letters lowercase, italicized (in accordance with WormBase guidelines).

Line 245: change "pdp-1" to "pdp1".

Worm nomenclature requires the "-".

Line 249: change to "… trauma-induced neurodegeneration of dopaminergic neurons…"

Table 1.

Are these parameters for mouse brain or rat brain? If they are for rat, then provide the correct parameters for mouse brain. If the title is in error, then correct the title.

Provide a descriptor as to what each parameter is and what higher or lower numbers indicate for the lay reader.

In the original manuscript, parameters were defined for the rat brain. Unlike the rat brain, viscoelastic properties for some mouse brain regions (the hippocampus in particular) have not been defined or reported in the literature. To address this, we performed the respective analysis with updated mouse brain parameters with the exception of the hippocampus. In this case, we superimposed the viscoelastic parameters of the rat hippocampus into the mouse model. These new properties for the revised model have been updated in Table 1 and additional text included in the revised methods section. Regardless of the rat brain injury model or the mouse/rat hybrid model, the conclusion remains the same. TBI promotes a wave of mechanical strain, which propagates through the whole brain.

Figure 1.

Panel C: Requires a reference map or annotations directly on the image to determine the precise anatomical location(s) pictured in the micrographs in order to inform the reader as to the principle cell populations involved. If this is the midbrain Substantia nigra, please label SNp, SNr, VTA, etc. If this is not precisely in that area, additional tissue staining may be required to identify cell types.

In the revised manuscript, we have included labels (1, 2 and 3) in the Figure to highlight the respective regions of the midbrain. All are included in the respective micrograph.

TH (tyrosine hydroxylase) is a marker of catecholaminergic cells and not specifically a marker of dopaminergic cells. Staining with TH will therefore include dopaminergic and noradrenergic cells which vary in their distribution throughout the midbrain to pons. If needed use dopamine β-hydroxylase (DBH) in addition to TH to differentiate dopaminergic cells from noradrenergic cells.

It is worth noting that any cell with TH will synthesize dopamine. However, noradrenergic cells convert the dopamine to norepinephrine and this occurs in the locus coeruleus, which is not in the brain section that we examined. Although noradrenergic cells invert part of the midbrain, no NA cell bodies have been reported in this brain region 2.

Use of cleaved caspase-3 staining is at insufficient resolution to determine whether TH cells (or an unrelated phenotype) are positive for cleaved caspase-3. Quantitation of colocalized cleaved caspase-3 across TH+, astrocytes, vs other cell types should be supplied.

We have provided a counter-stain in the revised manuscript with Nissl to confirm that we are assessing neurons rather than support cells.

Use of additional markers are required to verify the apoptotic cells are dopaminergic neurons and not simply other cell types such as infiltrated peripheral immune cell subsets (which are known to express tyrosine hydroxylase and apoptosize during the early phases of brain injury resolution).

We examine 28 days after injury to confirm a significant loss of TH positive cells. These numbers correspond nicely with levels of cleaved caspase observed in the same brain regions. Moreover, we do not anticipate that infiltrated peripheral immune cells would be significant contributors to overall TH-positive signal since no inflammation is observed at 7 days post injury as evidenced by RNA-seq, indirect immunofluorescence, and snRNA-seq.

Panel C: At what time point?

7 days post injury. Now included in the figure legend.

Panel D: At what time point? How many slices per animal were examined?

In the methods section of the revised manuscript, we have included additional text to provide more details regarding the confocal microscopy. Briefly, brain parenchyma images were collected as Z-stacks with 1 µm steps as follows: midbrain micrographs 20 µm/10x objective/5 tile Z-stacks, cortex micrographs 10 µm/40x objective/5 tile Z-stacks, hippocampus micrographs 20 µm/10x objective/8 tile Z-stacks, thalamus micrographs 5 µm/40x objective/2 tile Z-stacks. Number of tiles to be collected was determined by completely covering the brain structure of interest (i.e. midbrain 1x5 tiles, hippocampus 2x4 tiles). Tiled images were stitched with the Leica LAS X software. Cell culture micrographs were collected from cells grown on glass coverslips with a total of 10 fields collected with a 40x objective for image processing and analysis. Brain parenchyma images are displayed as maximal intensity projections of Z-stacks. Cell culture images are displayed as single plane micrographs.

Panel E: It is unusual for uninjured mice to fall off the rotorod so quickly, especially when they were previously trained on the task two days prior. Were the animals placed on the rod "backwards"? Provide data for all tested speeds if available.

We did not use a rotarod training paradigm. Rather we sought to perform a more naïve motor coordination analysis. In brief, mice were placed in the chamber 2 days prior to injury without turning on the machine. This time was used to familiarize the mice with their surroundings and enabled us to get more consistent readings after trauma. We have included additional text in the revised methods section to better clarify the assay and analysis.

Figure 2.

Between A and B: There is an illustrated "block". I am assuming this is either an illustrated "typo" or the impactor head. Please define or remove this "block".

The block observed in Figure 2 between A and B is the depiction of the impactor seen in A, but since the frame in B is at a later timepoint, the impactor is now ascending and therefore appears further away from the brain.

C. Both Table 1 and Figure S2 indicate the finite element model includes or can include analysis of cerebellum and brainstem. Data on brainstem and cerebellum should be supplied as they are significant areas of innervation to and projections from the areas under study. Further, they are intimately involved in both righting reflex and locomotion and may lend to the interpretation of this and future studies.

In the revised manuscript, we have included analysis with the new brain regions including the brainstem and cerebellum.

Figure 3.

Panel A and B: At what time point? Also, provide either quantitative real-time PCR or Western blot quanitation of relative RNAi efficiency.

All worm injury is assessed 24 hours post injury while mouse injury is assessed 7 days post-concussion unless otherwise stated. We have included the timing post-injury in the figure legend for clarity. Regarding efficiency of RNAi, we observe phenotypes upon cox-5b RNAi consistent with prior reports, including smaller body size and extended lifespan.

Panel C: Similar to Figure 1, provide outlines of regional anatomy and quantitation of TH vs "other cells" costaining along with "zoomed in" insets to verify colocalization of cleaved caspase-3 with dopaminergic neurons.

We have included the respective labeling system to highlight the different midbrain regions of interest. We have provided zoomed inserts of the brain micrographs in the supplemental figures (1 and 3).

Panel C: At what time point are the brain slices representing?

7 days post injury. Included in the revised figure legend.

Panel D: Are these TBI or control mice?

These are concussed mice. Very little cleaved caspase is observed in the midbrain of 12 week old mice. Please reference Figure 1 to compare control with concussion.

Panel F: Are these TBI or Control mice? If TBI, how long after TBI are the mice given this test?

Injured mice. Included in the figure legend.

Figure 4.

Title: Change "Reducing cytochrome C…" to "Decreasing cytochrome C oxidase expression…". This helps avoid confusion between electrochemical "reductions" involving cytochrome C oxidase and changes in cytochrome C protein expression levels, such as the use of RNAi intends to cause.

Panel A: Was there no latency between when the trauma occured and when they began peroxide measurements? If so, include that in the timescale.

Due to the nature of transferring the worms, measurements did not begin until 3 minutes post-injury. The respective time course beyond this lag is reported on the x-axis of the graph.

Panel C: Data lines are unlabeled as to the reagent used to test OCR. Graph should consist of control, actinomycin, FCCP and Oligomycin treatment conditions for each genotype. It is impossible to conclude that this even has living cells without providing more data.

Recommend using a dynamic Seahorse ECAR / OCR assay. Please provide the full Seahorse Glycolytic ECAR and Mitochondrial respiratory OCR profiles.

Since we did not use a Seahorse efflux analyzer to determine OCR, no OCR profiles (which are typical outputs for the Seahorse) were obtained. Rather, we used a fluorescence commercially available kit (Cayman Chemicals) to determine OCR measurement. This and catalog numbers for the reagents are now clearly stated in the revised methods section.

Panel E: n=3. Is this technical or biological replicates? How was this measured (mention in legend)?

Biological replicates were measured with a Clariostar plate reading fluorimeter.

Figure 5.

Panel B: Please run all samples on a single western blot and show results as a single boxed area. When western blots are presented such as this it is difficult to determine an effect of "injury". As it is, the reader cannot conclude that there's any difference between conditions other than a main effect of genotype.

Per the recommendation of the reviewer, we have included the whole western blot as a single boxed area in the Figure supplement.

Figure Legend Text for Panel C: remove the word "neuronal" or replace with "neural".

Panel C (figure): Gene symbols are not capitalized by convention.

We have implemented the following standard gene nomenclatures:

- Mouse: Only the first letter uppercase, italicized (in accordance with MGI guidelines).

- Human: All letters uppercase, italicized (in accordance with HCNC guidelines).

- C. elegans: All letters lowercase, italicized (in accordance with WormBase guidelines).

Panel F: Requires single cell quantitation across DA neurons, astrocytes, others to determine treatment effects differentially affect apoptosis in a partcular target cell type.

Colocalization of cleaved caspase 3 signal corresponds nicely with TH-positive cells even at higher resolutions. GFAP positive cells were sparse within the midbrain.

Panel G and H: Change pdp-1 to pdp1.

This is not accurate nomenclature for C. elegans.

Figure S1.

Panel C: How many slices per mouse were examined?

Included in the revised methods section.

Panel D and E: What study timepoint do these micrographs represent? How many slices per mouse were examined?

7 days post-trauma and details provided in the confocal imaging methods section of the revised manuscript.

Panel F: It is unusual to detect no change across a number of microglia enriched genes in areas that are experiencing neuronal apoptosis, especially in CD68 which would be under greater use in the elimination of cellular debris. How was this data normalized?

At 7 days post-trauma, we have not generated cranial fracture and dopaminergic neurons have not yet ruptured. Thus, it is feasible that CD68 and microglial activation has not yet occurred in this brain region. Importantly, our indirect immunofluorescence and RNA-seq data help validate our observations.

Was it library normalized per cell? If not, consider that analytical approach.

In this instance, we are performing RNA-seq on the entire brain. Normalization was based on RNA concentration.

What type of RNAseq and where was the material collected from? (I am assuming snRNAseq from the described subdissection)

In this panel we are performing RNAseq from whole brain. This information is provided in the methods section which describes the RNA isolation and library preparation as well as the model of sequencers used and the analysis methods.

Provide positive and negative controls to prove the assay worked. Ensure the microglia being reported on are from the area under study and not from a broadly mixed tissue source, which may have originally come from "uninjured" locations by sheer random chance and may not represent the SN. Microscopy or morphometric analyses may be required to ensure proximity.

Microglia were analyzed proximal to the midbrain in the revised manuscript. Based on highly sensitive RNA-seq and snRNA-seq, we have no indication that microglial activation is occurring 7 days post-concussion in our closed-head injury paradigm.

Figure S2.

No comments.

Figure S3.

Panel A: Are these Injury or control mice?

These are non-injured mice to characterize levels of reduced cytochrome c oxidase. This is included in the revised figure legend.

Panel B: Mortality should be represented as a curve across increasing impact force. For a first demonstration of mortality curves, ideally data from at least three different cohorts, with each cohort injured separately from one another on different days, should be supplied to allow the reader to determine the degree of reproducibility for both the execution of the method and the experimental measure being made.

Good points. However, our active animal protocols for concussion limits our ability to perform the proposed experiment.

Panels D and E: Panel D shows an apparent loss of DA neurons in injured mice with a compensatory upregulation of TH by remaining neurons 28 days following injury. Panel E quantifies the DA neuron loss for both Surf wt and KO mouse strains. What is the average immunofluroescent expression of TH by remaining DA neurons after injury and does it differ by strain?

In the revised manuscript, we provide a zoomed in panel to complement the existing micrograph. However, we do not quantify TH fluorescence in individual neurons. If this did occur, we hypothesize that it may represent a compensatory mechanism to account for reduced dopamine production from this brain region.

Figure S4.

No comments

Figure S5.

Panel A: Are these control mice?

Yes.

Panel B to E: At what timepoint after injury does this data represent?

Metabolic cage analysis was performed for a 7-day period post-injury. Shown are averages of the respective period. This data is described in the methods section of the revised manuscript.

Panel F and G: these panels appear to be misidentified in the figure legend. Please check.

We appreciate you picking up on this. In the revised manuscript, we confirmed that figure legends match the respective figure.

Panel M: It does not seem reasonable to acheive a snRNAseq of midbrain and striatal brain tissue without obtaining microglia and endothelial cells. Please reconcile this and report these cell types.

This is a good point and exactly why we leveraged single nuclei sequencing techniques. In collaboration with established experts on single cell analysis (laboratory of Dr. Konopka), we were able to distinguish dopamine producing cells from other cell types and analyze transcriptional differences within the defining sub-groups or “clusters” via genetic profiling (Figure 5—figure supplement 1N). Since we did not observe inflammatory changes 7 days post-concussion, we excluded microglia from our analysis. Examination of brain vasculature and endothelial cells is interesting but outside the scope of the present study.

Methods:

Single nuclei isolation, purification, and RNA sequencing: The reported dissection method will produce single nuclei representing subcortical / diencephalic structures, midbrain, pontine and medullary tissues. Interpretation of snRNAseq results cannot therefore be attributed to represent midbrain or striatal cell populations.

To your point, this is exactly why we used single nuclei sequencing techniques. As mentioned above, this new technique allows us to analyze transcriptional changes in select cell types from complex tissue. This is a major reason why this new technology has rapidly gained so much traction in the research community.

Closed-head traumatic brain injury: What % isoflurane, what flow rate oxygen, were the mice connected to isoflurane when impacted or not, was the scalp intact (as assumed to be but not explicitly stated)?

In the methods section of the revised manuscript, we provide all these specific details. In brief, we used 3% USP grade isoflurane and 2.5 lpm USP grade oxygen. The procedure takes under 10 seconds to set up the mice and administer the injury, so it is not necessary for the mice to remain connected to isoflurane. The scalp remained intact on these mice.

Media acidification assay: Product numbers and formulation of DMEM is critical for the interpretation of the reported method. At present the method description appears inadequate to provide the type of data desired or reported. Further, cell counts must be performed after the assay is performed since mitochondrial mutations are known to alter population growth kinetics. Therefore acidification results ought to be normalized against population numbers to control for this confound and allow an accurate interpretation of the results. Confirmation of acidification assay should also be performed using a secondary method such as pH meter.

In the revised methods section of the manuscript, we provide additional details regarding this particular assay. All data was normalized to 1x105 cells for analysis. We specify the use of DMEM/F-12 (Cat. No. 11320033; Gibco), which contains phenol red with a pH indicator ranging of 6.8 to 8.2 according to the Merck Index, 13th edition, 7329. Methods used in the paper were adapted from a 2018 Resource-Application note from BioTek titled, “Using phenol red to assess pH in Tissue culture media”.

Statistical Analysis: It is inadequate to state "Post-hoc analysis was determined by Prism recommendation". Please provide details on the types and specific uses of post-hoc analyses.

In the methods of the revised text, we have included more extensive details regarding our Post-hoc analysis. In brief, student’s t-test was used to compare means between two normal populations. Mann-Whitney U test was used to compare differences in the dependent variable between two groups. Post-hoc analysis performed after ANOVA included Dunnett’s multiple comparison (to compare means from several experimental groups against a control group mean) and Tukey’s multiple comparisons test (to compare all possible pairs of means). These details are provided in the revised figure legends.

RNAi administration: RNAi sequences and suppliers and concentrations must be provided.

Standardized C. elegans RNAi libraries (both Vidal and Ahringer) are commercially available with the respective sequence information. Please see the methods section on how RNAi cultures were prepared for experimentation.

Large-particle flow cytometry: How were data calibrated and normalized?

Prior to every run, laser power and flow rates were calibrated by use of GP control particles (COPAS Biosorter, 310-5071-001) as recommended by the company. From experiment to experiment, we maintained a consistent PTM laser power for each respective fluorophore. Moreover, we ran an empty vector control (both injured and non-injured) which we used to normalize all our experimental values.

Motor Analysis: What make, model and manufacturer is the Rotorod being reported? On the testing day, how many times were the mice tested and does the reported "latency to fall" represent the average? If it does not represent the average, what does it represent?

In the methods section of the revised manuscript, we have included the make, model, and manufacturer of our laboratory’s rotarod. Moreover, we provide a detailed description of the procedure. In brief, motor deficits in mice were measured by Rotarod (Cat. No. 76-0770; Harvard Apparatus) with a rod diameter of 3 cm and a rod height of 20 cm. Animals were not trained on a rotating rod prior to testing, but rather exposed to the Rotarod machine chamber for 2 minutes (3 times daily). For testing, animals were placed on the rod with accelerating speed (0-24 rpm in 120 s) for all experiments. Latency to fall was recorded electronically in seconds by the apparatus and values were averaged per group for reporting. The clock was stopped if an animal held to the rod on two consecutive rotations and if the animal failed to fall after 120 s. Animals were returned to their cage after each trial.

Confocal imaging: Was a single image plane acquired or were they z-stack images? How were the images controled between subjects / slices? If they were z-stacks, what was the step size? How were the images displayed in the manuscript- were they single planes or z-stacks? Were all the image volumes identical?

In the methods section of the revised manuscript, we have included additional text to provide more details regarding the confocal microscopy. Briefly, brain parenchyma images were collected as Z-stacks with 1 µm steps as follows: midbrain micrographs 20 µm/10x objective/5 tile Z-stacks, cortex micrographs 10 µm/40x objective/5 tile Z-stacks, hippocampus micrographs 20 µm/10x objective/8 tile Z-stacks, thalamus micrographs 5 µm/40x objective/2 tile Z-stacks. Number of tiles to be collected was determined by completely covering the brain structure of interest (i.e. midbrain 1x5 tiles, hippocampus 2x4 tiles). Tiled images were stitched with the Leica LAS X software. Cell culture micrographs were collected from cells grown on glass coverslips with a total of 10 fields collected with a 40x objective for image processing and analysis. Brain parenchyma images are displayed as maximal intensity projections of Z-stacks. Cell culture images are displayed as single plane micrographs.

Protein extraction and western blotting: Reported methods do not appear to include the use of a phosphatase inhibitor. Therefore western blots showing any changes in phosphorylation may be related more to target protein abundance and not differential phosphorylation. Westerns blots therefore ought to include probes against the non-phosphorylated "total" target protein then in order to help interpret apparent expression changes.

All cell extracts were created in the presence of the SDS detergent, which will linearize folded proteins and inactivate enzymes including phosphatases and kinases. Even alkaline phosphatases, which are known to be resistant to lower concentrations of SDS treatments, can be inactivated with 1% SDS5, which is the concentration used in our lysis buffer. This point has been added to the revised methods section. Regardless, we have now included additional western blots of total PDHE1 and confirm that its steady-state levels are not significantly altered in the different conditions.

https://doi.org/10.7554/eLife.69438.sa2

Article and author information

Author details

  1. Rene Solano Fonseca

    Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5927-5936
  2. Patrick Metang

    Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  3. Nathan Egge

    Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  4. Yingjian Liu

    Department of Mechanical Engineering, University of Texas at Dallas, Dallas, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  5. Kielen R Zuurbier

    1. Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    2. O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Formal analysis, investigation.
  6. Karthigayini Sivaprakasam

    1. O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States
    2. Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
  7. Shawn Shirazi

    Department of Integrative Biology, University of California, Berkeley, Berkeley, United States
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
  8. Ashleigh Chuah

    Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  9. Sonja LB Arneaud

    Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Investigation, Writing - original draft, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1123-3876
  10. Genevieve Konopka

    1. O’Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, United States
    2. Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Formal analysis, Methodology
    Competing interests
    Reviewing Editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3363-7302
  11. Dong Qian

    Department of Mechanical Engineering, University of Texas at Dallas, Dallas, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  12. Peter M Douglas

    1. Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, United States
    2. Hamon Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing
    For correspondence
    peter.douglas@utsouthwestern.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0734-1049

Funding

Welch Foundation (I-2061-20210327)

  • Peter M Douglas

National Institutes of Health (R01AG061338)

  • Peter M Douglas

Cancer Prevention and Research Institute of Texas (RR150089)

  • Peter M Douglas

Clayton Foundation for Research

  • Peter M Douglas

National Institutes of Health (R00AG042495)

  • Peter M Douglas

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

Acknowledgements

We thank Massimo Zeviani, PhD at the University of Padua for providing Surf1 mutant mice. We thank Lauren Zacharias, MS and Ralph DeBerardinis, MD, PhD of the Children’s Medical Center Metabolomics Facility at UT Southwestern Medical Center (UTSW) for targeted metabolomic profiling and advice on data analysis. We thank Syann Lee, PhD at the Metabolic Phenotyping Core at UTSW for overseeing mouse metabolic cage experiments. We thank Matt Seiber, PhD, William Dauer, MD, Munro Cullum, PhD at UTSW, and Michael Douglas, PhD for critical feedback. Funding: Our funding sources are from the Clayton Foundation for Research, Welch foundation (I-2061–20210327 to PMD), the American Federation of Aging Research, the Glenn Center for Aging, the NIH (R00AG042495 and R01AG061338 to PMD), and the Cancer Prevention Research Institute of Texas (CPRIT) (RR150089 to PMD).

Ethics

Animal experimentation: All mouse studies were approved by the UT Southwestern Medical Center Institutional Animal Care and Use Committee (IACUC) protocols (#2016-101750) and performed in accordance with institutional and federal guidelines.

Senior Editor

  1. Matt Kaeberlein, University of Washington, United States

Reviewing Editor

  1. Scott F Leiser, University of Michigan, United States

Publication history

  1. Received: April 15, 2021
  2. Accepted: August 24, 2021
  3. Accepted Manuscript published: September 2, 2021 (version 1)
  4. Version of Record published: September 17, 2021 (version 2)

Copyright

© 2021, Solano Fonseca et al.

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

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