Enhanced oxidative phosphorylation, re-organized intracellular signaling, and epigenetic de-silencing as revealed by oligodendrocyte translatome analysis after contusive spinal cord injury

Forston, Michael D.; Wei, George Z.; Chariker, Julia H.; Stephenson, Tyler; Andres, Kariena; Glover, Charles; Rouchka, Eric C.; Whittemore, Scott R.; Hetman, Michal

doi:10.1038/s41598-023-48425-6

Download PDF

Article
Open access
Published: 01 December 2023

Enhanced oxidative phosphorylation, re-organized intracellular signaling, and epigenetic de-silencing as revealed by oligodendrocyte translatome analysis after contusive spinal cord injury

Michael D. Forston^1,3^na1,
George Z. Wei^1,4,5^na1,
Julia H. Chariker^6,7,
Tyler Stephenson^1,2,
Kariena Andres^1,2,
Charles Glover^1,2,
Eric C. Rouchka^6,8,
Scott R. Whittemore^1,2,3,4,5 &
…
Michal Hetman^1,2,3,4,5

Scientific Reports volume 13, Article number: 21254 (2023) Cite this article

728 Accesses
2 Citations
2 Altmetric
Metrics details

Subjects

Abstract

Reducing the loss of oligodendrocytes (OLs) is a major goal for neuroprotection after spinal cord injury (SCI). Therefore, the OL translatome was determined in Ribotag:Plp1-CreERT2 mice at 2, 10, and 42 days after moderate contusive T9 SCI. At 2 and 42 days, mitochondrial respiration- or actin cytoskeleton/cell junction/cell adhesion mRNAs were upregulated or downregulated, respectively. The latter effect suggests myelin sheath loss/morphological simplification which is consistent with downregulation of cholesterol biosynthesis transcripts on days 10 and 42. Various regulators of pro-survival-, cell death-, and/or oxidative stress response pathways showed peak expression acutely, on day 2. Many acutely upregulated OL genes are part of the repressive SUZ12/PRC2 operon suggesting that epigenetic de-silencing contributes to SCI effects on OL gene expression. Acute OL upregulation of the iron oxidoreductase Steap3 was confirmed at the protein level and replicated in cultured OLs treated with the mitochondrial uncoupler FCCP. Hence, STEAP3 upregulation may mark mitochondrial dysfunction. Taken together, in SCI-challenged OLs, acute and subchronic enhancement of mitochondrial respiration may be driven by axonal loss and subsequent myelin sheath degeneration. Acutely, the OL switch to oxidative phosphorylation may lead to oxidative stress that is further amplified by upregulation of such enzymes as STEAP3.

Antisense oligonucleotide therapeutic approach for Timothy syndrome

Article Open access 24 April 2024

Formation of memory assemblies through the DNA-sensing TLR9 pathway

Article Open access 27 March 2024

Messenger RNA transport on lysosomal vesicles maintains axonal mitochondrial homeostasis and prevents axonal degeneration

Article Open access 10 April 2024

Introduction

Contusive spinal cord injury (SCI) has a complex pathogenesis that involves time-dependent components including primary and secondary injuries as well as post-injury remodeling and plasticity¹. SCI associated white matter damage (WMD) is a major driver of functional deficits below the level of injury^2,3. Death of OLs contributes to WMD post-SCI^4,5. However, few OL-expressed genes such as p75/Ngfr⁶, Bax⁷ or Klk8⁸ have been implicated as mediators of SCI-induced OL death/WMD. This is at least partly due to limited insight into the OL gene expression programs after SCI.

Single cell (sc) RNASeq technology has been recently applied to study the SCI transcriptomic response at the cellular level^9,10. Specifically, scRNASeq-enabled transcriptomic phenotyping revealed region-specific changes in OL subtype content at chronic timepoints after hemisection or contusion SCI⁹. However, few SCI-associated OL gene expression changes were detected⁹. Importantly, the currently available scRNAseq technology has significant limitations. Its reliance on successful sorting of suspensions of viable cells is a problem when dealing with cells that have complex morphologies, spatially-regulated transcriptomes and/or sustained significant damage due to injury¹¹. Additional challenges include limited depths/low sensitivity, high level of stochastic variability of single cell transcriptomes, ambiguity in interpreting negative signals, and data set contamination with highly expressed mRNAs that were released from other cells that lysed during sample preparation and/or sorting^11,12. Last, but not least, scRNASeq is focused on overall cellular mRNA levels. Therefore, scRNASeq data lack information on gene expression regulation at the level of protein synthesis. Yet, such post-transcriptional regulation, that is present in neurons and OLs, plays a major role in response of those cells to pathologies^13,14,15.

Isolation of translating ribosomes and quantification of their associated mRNAs offers insight into the cell translatome and accounts for translation initiation, which is the critical regulatory step of protein synthesis^13,14,15. The Ribotag technology enables analysis of cell type-specific translatomes in whole animal studies¹⁶. In Ribotag mice, cell type-specific expression of the Cre recombinase results in removal of a stop codon to produce the RPL22 protein with a hemagglutinin (HA) tag at the C-terminus¹⁶. The large ribosomal subunit that contains RPL22-HA associates with the small ribosomal subunit during successful translation initiation, which enables immunoaffinity isolation of translating ribosomes by targeting the HA tag. Then, cell type-specific translatomes are determined by RNASeq¹⁶. Ribotag has been successfully applied to study astrocyte- or macrophage translatomes after contusive thoracic SCI^17,18.

The current study has been initiated to determine the translatome of mature OLs at various stages of recovery following moderate contusive SCI at the thoracic segment (T)9 level. Ribotag identified hundreds of differentially expressed genes in SCI-challenged OLs. These newly described gene expression landscapes implicate axonal disconnection and loss of myelin sheaths as major drivers of the acute OL gene expression response to SCI. Unexpectedly, similar factors may contribute to OL gene expression regulation in the subchronic phase of the recovery.

Results

Isolation and sequencing of the OL translatome from the spinal cord

OL-Ribotag mice were treated with tamoxifen to activate Cre-mediated recombination of the Rpl22^{fl(STOP)fl-HA} allele (Fig. 1a). Four weeks later, HA immunostaining was observed in 75% or 5% of CC1⁺ or CC1^- cells throughout the thoracic spinal cord, respectively (Fig. 1b–d). Double-positive cells were present in the white and grey matter, as expected for mature, CC1⁺ OLs (Fig. 1b,c). Conversely, in vehicle-treated controls, HA⁺ cells were rare (< 2% or < 0.5% CC1 + or CC1- cells, respectively, Fig. 1b–d). Therefore, tamoxifen treatment resulted in efficient and OL-specific expression of RPL22-HA in OL-Ribotag mice.

Next, moderate contusive SCI (50 kilodyne (kdyn), T9) was performed in tamoxifen-induced OL-Ribotag mice and 5 mm spinal cord segments spanning the lesion site were collected at day post-injury (dpi) 2, 10, and 42. These time points were selected based on dynamics of spinal cord pathology after SCI including acute OL loss at the injury epicenter (dpi 2), peak of delayed OL apoptosis in the spared white matter (dpi 10) and limited remyelination (dpi 42)^4,5,19. With the reported rostral-caudal lesion span of 1–1.5 mm after T9 50 kdyn IH SCI in young adult female mice²⁰, each sample included the primary damage site, spared white matter at the injury epicenter, the injury penumbra, and uninjured regions both caudal and rostral to the epicenter. Uninjured, naïve OL-Ribotag mice were used as controls. Control and dpi 42 samples were collected on the same day, i.e. 10 weeks after starting tamoxifen induction (16 weeks of age). As most mature OLs survive for at least 8 months of mouse life²¹, stable levels of Ribotag labeling are expected over a 10-week post-induction period.

After isolation of total spinal cord polysomes, OL polysomes were immunoprecipitated using anti-HA antibody. To verify their successful isolation, qPCR for neural cell marker transcripts was performed. As expected for translatomes from cells that are estimated to make about 20% of all spinal cord cells^22,23, the average enrichment of an OL marker mRNA reached 5.89 fold change (FC) total RNA control (Fig. 1e). Conversely, several astrocytic, neuronal or microglial marker transcripts were depleted from OL translatome samples (median FC 0.52, 0.36 or 0.1, respectively, Fig. 1e). Such a differential enrichment pattern was observed across all samples indicating successful isolation of the OL translatome from spinal cord tissue of OL-Ribotag mice.

RNASeq was then performed on all samples. The principal component analysis (PCA) of the resulting mRNA expression data revealed robust separation of samples that represented OL-enriched translatome vs. total RNA input with the PC1 accounting for 70% variance (Fig. 2a). Further separation was also evident including that between naïve and SCI samples (PC2, 19% variance, Fig. 2a) or dpi 2 vs. other samples (PC3, 4% variance, Fig. 2a).

Next, OL-enriched mRNAs were identified for each set of samples. As compared to total RNA inputs, OL enrichment of Log2FC(Total) > 0.5 was observed for 3,511, 3,302, 3,262, or 3,313 mRNAs in naïve, dpi 2, dpi 10 or dpi 42 samples, respectively (q < 0.05, Supplementary Table S1). While established mRNA markers of mature OLs were enriched, mRNA markers of neurons, astrocytes or microglia were depleted (Fig. 2b). Gene ontology term enrichment analysis (GO) was performed for OL-enriched mRNAs (Fig. 2c and Supplementary Table S2). The top enriched GO terms did not include OL-specific biological processes (BPs) or cellular components (CCs, Fig. 2c). However, OL-specific GO-BPs/CCs such as axon ensheathment, myelination, or myelin sheath were highly overrepresented when the analysis focused on 520 mRNAs with highly selective OL expression as defined by Log₂FC(Total) > 2, q < 0.05 (Fig. 2c). A similar pattern of GO term enrichment was observed when analyzing OL-specific mRNAs from other samples sets including dpi 2, 10, or 42 (Supplementary Fig. S1 and Table S2). Therefore, OL translatomes were successfully isolated from both intact and injured spinal cords.

Identification of differentially expressed OL genes after SCI

To compare SCI-mediated changes in OL-enriched translatomes, differentially expressed mRNAs were identified between OL SCI vs. OL naïve samples (|Log₂FC/naïve/|> 1, q < 0.05). However, several well-established markers of microglia/monocyte-derived macrophages were identified as OL-upregulated after SCI (Fig. 3a). Those included such mRNAs as Itgam/Cd11b, Cx3cr1, and Aif1/Iba1, whose post-SCI protein expression has been confirmed in many studies to be microglia/macrophage-specific (as reviewed in Ref.^19,24). In support of being expressed mainly by inflammatory cells, all those marker transcripts remained OL-depleted after SCI and their OL de-enrichment did not change significantly (Fig. 3a). Moreover, extensive overlaps were observed between SCI-upregulated mRNAs from OL translatomes and the top 500-microglia-enriched mRNAs (Fig. 3b). Such findings suggest that some mRNAs that are present in OL translatome represent a contamination from non-OL cells, as recently shown in heterologous culture systems²⁵. Such a non-specific co-purification with cell type-tagged ribosomes may be particularly relevant acutely after CNS injury when tissue cellularity changes and some mRNAs such as those expressed by the inflammatory cells become extremely abundant¹⁰. To reduce interference by those potential contaminating transcripts, a two-arm filtration procedure was applied to differentially expressed mRNAs from OL translatomes (Fig. 3c, see “Material and methods” for further details). By taking into account not just the OL expression change but also differential- (arm 1) or constantly high (arm 2) OL enrichment, the filtration identified hundreds of mRNAs that represent high confidence components of OL gene expression response to SCI (Fig. 3c,d, Supplementary Table S3).

Bioenergetic re-organization and reduced morphological complexity/connectivity as major components of OL-specific gene expression response to SCI

Among 344 highly upregulated transcripts on dpi 2, the top-enriched GO-BP terms included several broad categories such as those related to development, nervous system development, regulation of biological quality or signaling (Fig. 4a and Supplementary Table S4). More specificity emerged among top enriched GO-CC, GO-MF, and KEGG pathway terms. Those included such mitochondrial function-associated GOs as inner mitochondrial membrane, mitochondrion, proton-transporting ATP synthase activity, oxidative phosphorylation, or thermogenesis. Interestingly, eight components of the mitochondrial respirasome were highly upregulated (Log₂FC(naïve) > 1, Fig. 4b). Moreover, 30 additional respirasome genes were found when the OL upregulation threshold was lowered to Log₂FC(naïve) > 0.5 (q < 0.05, Fig. 4b). Together, over 55% of 68 OL-expressed mitochondrial respirasome genes (GO:0005746) were upregulated with median Log₂FC(naïve) = 0.87 (q < 0.05) suggesting a coordinated response to increase oxidative phosphorylation.

On dpi 10, 155 highly upregulated mRNAs showed greatest overrepresentation of several broad GO-BP terms that were related to development (Fig. 4c and Supplementary Table S4). In addition, enrichment of synapse-related GO-CCs was observed (Fig. 4c). Upregulation of synapse-associated transcripts could represent an attempt to re-establish OL-axonal synapses that were likely lost during the acute phase of SCI-associated axonal injury²⁶. Development remained a top-enriched GO-BP theme among 294 highly upregulated mRNAs on dpi 42 (Fig. 4d and Supplementary Table S4). In addition, high enrichment of mitochondria-associated GOs was found including the GO-CC or KEGG pathway terms mitochondrial inner membrane, mitochondrion, oxidative phosphorylation and thermogenesis (Fig. 4d). On dpi 42, both the spectrum and scale of mitochondria-related mRNA upregulations appeared to be even greater than that on dpi 2. Specifically, 48 mitochondrial respirasome genes were upregulated in OLs on dpi 42 (Log₂FC(naïve) > 0.5, q < 0.05, Fig. 4b) with median Log₂FC(naïve) = 0.95). These data suggest that on dpi 42, OL metabolism is again reorganized to favor oxidative phosphorylation.

On dpi 2, 278 highly downregulated mRNAs from the OL-enriched translatome showed high level enrichment for several broadly defined GO-BP terms that are associated with development (Fig. 5a and Supplementary Table S4). In addition, several top enriched GO-BP, GO-CC, and GO-MF terms were related to cytoskeleton organization, cell projection organization, cell periphery, cell projections, cell junctions and actin cytoskeleton. While GO-BP cell adhesion (GO:0007155) was not among top enriched terms, 40 out of 271 genes in that category were also down with Log₂FC(naïve) < − 1, − log(q) = 5.06). Such a functional profile of downregulated genes suggests adaptive changes to reduced morphological complexity of OLs and/or OL disconnection from other cells/extracellular matrix. Myelin sheath loss and disconnection from axons may be major drivers of these changes.

Although only 35 genes were highly downregulated in OLs on dpi 10, they showed significant overrepresentation of GO terms related to sterol biosynthesis (Fig. 5b and Supplementary Table S4). When data for all 23 OL-expressed components of the cholesterol biosynthesis superpathway were analyzed, 15 genes were downregulated (median Log₂FC(naïve) = − 1.14, q < 0.05) and just one (Hmgcs2) was upregulated (Fig. 5c, Supplementary Fig. S2). Importantly, downregulated genes included two critical regulators of cholesterol biosynthesis Hmgcr and Sqle (Fig. 5c, Supplementary Fig. S2). Both genes remained downregulated on dpi 42 (Fig. 5c). Importantly, all downregulated cholesterol biosynthesis genes showed OL-enriched expression in naïve, dpi 2, or dpi 10 mice with median log₂FC(Total) = 1.55, 1.61, or 1.39, respectively (q < 0.05, Fig. 5c). Such an expression pattern is consistent with a critical role of cholesterol synthesis in long term maintenance of myelin and survival of mature OLs²⁷. Therefore, downregulation of cholesterol biosynthesis may represent an adaptive response to loss of myelinated axons/myelin sheaths acutely post-SCI. It may also contribute to OL apoptosis subacutely post-SCI^4,5.

Top-enriched GOs for 224 transcripts that were highly downregulated on dpi 42 included several broadly defined terms such as development, biological regulation or binding (Fig. 5d and Supplementary Table S4). In addition, cell adhesion, cell junction and glutamatergic synapse were also enriched (Fig. 5d and Supplementary Table S4). This cell disconnection-like response resembles that observed on dpi 2. Collectively, GO analysis of OL-specific gene expression suggests a bioenergetic shift towards oxidative phosphorylation and reduction of morphological complexity/cell connectivity both acutely and subchronically post-SCI. At least subchronically, stress adaptation and OL survival is the likely outcome of such changes as most OL loss occurs during acute/subacute phases of the SCI recovery^4,5. However, those surviving OLs appear to undergo protracted degeneration including putative disconnection from axons.

Identifying candidate regulators of acute OL loss

After SCI, most OL loss occurs at the injury epicenter 24–48 h post SCI^4,5. Subacutely (dpi 7-dpi 21), additional OL loss via apoptosis is found in areas rostral and caudal from the epicenter^4,5. No major OL loss has been reported beyond dpi 28^4,5. Thus, OL mRNAs with acute post-SCI upregulation on dpi 2, but decreasing expression on dpi 42, may include major regulators of SCI-associated OL loss. Of 344 highly upregulated OL translatome mRNAs on dpi 2, 148 were either not significantly upregulated on dpi 42, or their dpi 42 levels were at least two-fold lower than on dpi 2 (Fig. 6a, Supplementary Table S5). GO analysis of those OL loss-associated mRNAs revealed enrichment for GO-BP terms related to cell signaling (Fig. 6b, Supplementary Table S5). Those included response to stimulus, regulation of cell communication, regulation of signaling, and signal transduction. Cell death-associated GO-BP terms were also moderately enriched (e.g. GO:1901214, regulation of neuron death, 12/381 genes, − log(q) = 2.80, Supplementary Table S5).

Literature analysis was performed to identify which of 148 acute-phase-specific mRNAs are likely regulators of OL loss. Interestingly, several of those transcripts encoded components of the two major OL survival signaling cascades: the ERK1/2 MAP kinase pathway and the PI3K-AKT pathway^28,29,30 (Fig. 6c,e,f). Both positive and negative regulators of those pathways were upregulated (Fig. 6c). In case of the ERK1/2 MAP kinase pathway, increased activity of the glutamate receptor (Grik3, Cacng5)³¹ or receptor tyrosine kinase-mediated inputs (Fgfr4, Fgfbp3)³² appeared to be opposed by upregulation of negative regulators of RAS/RAC1-mediated activation of MKK1/2 (Syngap1³³, Spry1, Spred3, Spry4³⁴, Steap3³⁵) or the ERK1/2 phosphatase Ptpn5³⁶. In the case of the PI3K-AKT pathway, enhanced inputs (Fgfr4, Fgfbp3, Itgad, Bcat1/Bcat2, Pfkfb4)^37,38,39 were opposed by upregulation of negative regulators including Parvb⁴⁰ and Inpp4a⁴¹. Interestingly, OL upregulation of the pro-survival cytokine Il17b⁴² may represent an autocrine/paracrine stress survival mechanism as the IL17b receptor mRNA (Il17rb) is highly OL-enriched both in uninjured and contused spinal cord (Log₂FC(Total) = 2.40, 2.87, 2.34, or 2.51 for naïve, dpi 2, 10, or 42, respectively, q < 0.05). Likewise, OLs appear as the main source of Il17b expression (Fig. 6e). Thus, pro-survival signaling networks are re-organized during the acute phase of OL response to SCI. Several of those changes may be adaptive and support survival under stress (e.g. activation of the ERK1/2-NFkB signaling by IL17B⁴², AKT activation by PFKFB4/BCAT1/2-mTOR^39,43, or PTPN5-mediated dephosphorylation of the activated ERK1/2 to limit ERK1/2 signaling duration⁴⁴).

Several acutely upregulated genes were identified as potential regulators of the cell death machinery (Fig. 6d–f). Activation of cell death may be promoted by: (i) reactive oxygen species (ROS) generating enzymes Pcyox1l and Pla2g3^45,46, (ii) an inhibitor of the death receptor-driven, pro-survival gene transcription, Tifab⁴⁷, (iii) a pro-excitotoxic inhibitor of JUN degradation, Prr7⁴⁸ and/or (iv) a positive regulator of pro-apoptotic MAP kinases JNK/p38, Steap3⁴⁹. Conversely, cell death initiation may be negatively regulated by increased expression of (i) enzymes that antagonize accumulation of the pro-apoptotic second messenger ceramide (Acsl5, Sphk1^50,51), (ii) Sphk1 which stimulates production of the cytoprotective lipid mediator sphingosine-1-phosphate⁵¹, (iii) negative regulators of ERK1/2 that may limit persistent, pro-necrotic activation of the ERK1/2 pathway in oxidative stress-exposed cells⁴⁴, (iv) several enzymes that contribute to anti-oxidant defenses reducing ROS toxicity (Lpo⁵², Apod⁵³, Bcat1/2⁵⁴, Aldh18a1⁵⁵, Pyccr1⁵⁶, Pfkfb4⁵⁷) and (v) reduced ROS generation due to Trf (transferrin)-mediated chelation of iron⁵⁸. Likewise, the lipid-peroxidation mediated effector phase of cell death cascades including ferroptosis may be antagonized by those positive regulators of anti-oxidant defenses. Opposite effects on oxidative mechanisms of cell death execution are expected from upregulation of the potentially pro-ferroptotic lysosomal/endosomal ferroreductase Steap3 which may promote lipid peroxidation^59,60,61. Similar consequences may follow upregulation of the GSH-depleting enzyme Chac1 compromising GSH-dependent lipid repair⁶¹. In addition, upregulation of the pro-apoptotic gene Hrk may promote SCI-induced OL apoptosis⁶².

Several upregulated genes with a potential to modulate oxidative cell death (Pla2g3, Apod, Trf, Lpo) encode for secreted proteins⁶³. In addition, on dpi 2, those transcripts are highly OL-enriched, suggesting OLs to be the main source of their respective protein products in the contused spinal cord tissue acutely post-SCI (Fig. 6e). Other acutely OL-upregulated genes showing a similar pattern include those for secreted serine proteases (Klk8, Klk9) and secreted serine protease inhibitors (Serpina3n, Serpina3c, Fig. 6e). Excessive extracellular serine protease activity may promote cell death and enhance white matter damage^8,64,65. Therefore, OLs appear to activate autocrine/paracrine mechanisms that may modify acute pathogenesis of SCI via regulation of oxidative stress and/or extracellular proteolysis. Such a concept is supported by a report that SCI-associated OL death and axonal damage was reduced in Klk8^–/– mice⁸.

Epigenetic de-silencing as a potential regulator of the OL response to SCI

To identify mechanisms that may contribute to SCI-associated OL gene expression changes, overlaps between OL-upregulated genes and public datasets of ChIPSeq-confirmed mouse genome operons were determined using the ChIPSeq database module of the X2K suite⁶⁶. At each post-SCI timepoint, top enriched operons included SUZ12 and MTF2, two components of the PRC2 chromatin repressive complex (Supplementary Table S6)⁶⁷. To further validate the specificity of these enrichments, z-scores were calculated for operon overlap gene counts of 344 dpi 2 upregulated genes vs. average overlap gene count for 10 random sets of 344 OL expressed genes. OL-upregulated genes showed overlaps with seven mouse genome operons that passed a stringent specificity criterion of z > 5 (Fig. 7a). Four of those operons (including three with top z-scores) were for the chromatin silencing factor SUZ12. Two other chromatin silencing factors including RCOR3 and MTF2 were also highly enriched. The specificity of those findings was further confirmed by weak operon enrichment among 289 acutely downregulated genes with a z-score range for top ten overlaps of 0.94–2.57 (Supplementary Table S6).

SUZ12/PRC2-mediated repressive methylation of histones is required for OL differentiation by downregulating differentiation inhibitory genes⁶⁸. RCOR3 is a negative regulator of the KDM1A-RCOR1/2/3-HDAC-driven gene silencing resulting in upregulation of genes that are repressed during cell differentiation⁶⁹. Suz12, Mtf2, and Rcor3 transcripts were detected both in total RNA and OL translatomes (Fig. 7b,c). Their expression was unaffected by SCI. While Suz12 and Mtf2 showed similar expression in OLs and total RNA, Rcor3 was moderately OL-enriched in all groups except dpi 42 (Fig. 7b,c). Therefore, the identified operons are matched by continuous OL expression of their respective regulators.

Interestingly, the list of 129 unique SUZ12 target genes that were OL-upregulated acutely after SCI showed overrepresentation of GO terms related to signaling (Fig. 7d) and included several potential regulators of acute OL loss (Supplementary Table S6). Most those PRC2 targets overlapped with the RCOR3 operon (Supplementary Table S6). The current data suggest that de-silencing of the PRC2 and RCOR3 operons may contribute to acute upregulation of many SCI-response genes in OLs. Moreover, de-silencing of PRC2-repressed genes may also play a role in OL gene upregulation on dpi 10 and dpi 42.

STEAP3 as a novel marker of acute OL response to SCI

STEAP3 is a membrane protein that acts as a major endosomal/lysosomal metalloreductase that reduces Fe³⁺/Cu³⁺ to Fe²⁺/Cu²⁺^59,70. As Fe²⁺ is then exported from endosomes/lysosomes to cytoplasm, STEAP3 plays an important role in the cellular iron supply^59,60,71, but may also promote lipid peroxidation and ferroptosis⁷². In addition, STEAP3 binds to cell death effectors and signaling mediators to promote apoptosis and/or reduce activity of the pro-survival pathways^35,49,73. It is also required for stress-dependent exosome secretion^74,75. Therefore, OL-upregulated Steap3 may promote OL loss, but may also, via stimulation of exosome-mediated secretion, affect axons and/or other spinal cord cells.

In OLs, Steap3 was sharply up on dpi 2 and remained elevated, albeit at reduced levels throughout the recovery period with Log₂FC(naive) ranging from 6.52 on dpi 2 to 3.67 on dpi 42 (Fig. 6e). In total RNA samples, Steap3 levels were also increased throughout the recovery period with an apparent peak on dpi 2 (Log₂FC(naive) = 2.9, 2.7, or 1.89 on dpi 2, 10, or 42, respectively, q < 0.05). On dpi 2, Steap3 was highly OL-enriched (Fig. 6e). At that timepoint, increased expression of STEAP3 protein was also confirmed by western blotting (Fig. 8a, b, Supplementary Fig. S5a). Therefore, at least acute OL translatome upregulation of Steap3 transcript correlates with increased bulk spinal cord expression of STEAP3 protein.

In naïve mice, faint STEAP3 immunofluorescence was observed in cell bodies of white matter OLs (CC1⁺, Fig. 8c, Supplementary Fig. S3). On dpi 1 or 2, such staining was also present throughout the spared ventral white matter at the injury epicenter as well as rostrally or caudally (Fig. 8c, Supplementary Figs. S3 and S4). Seemingly nuclear localization of OL STEAP3 staining on transverse sections may represent perinuclear localization that is polarized mainly in the rostral-caudal axis (Fig. 8c). Unipolar, perinuclear STEAP3 immunofluorescence that reflects localizations to the endosomal/trans-Golgi network (TGN) has been reported in various cell types^59,70,74,75.

Acutely after SCI (dpi 1 or 2), a strong STEAP3 signal was also found in ring-like structures throughout the spared white matter (Fig. 8c,d, Supplementary Figs. S3 and S4). Those structures were observed both at the injury epicenter as well as at the penumbra region rostrally and caudally from the injury site. They often showed partially overlapping and/or closely associated signals for the myelin marker MBP, as well as axonal marker phospho-NFH (p-NFH, Fig. 8d, Supplementary Fig. S4). Such close associations without a complete overlap suggest that after SCI STEAP3 marks OL paranodes and/or a peri-axonal compartment adjacent to damaged axons. However, testing such possibilities will require future imaging studies using longitudinal sections. Importantly, the STEAP3⁺ structures likely represent a specific STEAP3 signal, as they were not observed with a control IgG (Supplementary Figs. S3 and S4). Therefore, acute upregulation of OL STEAP3 may be directly related to degeneration of myelinated axons.

Interestingly, STEAP3 was upregulated following treatment of cultured OL precursor cells (OPCs) or OPC-derived OLs with a mitochondrial uncoupling drug FCCP, but not the ER stress inducer tunicamycin (Fig. 8e–h, Supplementary Fig. S5b,c). As FCCP uncouples mitochondrial respiration from ATP synthesis and increases mitochondrial ROS generation⁷⁶, upregulation of STEAP3 may represent a response of OL lineage cells to mitochondria dysfunction/mitochondrial oxidative stress. The latter form of cellular damage may upregulate expression of iron supply genes as mitochondrial biogenesis is a major driver of iron demand⁷⁷.

Finally, OL expression of another acutely upregulated and OL-enriched candidate pro-oxidant transcript, Pcyox1l⁴⁵ was confirmed by immunostaining. Unlike STEAP3, the PCYOX1L signal was observed mainly in cell bodies and adjacent processes of CC1⁺ or CNP⁺ OLs of the ventral white matter (Supplementary Fig. S6). Therefore, OLs may directly contribute to SCI-associated oxidative stress.

Discussion

The current data set represents the first comprehensive transcriptomic/translatomic description of OL gene expression response to SCI. Acutely, SCI-challenged OLs show translatomic changes indicative of a metabolic shift towards mitochondrial respiration that coincides with morphological simplification/cellular disconnection. Unexpectedly, a similar pattern reemerges subchronically. Acutely, OLs appear to undergo extensive re-organization of survival signaling networks. In addition, the acute OL translatome changes suggest an active role in regulation of cytotoxic mechanisms that contribute to secondary injury including extracellular proteolysis and oxidative stress. Epigenetic de-silencing appears to be a major driver of the SCI-activated OL gene expression. Lastly, STEAP3 is a novel marker of OL injury response that may play a dual role as a positive, pleiotropic regulator of OL death/degeneration and an enhancer of exosome-mediated intercellular communication.

The current translatome analysis revealed OL-specific, SCI-mediated upregulation of several genes that were also identified as OL-upregulated in a recent scRNASeq analysis of the mouse spinal cord tissue acutely/subacutely after moderate contusive SCI¹⁰. Those include Apod, Pla2g3, Trf, Klk8, Serpina3c, Serpina3n, Steap3, Pcyox1l, Itgad, Pfkfb4, Bact1/2 or Spry1. In addition, SCI-associated OL translatome changes show partial overlap with OL transcriptomic response to other types of CNS injury⁷⁸. Recent analysis of multiple scRNASeq datasets from various mouse models of white matter damage including amyloidosis, tauopathy, and cuprizone or lysolecithin-induced demyelination has identified three major transcriptomic profiles of disease-associated OLs called DA1, DA2 and IFN⁷⁸. Of note, signature transcripts of all those profiles showed significant overlaps with SCI-upregulated OL mRNAs (Supplementary Fig. S7). The greatest overlaps were observed for the persistent, pro-inflammatory DA1 cluster. The overlap peaked on dpi 10, but was clearly present also on dpi 2 and dpi 42. Interestingly, Steap3 has been identified as a marker of DA1⁷⁸. The overlap with the transient cell injury/cell survival response profile DA2 peaked on dpi 2. At that time, a strong overrepresentation of the inflammation-response cluster IFN was also present. In addition, similar to subacute and subchronic OL SCI translatomes, all disease-associated OL gene expression profiles showed downregulation of cholesterol biosynthesis⁷⁸.

While the current OL translatome dataset captures a large set of OL-expressed genes including those that are regulated by injury, it has important limitations that should be considered when choosing analysis tools or interpreting data. First, we observed apparent contamination of OL polysomes with mRNAs from non-OL cells including microglia/macrophages. Such a contamination may originate from non-specific interactions between highly abundant non-OL mRNAs and IgG-coupled magnetic beads that occurs during sample preparation. Similar contamination has been recently reported in co-cultures of human and mouse neural cells²⁵. The two-fold filtration process that took into account differential or constantly high OL enrichment reduced that contamination and identified high confidence components of the OL translatome that are regulated by SCI. However, while specificity of detection has improved, sensitivity likely suffered with over 60% of differentially expressed OL translatome mRNAs not passing the filtration criteria (Fig. 3). Therefore, some components of OL response are likely missed in our analysis. Nevertheless, such a conservative analysis approach is justified in acute CNS injury models where dramatic changes in both tissue cellular composition and transcriptomes of inflammatory cells are major factors that determine bulk transcriptome readouts.

Another limitation is related to imperfect OL specificity of Plp-CreERT2 expression. The Plp-CreERT2 transgenic line that was used here to activate the Ribotag also drives Cre-ERT2 activity in Schwann cells⁷⁹. As spinal cord samples my contain some short fragments of spinal nerve roots, it is possible that a small fraction of OL polysomes is of Schwann cell origin. Such a contamination of the current dataset is suggested by an apparent OL enrichment and SCI upregulation of marker transcripts of Schwann cell nerve injury response including Egfl8 or Gdnf (Supplementary Table S2)⁸⁰. Hence, at least some SCI-associated effects on OL gene expression may represent Schwann cell responses. Potential classification mistakes can be best avoided by confirming OL translatome mRNA changes using single cell level analysis of protein expression such as immunofluorescence or in situ hybridization-based approaches.

Last, but not least, the current study was limited to female mice, potentially missing male-specific components of the OL SCI translatome. As more post-operative complications arise after experimental SCI in males compared to females, including difficulty in bladder expression and increased frequency in urinary infections, the majority of rodent SCI literature uses females. Selecting only females for experimental SCI therefore provides a more successful survival rate and reliable sample size. For instance, several cell type specific SCI gene expression datasets were obtained from C57Bl6 female mice^10,17,18. Importantly, in that genetic background, post-contusion tissue loss/locomotor recovery shows subtle, mostly non-significant differences between males and females^81,82. Therefore, the current SCI dataset from C57Bl6 OL-Ribotag females may miss only few components of the OL translatome that are biologically relevant for tissue sparing and/or recovery in males.

In myelinating glia, mitochondrial respiration is critical for myelination during development as myelin synthesis requires a large amount of ATP^83,84. Conversely, in mature OLs, mitochondrial respiration is dispensable for OL survival, myelin sheath maintenance and axonal function⁸³. Instead, OLs support axonal energy needs by providing lactate that is generated via aerobic glycolysis⁸³. As electrical activity of axons is a major consumer of OL-generated lactate⁸³, one could expect that OL disconnection from axons and/or axonal damage would lower lactate demand. Consequently, aerobic glycolysis would be reduced and mitochondrial respiration would increase. Our observed concomitant upregulation of respirasome genes, together with downregulation of genes associated with morphological complexity/connectivity, supports such a scenario both acutely and subchronically after SCI. Indeed, while most SCI-associated axonal loss is acute, continuing subchronic degeneration of myelinated axons has been described^85,86. In addition, subchronic reorganization of OL respiration may be a response to post-SCI reduction in activity of specific axonal tracts and/or spinal circuitries.

Interestingly, SCI-mediated changes in OL gene expression indicate their active role in modifying the tissue environment to modulate secondary injury. Such effects may be mediated both by canonical secretion of signal-peptide containing proteins and non-canonical secretion by exosomes, as STEAP3 is a major regulator of exosome-mediated secretion^74,75. Of note, significant enrichment of secreted proteins among upregulated genes on dpi 10 or dpi 42 suggests that OLs regulate not only acute damage, but also subacute/subchronic repair of the contused spinal cord.

In summary, this study establishes the first translatomic chart of OL response to thoracic contusive SCI. Acutely upregulated candidate regulators of OL survival are identified. Their increased expression is likely driven by axonal disconnection, oxidative damage, and inflammation. Subchronic gene expression changes suggest reduced myelination and decreased axonal trophic support by surviving OLs. Moreover, upregulation of various secretome genes and transcriptional similarity to immunogenic OLs from other white matter injury models⁷⁸ (Supplementary Fig. S7) supports an active role of OLs in tissue damage and repair throughout the post-SCI recovery period. Importantly, previously unrecognized aspects of OL biology after SCI are uncovered. Those include putative metabolic re-programming, re-organization of intracellular signaling and epigenetic de-silencing as a major driver of OL gene expression response to injury. At least some of those newly identified elements of SCI pathogenesis may be further validated as potential targets for therapies to reduce white matter damage and/or dysfunction after SCI. For instance, reducing oxidative phosphorylation in OLs or targeting OL upregulated cell death genes may attenuate acute loss of OLs. Subchronically, therapeutic downregulation of oxidative phosphorylation or upregulation of cholesterol biosynthesis may be beneficial for long-term maintenance of white matter integrity and/or axonal function. Finally, the SCI-associated OL translatome data will be useful for design and/or interpretation of mechanistic studies of SCI-associated white matter damage as well as other types of white matter pathology. Current data are available as a searchable database at SCI OL Gene Expression Database (scigenedatabase.com).

Materials and methods

Animals

All animal procedures were performed in strict accordance with the Public Health Service Policy on Humane Care and Use of Laboratory Animals, Guide for the Care and Use of Laboratory Animals (Institute of Laboratory Animal Resources, National Research Council, 2011), and adhered to NIH guidelines on the use of experimental animals. Animal procedures were approved by the University of Louisville Institutional Animal Care and Use (IACUC) and Institutional Biosafety (IBC) Committees. Timed-pregnant Sprague–Dawley rats and wild-type (WT) C57Bl/6 mice (6–8 weeks) were obtained from Envigo (Indianapolis, IN). Plp-cre^ERT2 (proteolipid protein) (B6.Cg-Tg (Plp1-Cre/ERT)3Pop/J; Stock No: 005975)⁷⁹ and Ribotag mice (Rpl22^{fl(STOP)fl-HA/wt}, B6J.129(Cg)-Rpl22 ^tm1.1Psam2/SjJ); Stock No: 029977)¹⁶ mice, both on C57Bl/6 background, were acquired from the Jackson Laboratory (Bar Harbor, ME). These lines were crossed to produce OL-Ribotag mice (Plp1-Cre^ERT2+/wt:Rpl22^{fl(STOP)fl-HA/wt}). Genotypes of the crosses were confirmed using standard PCR genotyping as recommended by the Jackson Laboratory. To induce OL Ribotag expression, 6 week old OL-Ribotag mice received 1 mg tamoxifen (20 mg/mL in sunflower oil) i.p. daily for 8 days as previously described⁸⁷. Male and female mice were used for initial validation of Ribotag transgene induction. Reporting of animal studies described in this manuscript followed ARRIVE guidelines.

SCI

Female WT or OL-Ribotag mice were used for SCI at 8–10 weeks of age. In tamoxifen-induced mice, SCI was performed 3 weeks after completion of the induction treatment. Anesthetized animals (400 mg/kg body weight 2,2,2-tribromoethanol i.p.) were shaved around the surgical site and disinfected using 4% chlorohexidine solution. Lacri-Lube ophthalmic ointment (Allergen, Madison, NJ) was applied to prevent eye drying. Following dorsal laminectomy at the T9 vertebrae, moderate contusive SCI was performed using the IH impactor (50 kdyn force/400–600 μm displacement, Infinite Horizons, Lexington, KY) as previously described⁸⁸. Starting immediately after surgery, postoperative care included 0.1 ml saline (s.c. daily for 7 days), 5 mg/kg gentamycin (s.c. daily for 7 days), 0.1 mg/kg buprenorphine (s.c. every 12 h for 2 days), and manual expression of bladders twice a day for seven to ten days or until spontaneous voiding returned. All surgical and post-surgery procedures were completed according to NIH and IACUC guidelines. All surgeries were performed without knowledge of group assignment or genotype.

Tissue collection

Anesthetized mice were transcardially perfused with phosphate buffered saline (PBS, 4 °C). For immunostaining, this was followed by 4% paraformaldehyde (PFA in PBS, 4 °C) perfusion. Then, a 5 mm portion of the spinal cord spanning the injury epicenter was dissected and (i) post-fixed for 1 h in 4% PFA at 4 °C (immunostaining) or (ii) flash frozen in liquid nitrogen and stored at − 80 °C until further use (Ribotag polysome purification or RNA/protein isolation).

Immunostaining

Tissue processing, preparation of frozen 20 μm coronal (transverse) spinal cord sections, immunostaining and epifluorescent (RPL22-HA) or confocal (STEAP3, PCYOX1L) imaging followed standard protocols as previously described^88,89 (see Supplementary Methods for more details). Primary antibodies used were as follows: anti-APC (mouse CC1 clone, 1:200, Abcam, Cambridge, UK, Cat# ab16794, RRID: AB_443473), anti-HA (mouse, 1:1000, HA.11 Clone 1612, Biolegend, San Diego, CA, Cat# 901516, RRID: AB_2820200), anti-STEAP3 (species: rabbit, dilution: 1:200, Thermo Fisher Scientific, Walthanm, MA, Cat# PA5-102321, RRID: AB_2851729), anti-phospho-NFH (species: mouse, dilution: 1:500, Biolegend Cat# 801601, RRID: AB_2564641), anti-MBP (species: chicken, dilution: 1:500, Thermo Fisher Scientific Cat# PA1-10008, RRID: AB_1077024), anti-PCYOX1L (species: rabbit, dilution: 1:200, Atlas Antibodies, Bromma, Sweden, Cat# HPA037463, RRID: AB_10673632), and anti-CNP (species: mouse, Clone SMI 91, dilution: 1:200, Biolegend Cat# 836404, RRID AB_2566639). Species- and isotype-specific Alexa488-, Alexa594-, or Alexa647- F(ab’)2 secondary antibodies (donkey, 1:200, Life Technologies) were used. Counting of Ribotag/HA⁺ cells was performed without knowledge of sample origin (genotype or tamoxifen treatment).

Ribotag RNA purification and RNASeq

Frozen spinal cord samples from 2 female mice were pooled to produce one biological replicate (3 biological replicates/group representing 6 animals). Pooling was done to increase RNA yield of the Ribotag immune-purification as determined in pilot experiments. SCI and naïve mouse spinal cord samples were processed for input polysome-associated mRNAs (total spinal cord mRNA) or immune-purified OL polysome mRNAs using anti-HA antibody and magnetic beads as previously described^16,90 (see Supplementary Methods for a detailed description of the protocol). RNA isolation, mRNA library preparation and RNA sequencing on the Illumina NextSeq 500 platform followed standard procedures. Before preparing RNASeq libraries, successful isolation of OL polysomes was verified by qPCR for OL- and non-OL cell marker transcripts. Pilot studies revealed that polysome immunoprecipitation using a control IgG produced low RNA yields as compared to the anti-HA antibody confirming the specificity of the latter reagent (average of 48.4 or 285.34 ng RNA/sample with IgG or anti-HA, respectively. Given such a disparity in RNA recovery between the control IgG and the anti-HA antibody, further analyses focused on anti-HA-purified OL translatomes.

Quantitative real-time PCR

To prepare cDNA, the SuperScript IV system was used following manufacturer’s recommendations (Thermo Fisher, Cat# 18091050). qPCR was run using a microfluidic Custom TaqMan Gene Expression Array Card (Thermo Fisher, Cat# 43442249) containing primers for CNS cell-type specific marker mRNAs of oligodendrocytes (Mbp, Plp1, Mog, Cldn11, Mobp, Opalin, Mag, Fa2h, Gjb1, Ermn, Gjc2, Klk6, Sox10), astrocytes (Aldh1l1, Hgf), neurons (Reln, Snap25, Lhx5), and microglia/macrophages (Osm, Cd68, Tmem19). The card design is shown in Supplementary Methods. RNA levels were quantified using the ΔΔCT method with Hprt, Ppia, and 18S rRNA as reference transcripts. For each sample pair (OL translatome and total spinal cord RNA from which OL translatome was isolated), OL mRNA levels were determined as a fold change of their total spinal cord expression.

RNAseq data analysis

Analysis of sequenced RNA was performed by the Kentucky IDeA Networks of Biomedical Research Excellence (KY INBRE) Bioinformatics Core. A quality control analysis was performed using FastQC (v.0.10.1) and indicated good sequencing quality. The reads were directly aligned to the Mus musculus reference genome (mm10.fa) using the STAR aligner (version 2.6). The average number of sequenced reads per sample was ~ 37,500,000 with an average alignment rate of 98.06%. Raw read counts were generated using HTSeq (v.0.10.0) and input to DESeq2 for differential expression analysis. The raw counts were normalized using Relative Log Expression (RLE) and filtered to exclude genes with fewer than 10 counts across samples. Principal component analysis (PCA) was performed on all 24 samples to measure variance of the overall mRNA expression pattern across all groups/sample sets. As part of the differential expression analysis, a DESeq2 interaction term was used to analyze differential OL enrichment after SCI and can be defined by delta Log₂FC(OL/total) = Log₂FC(OL/total)_SCI – Log₂FC(OL/total)_naive. In this analysis, OL translatome- and total spinal cord RNA samples from the same tissue were paired for determination of differential OL enrichment. To identify genes with an RNA origin-specific effect (OL vs. total) at one or more time points, the full regression model was compared to a reduced model using a likelihood ratio test. Group-specific effects were then identified at individual timepoints using a Wald test for significance. As a significant contamination of non-OL transcripts has been detected in OL translatomes from SCI samples, a two arm filtration process was implemented to identify highly likely components of the OL translatome that are differentially expressed after SCI (Fig. 3a–c). In arm 1, the identified transcripts were filtered using the DESeq2 interaction function to identify those that also showed differential OL enrichment of the same direction/magnitude (change of OL enrichment in SCI vs. naïve samples, |Log₂FC/Total/_SCI-Log₂FC/Total/_naïve|> 1, q < 0.05). This process eliminated mRNAs whose OL translatome changes are driven primarily by their parallel changes in the total RNA pool. However, differential enrichment analysis may miss those mRNAs whose OL translatome levels change in the same direction/magnitude as in total spinal cord samples. In case of constantly OL-enriched mRNAs that also show preferential OL expression (Log₂FC(Total) > 1, q < 0.05), their exclusion is not justified as OLs are their major expressors and cross-contamination from other cell types is less likely. In total RNA samples, injury related changes of such OL-enriched transcripts are still a reflection of OL-specific response to SCI. Therefore, the second arm of the filtration procedure identified those highly regulated mRNAs that were also OL enriched regardless of passing the differential enrichment filtration. All RNASeq data are available in GEO (Accession number: GSE225308). A searchable, public database is also available at http://scigenedatabase.com/.

Gene ontology (GO) functional annotation analysis was performed using g:Profiler (version e108_eg55_p17_d098162)⁹¹. Mitochondrial respirasome genes (Respirasome_GO_0005746) were retrieved from g:Profiler, and cholesterol biosynthesis superpathway genes were retrieved from Kegg Pathway (mmu:00100 and mmu:00900) and BioCyc databases⁹². Mitochondrial respirasome (Respirasome_GO_0005746) and cholesterol biosynthesis superpathway gene lists were retrieved from Metascape or Kegg Pathway (mmu:00100 and mmu:00900) databases, respectively.

Gene list overlap analyses

Lists of top 500 neural cell type-enriched transcripts were obtained from the BrainRNASeq database (www.brainrnaseq.org)⁹³. Their overlaps with the SCI-regulated OL mRNAs were analyzed using the hypergeometric test (http://nemates.org/MA/progs/overlap_stats.html). In those calculations, the maximal number of all identified transcripts across all analyzed samples (17,168) was used to define the size of the whole spinal cord transcriptome.

Immunoblotting

Immunoblotting followed standard methodology as previously described (see Supplementary methods for more details)⁸⁸. The primary antibodies included STEAP3 and GAPDH (loading control) (Chemicon, 1:1000, Temecula, CA). BioRad ChemiDoc MP Imaging System was used. Immunoblot quantifications were performed using TIFF-formatted blot images and BioRad Image Lab software.

Cell culture

Culture and OL differentiation of adult rat spinal cord OPCs followed previously described methodology⁹⁴. Cells were treated as follows: FCCP (50 μM working stock solution in 0.1%DMSO/cell culture medium that was diluted to final concentrations 500 nM or 100 nM in cell culture medium; the FCCP stock was always prepared fresh), Tunicamycin (10 mg/ml stock prepared in DMSO and diluted to the final concentration of 2 μg/ml in culture medium) or their vehicle (0.02% DMSO in culture medium). Each solution was made fresh on day of treatment.

Statistical analysis of immunostaining, qPCR and immunoblotting data

All ratiometric data including % Ribotag⁺ cells, qPCR-determined total spinal cord-normalized OL mRNA levels and control treatment-normalized protein expression were performed using the two-tailed non-parametric Mann–Whitney u-test.

Data availability

All key data are part of the manuscript and/or are available in the public repository (NCBI GEO Accession number: GSE225308) or via authors web page (http://scigenedatabase.com/). Additional data would be shared by the corresponding author upon reasonable request.

References

Ahuja, C. S. et al. Traumatic spinal cord injury. Nat. Rev. Dis. Primers 3, 17018. https://doi.org/10.1038/nrdp.2017.18 (2017).
Article PubMed Google Scholar
Basso, D. M., Beattie, M. S. & Bresnahan, J. C. Graded histological and locomotor outcomes after spinal cord contusion using the NYU weight-drop device versus transection. Exp. Neurol. 139, 244–256. https://doi.org/10.1006/exnr.1996.0098 (1996).
Article CAS PubMed Google Scholar
Magnuson, D. S. et al. Comparing deficits following excitotoxic and contusion injuries in the thoracic and lumbar spinal cord of the adult rat. Exp. Neurol. 156, 191–204. https://doi.org/10.1006/exnr.1999.7016 (1999).
Article CAS PubMed Google Scholar
Pukos, N., Goodus, M. T., Sahinkaya, F. R. & McTigue, D. M. Myelin status and oligodendrocyte lineage cells over time after spinal cord injury: What do we know and what still needs to be unwrapped?. Glia 67, 2178–2202. https://doi.org/10.1002/glia.23702 (2019).
Article PubMed PubMed Central Google Scholar
Duncan, G. J. et al. The fate and function of oligodendrocyte progenitor cells after traumatic spinal cord injury. Glia 68, 227–245. https://doi.org/10.1002/glia.23706 (2020).
Article PubMed Google Scholar
Beattie, M. S. et al. ProNGF induces p75-mediated death of oligodendrocytes following spinal cord injury. Neuron 36, 375–386 (2002).
Article CAS PubMed PubMed Central Google Scholar
Dong, H. et al. Enhanced oligodendrocyte survival after spinal cord injury in Bax-deficient mice and mice with delayed Wallerian degeneration. J. Neurosci. 23, 8682–8691. https://doi.org/10.1523/JNEUROSCI.23-25-08682.2003 (2003).
Article CAS PubMed PubMed Central Google Scholar
Terayama, R. et al. Neuropsin promotes oligodendrocyte death, demyelination and axonal degeneration after spinal cord injury. Neuroscience 148, 175–187. https://doi.org/10.1016/j.neuroscience.2007.05.037 (2007).
Article CAS PubMed Google Scholar
Floriddia, E. M. et al. Distinct oligodendrocyte populations have spatial preference and different responses to spinal cord injury. Nat. Commun. 11, 5860. https://doi.org/10.1038/s41467-020-19453-x (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Milich, L. M. et al. Single-cell analysis of the cellular heterogeneity and interactions in the injured mouse spinal cord. J. Exp. Med. https://doi.org/10.1084/jem.20210040 (2021).
Article PubMed PubMed Central Google Scholar
Potter, S. S. Single-cell RNA sequencing for the study of development, physiology and disease. Nat. Rev. Nephrol. 14, 479–492. https://doi.org/10.1038/s41581-018-0021-7 (2018).
Article CAS PubMed PubMed Central Google Scholar
Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience https://doi.org/10.1093/gigascience/giaa151 (2020).
Article PubMed PubMed Central Google Scholar
Kapur, M., Monaghan, C. E. & Ackerman, S. L. Regulation of mRNA translation in neurons-a matter of life and death. Neuron 96, 616–637. https://doi.org/10.1016/j.neuron.2017.09.057 (2017).
Article CAS PubMed PubMed Central Google Scholar
Kats, I. R. & Klann, E. Translating from cancer to the brain: Regulation of protein synthesis by eIF4F. Learn. Mem. 26, 332–342. https://doi.org/10.1101/lm.050047.119 (2019).
Article CAS PubMed PubMed Central Google Scholar
Elbaz, B. & Popko, B. Molecular control of oligodendrocyte development. Trends Neurosci. 42, 263–277. https://doi.org/10.1016/j.tins.2019.01.002 (2019).
Article CAS PubMed PubMed Central Google Scholar
Sanz, E. et al. Cell-type-specific isolation of ribosome-associated mRNA from complex tissues. Proc. Natl. Acad. Sci. U.S.A. 106, 13939–13944. https://doi.org/10.1073/pnas.0907143106 (2009).
Article ADS PubMed PubMed Central Google Scholar
Anderson, M. A. et al. Astrocyte scar formation aids central nervous system axon regeneration. Nature 532, 195–200. https://doi.org/10.1038/nature17623 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Zhu, Y. et al. Macrophage transcriptional profile identifies lipid catabolic pathways that can be therapeutically targeted after spinal cord injury. J. Neurosci. 37, 2362–2376. https://doi.org/10.1523/JNEUROSCI.2751-16.2017 (2017).
Article CAS PubMed PubMed Central Google Scholar
Milich, L. M., Ryan, C. B. & Lee, J. K. The origin, fate, and contribution of macrophages to spinal cord injury pathology. Acta Neuropathol. 137, 785–797. https://doi.org/10.1007/s00401-019-01992-3 (2019).
Article PubMed PubMed Central Google Scholar
Myers, S. A., Andres, K. R., Hagg, T. & Whittemore, S. R. CD36 deletion improves recovery from spinal cord injury. Exp. Neurol. 256, 25–38. https://doi.org/10.1016/j.expneurol.2014.03.016 (2014).
Article CAS PubMed PubMed Central Google Scholar
Tripathi, R. B. et al. Remarkable stability of myelinating oligodendrocytes in mice. Cell Rep. 21, 316–323. https://doi.org/10.1016/j.celrep.2017.09.050 (2017).
Article CAS PubMed PubMed Central Google Scholar
Valerio-Gomes, B., Guimaraes, D. M., Szczupak, D. & Lent, R. The absolute number of oligodendrocytes in the adult mouse brain. Front. Neuroanat. 12, 90. https://doi.org/10.3389/fnana.2018.00090 (2018).
Article CAS PubMed PubMed Central Google Scholar
Dawson, M. R., Polito, A., Levine, J. M. & Reynolds, R. NG2-expressing glial progenitor cells: An abundant and widespread population of cycling cells in the adult rat CNS. Mol. Cell. Neurosci. 24, 476–488. https://doi.org/10.1016/s1044-7431(03)00210-0 (2003).
Article CAS PubMed Google Scholar
David, S. & Kroner, A. Repertoire of microglial and macrophage responses after spinal cord injury. Nat. Rev. Neurosci. 12, 388–399. https://doi.org/10.1038/nrn3053 (2011).
Article CAS PubMed Google Scholar
Gregory, J. A. et al. Cell type-specific in vitro gene expression profiling of stem cell-derived neural models. Cells https://doi.org/10.3390/cells9061406 (2020).
Article PubMed PubMed Central Google Scholar
Hughes, A. N. & Appel, B. Oligodendrocytes express synaptic proteins that modulate myelin sheath formation. Nat. Commun. 10, 4125. https://doi.org/10.1038/s41467-019-12059-y (2019).
Article ADS CAS PubMed PubMed Central Google Scholar
Miron, V. E. et al. Statin therapy inhibits remyelination in the central nervous system. Am. J. Pathol. 174, 1880–1890. https://doi.org/10.2353/ajpath.2009.080947 (2009).
Article CAS PubMed PubMed Central Google Scholar
Lee, J. Y., Oh, T. H. & Yune, T. Y. Ghrelin inhibits hydrogen peroxide-induced apoptotic cell death of oligodendrocytes via ERK and p38MAPK signaling. Endocrinology 152, 2377–2386. https://doi.org/10.1210/en.2011-0090 (2011).
Article CAS PubMed Google Scholar
Vemuri, G. S. & McMorris, F. A. Oligodendrocytes and their precursors require phosphatidylinositol 3-kinase signaling for survival. Development 122, 2529–2537. https://doi.org/10.1242/dev.122.8.2529 (1996).
Article CAS PubMed Google Scholar
Flores, A. I. et al. Akt-mediated survival of oligodendrocytes induced by neuregulins. J. Neurosci. 20, 7622–7630. https://doi.org/10.1523/JNEUROSCI.20-20-07622.2000 (2000).
Article CAS PubMed PubMed Central Google Scholar
Wiegert, J. S. & Bading, H. Activity-dependent calcium signaling and ERK-MAP kinases in neurons: A link to structural plasticity of the nucleus and gene transcription regulation. Cell Calcium 49, 296–305. https://doi.org/10.1016/j.ceca.2010.11.009 (2011).
Article CAS PubMed Google Scholar
McKay, M. M. & Morrison, D. K. Integrating signals from RTKs to ERK/MAPK. Oncogene 26, 3113–3121. https://doi.org/10.1038/sj.onc.1210394 (2007).
Article CAS PubMed Google Scholar
Rumbaugh, G., Adams, J. P., Kim, J. H. & Huganir, R. L. SynGAP regulates synaptic strength and mitogen-activated protein kinases in cultured neurons. Proc. Natl. Acad. Sci. U.S.A. 103, 4344–4351. https://doi.org/10.1073/pnas.0600084103 (2006).
Article ADS CAS PubMed PubMed Central Google Scholar
Cabrita, M. A. & Christofori, G. Sprouty proteins, masterminds of receptor tyrosine kinase signaling. Angiogenesis 11, 53–62. https://doi.org/10.1007/s10456-008-9089-1 (2008).
Article CAS PubMed Google Scholar
Li, P. L. et al. STEAP3 (six-transmembrane epithelial antigen of prostate 3) inhibits pathological cardiac hypertrophy. Hypertension 76, 1219–1230. https://doi.org/10.1161/HYPERTENSIONAHA.120.14752 (2020).
Article CAS PubMed Google Scholar
Choi, Y. S. et al. Status epilepticus-induced somatostatinergic hilar interneuron degeneration is regulated by striatal enriched protein tyrosine phosphatase. J. Neurosci. 27, 2999–3009 (2007).
Article CAS PubMed PubMed Central Google Scholar
Hemmings, B. A. & Restuccia, D. F. PI3K-PKB/Akt pathway. Cold Spring Harbor Perspect. Biol. 4, a011189. https://doi.org/10.1101/cshperspect.a011189 (2012).
Article CAS Google Scholar
Sivanand, S. & Vander Heiden, M. G. Emerging roles for branched-chain amino acid metabolism in cancer. Cancer Cell 37, 147–156. https://doi.org/10.1016/j.ccell.2019.12.011 (2020).
Article CAS PubMed PubMed Central Google Scholar
Pegoraro, C. et al. PFKFB4 controls embryonic patterning via Akt signalling independently of glycolysis. Nat. Commun. 6, 5953. https://doi.org/10.1038/ncomms6953 (2015).
Article ADS CAS PubMed Google Scholar
Mongroo, P. S. et al. Beta-parvin inhibits integrin-linked kinase signaling and is downregulated in breast cancer. Oncogene 23, 8959–8970. https://doi.org/10.1038/sj.onc.1208112 (2004).
Article CAS PubMed Google Scholar
Ivetac, I. et al. Regulation of PI(3)K/Akt signalling and cellular transformation by inositol polyphosphate 4-phosphatase-1. EMBO Rep. 10, 487–493. https://doi.org/10.1038/embor.2009.28 (2009).
Article CAS PubMed PubMed Central Google Scholar
Bastid, J., Dejou, C., Docquier, A. & Bonnefoy, N. The emerging role of the IL-17B/IL-17RB pathway in cancer. Front. Immunol. 11, 718. https://doi.org/10.3389/fimmu.2020.00718 (2020).
Article CAS PubMed PubMed Central Google Scholar
Neinast, M., Murashige, D. & Arany, Z. Branched chain amino acids. Annu. Rev. Physiol. 81, 139–164. https://doi.org/10.1146/annurev-physiol-020518-114455 (2019).
Article CAS PubMed Google Scholar
Chu, C. T., Levinthal, D. J., Kulich, S. M., Chalovich, E. M. & DeFranco, D. B. Oxidative neuronal injury. The dark side of ERK1/2. Eur. J. Biochem. 271, 2060–2066 (2004).
Article CAS PubMed PubMed Central Google Scholar
Banfi, C. et al. Prenylcysteine oxidase 1, an emerging player in atherosclerosis. Commun. Biol. 4, 1109. https://doi.org/10.1038/s42003-021-02630-z (2021).
Article CAS PubMed PubMed Central Google Scholar
Yagami, T., Yamamoto, Y. & Koma, H. The role of secretory phospholipase A(2) in the central nervous system and neurological diseases. Mol. Neurobiol. 49, 863–876. https://doi.org/10.1007/s12035-013-8565-9 (2014).
Article CAS PubMed Google Scholar
Niederkorn, M., Agarwal, P. & Starczynowski, D. T. TIFA and TIFAB: FHA-domain proteins involved in inflammation, hematopoiesis, and disease. Exp. Hematol. 90, 18–29. https://doi.org/10.1016/j.exphem.2020.08.010 (2020).
Article CAS PubMed Google Scholar
Kravchick, D. O. et al. Synaptonuclear messenger PRR7 inhibits c-Jun ubiquitination and regulates NMDA-mediated excitotoxicity. EMBO J. 35, 1923–1934. https://doi.org/10.15252/embj.201593070 (2016).
Article CAS PubMed PubMed Central Google Scholar
Guo, W. Z. et al. Six-transmembrane epithelial antigen of the prostate 3 deficiency in hepatocytes protects the liver against ischemia-reperfusion injury by suppressing transforming growth factor-beta-activated kinase 1. Hepatology 71, 1037–1054. https://doi.org/10.1002/hep.30882 (2020).
Article CAS PubMed Google Scholar
Senkal, C. E. et al. Ceramide is metabolized to acylceramide and stored in lipid droplets. Cell Metab. 25, 686–697. https://doi.org/10.1016/j.cmet.2017.02.010 (2017).
Article CAS PubMed PubMed Central Google Scholar
Ueda, N. A rheostat of ceramide and sphingosine-1-phosphate as a determinant of oxidative stress-mediated kidney injury. Int. J. Mol. Sci. https://doi.org/10.3390/ijms23074010 (2022).
Article PubMed PubMed Central Google Scholar
Yamakaze, J. & Lu, Z. Deletion of the lactoperoxidase gene causes multisystem inflammation and tumors in mice. Sci. Rep. 11, 12429. https://doi.org/10.1038/s41598-021-91745-8 (2021).
Article CAS PubMed PubMed Central Google Scholar
Elliott, D. A., Weickert, C. S. & Garner, B. Apolipoproteins in the brain: Implications for neurological and psychiatric disorders. Clin. Lipidol. 51, 555–573. https://doi.org/10.2217/CLP.10.37 (2010).
Article CAS PubMed PubMed Central Google Scholar
Wang, K. et al. Branched-chain amino acid aminotransferase 2 regulates ferroptotic cell death in cancer cells. Cell Death Differ. 28, 1222–1236. https://doi.org/10.1038/s41418-020-00644-4 (2021).
Article ADS CAS PubMed Google Scholar
Coutelier, M. et al. Alteration of ornithine metabolism leads to dominant and recessive hereditary spastic paraplegia. Brain 138, 2191–2205. https://doi.org/10.1093/brain/awv143 (2015).
Article PubMed PubMed Central Google Scholar
Hollinshead, K. E. R. et al. Oncogenic IDH1 mutations promote enhanced proline synthesis through PYCR1 to support the maintenance of mitochondrial redox homeostasis. Cell Rep. 22, 3107–3114. https://doi.org/10.1016/j.celrep.2018.02.084 (2018).
Article CAS PubMed PubMed Central Google Scholar
Yi, M. et al. 6-Phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 and 4: A pair of valves for fine-tuning of glucose metabolism in human cancer. Mol. Metab. 20, 1–13. https://doi.org/10.1016/j.molmet.2018.11.013 (2019).
Article CAS PubMed Google Scholar
Daruich, A. et al. Iron is neurotoxic in retinal detachment and transferrin confers neuroprotection. Sci. Adv. 5, eaau9940. https://doi.org/10.1126/sciadv.aau9940 (2019).
Article ADS CAS PubMed PubMed Central Google Scholar
Ohgami, R. S. et al. Identification of a ferrireductase required for efficient transferrin-dependent iron uptake in erythroid cells. Nat. Genet. 37, 1264–1269. https://doi.org/10.1038/ng1658 (2005).
Article CAS PubMed PubMed Central Google Scholar
Lambe, T. et al. Identification of a Steap3 endosomal targeting motif essential for normal iron metabolism. Blood 113, 1805–1808. https://doi.org/10.1182/blood-2007-11-120402 (2009).
Article CAS PubMed PubMed Central Google Scholar
Ratan, R. R. The chemical biology of ferroptosis in the central nervous system. Cell Chem. Biol. 27, 479–498. https://doi.org/10.1016/j.chembiol.2020.03.007 (2020).
Article CAS PubMed PubMed Central Google Scholar
Yin, K. J. et al. JNK activation contributes to DP5 induction and apoptosis following traumatic spinal cord injury. Neurobiol. Dis. 20, 881–889. https://doi.org/10.1016/j.nbd.2005.05.026 (2005).
Article CAS PubMed Google Scholar
Kaspric, N., Picard, B., Reichstadt, M., Tournayre, J. & Bonnet, M. ProteINSIDE to easily investigate proteomics data from ruminants: application to mine proteome of adipose and muscle tissues in bovine foetuses. PLoS One 10, e0128086. https://doi.org/10.1371/journal.pone.0128086 (2015).
Article CAS PubMed PubMed Central Google Scholar
Radulovic, M. et al. Kallikrein cascades in traumatic spinal cord injury: In vitro evidence for roles in axonopathy and neuron degeneration. J. Neuropathol. Exp. Neurol. 72, 1072–1089. https://doi.org/10.1097/NEN.0000000000000007 (2013).
Article CAS PubMed Google Scholar
Bando, Y. et al. Kallikrein 6 secreted by oligodendrocytes regulates the progression of experimental autoimmune encephalomyelitis. Glia 66, 359–378. https://doi.org/10.1002/glia.23249 (2018).
Article PubMed Google Scholar
Clarke, D. J. B. et al. eXpression2Kinases (X2K) Web: Linking expression signatures to upstream cell signaling networks. Nucleic Acids Res. 46, W171–W179. https://doi.org/10.1093/nar/gky458 (2018).
Article CAS PubMed PubMed Central Google Scholar
Loh, C. H. & Veenstra, G. J. C. The role of polycomb proteins in cell lineage commitment and embryonic development. Epigenomes https://doi.org/10.3390/epigenomes6030023 (2022).
Article PubMed PubMed Central Google Scholar
He, D. et al. lncRNA functional networks in oligodendrocytes reveal stage-specific myelination control by an lncOL1/Suz12 complex in the CNS. Neuron 93, 362–378. https://doi.org/10.1016/j.neuron.2016.11.044 (2017).
Article CAS PubMed Google Scholar
Upadhyay, G., Chowdhury, A. H., Vaidyanathan, B., Kim, D. & Saleque, S. Antagonistic actions of Rcor proteins regulate LSD1 activity and cellular differentiation. Proc. Natl. Acad. Sci. U.S.A. 111, 8071–8076. https://doi.org/10.1073/pnas.1404292111 (2014).
Article ADS CAS PubMed PubMed Central Google Scholar
Kleven, M. D., Dlakic, M. & Lawrence, C. M. Characterization of a single b-type heme, FAD, and metal binding sites in the transmembrane domain of six-transmembrane epithelial antigen of the prostate (STEAP) family proteins. J. Biol. Chem. 290, 22558–22569. https://doi.org/10.1074/jbc.M115.664565 (2015).
Article CAS PubMed PubMed Central Google Scholar
Zhang, F. et al. Metalloreductase Steap3 coordinates the regulation of iron homeostasis and inflammatory responses. Haematologica 97, 1826–1835. https://doi.org/10.3324/haematol.2012.063974 (2012).
Article CAS PubMed PubMed Central Google Scholar
Howie, H. L. et al. Differences in Steap3 expression are a mechanism of genetic variation of RBC storage and oxidative damage in mice. Blood Adv. 3, 2272–2285. https://doi.org/10.1182/bloodadvances.2019000605 (2019).
Article CAS PubMed PubMed Central Google Scholar
Passer, B. J. et al. The p53-inducible TSAP6 gene product regulates apoptosis and the cell cycle and interacts with Nix and the Myt1 kinase. Proc. Natl. Acad. Sci. U.S.A. 100, 2284–2289. https://doi.org/10.1073/pnas.0530298100 (2003).
Article ADS CAS PubMed PubMed Central Google Scholar
Amzallag, N. et al. TSAP6 facilitates the secretion of translationally controlled tumor protein/histamine-releasing factor via a nonclassical pathway. J. Biol. Chem. 279, 46104–46112. https://doi.org/10.1074/jbc.M404850200 (2004).
Article CAS PubMed Google Scholar
Lespagnol, A. et al. Exosome secretion, including the DNA damage-induced p53-dependent secretory pathway, is severely compromised in TSAP6/Steap3-null mice. Cell Death Differ. 15, 1723–1733. https://doi.org/10.1038/cdd.2008.104 (2008).
Article CAS PubMed Google Scholar
Demine, S., Renard, P. & Arnould, T. Mitochondrial uncoupling: A key controller of biological processes in physiology and diseases. Cells https://doi.org/10.3390/cells8080795 (2019).
Article PubMed PubMed Central Google Scholar
Horowitz, M. P. & Greenamyre, J. T. Mitochondrial iron metabolism and its role in neurodegeneration. J. Alzheimers Dis. 20(Suppl 2), S551-568. https://doi.org/10.3233/JAD-2010-100354 (2010).
Article CAS PubMed PubMed Central Google Scholar
Pandey, S. et al. Disease-associated oligodendrocyte responses across neurodegenerative diseases. Cell Rep. 40, 111189. https://doi.org/10.1016/j.celrep.2022.111189 (2022).
Article CAS PubMed Google Scholar
Doerflinger, N. H., Macklin, W. B. & Popko, B. Inducible site-specific recombination in myelinating cells. Genesis 35, 63–72. https://doi.org/10.1002/gene.10154 (2003).
Article CAS PubMed Google Scholar
Weiss, T. et al. Schwann cell plasticity regulates neuroblastic tumor cell differentiation via epidermal growth factor-like protein 8. Nat. Commun. 12, 1624. https://doi.org/10.1038/s41467-021-21859-0 (2021).
Article ADS CAS PubMed PubMed Central Google Scholar
McFarlane, K. et al. Effect of sex on motor function, lesion size, and neuropathic pain after contusion spinal cord injury in mice. J. Neurotrauma 37, 1983–1990. https://doi.org/10.1089/neu.2019.6931 (2020).
Article PubMed PubMed Central Google Scholar
Luchetti, S. et al. Comparison of immunopathology and locomotor recovery in C57BL/6, BUB/BnJ, and NOD-SCID mice after contusion spinal cord injury. J. Neurotrauma 27, 411–421. https://doi.org/10.1089/neu.2009.0930 (2010).
Article PubMed PubMed Central Google Scholar
Funfschilling, U. et al. Glycolytic oligodendrocytes maintain myelin and long-term axonal integrity. Nature 485, 517–521. https://doi.org/10.1038/nature11007 (2012).
Article ADS CAS PubMed PubMed Central Google Scholar
Meyer, N. & Rinholm, J. E. Mitochondria in myelinating oligodendrocytes: Slow and out of breath?. Metabolites https://doi.org/10.3390/metabo11060359 (2021).
Article PubMed PubMed Central Google Scholar
Hill, C. E. A view from the ending: Axonal dieback and regeneration following SCI. Neurosci. Lett. 652, 11–24. https://doi.org/10.1016/j.neulet.2016.11.002 (2017).
Article CAS PubMed Google Scholar
Lasiene, J., Shupe, L., Perlmutter, S. & Horner, P. No evidence for chronic demyelination in spared axons after spinal cord injury in a mouse. J. Neurosci. 28, 3887–3896. https://doi.org/10.1523/JNEUROSCI.4756-07.2008 (2008).
Article CAS PubMed PubMed Central Google Scholar
Ohri, S. S. et al. Blocking autophagy in oligodendrocytes limits functional recovery after spinal cord injury. Neurosci. 38, 5900–5912. https://doi.org/10.1523/JNEUROSCI.0679-17.2018 (2018).
Ohri, S. S. et al. Attenuating the endoplasmic reticulum stress response improves functional recovery after spinal cord injury. Glia 59, 1489–1502. https://doi.org/10.1002/glia.21191 (2011).
Article PubMed PubMed Central Google Scholar
Bankston, A. N. et al. Autophagy is essential for oligodendrocyte differentiation, survival, and proper myelination. Glia 67, 1745–1759. https://doi.org/10.1002/glia.23646 (2019).
Article PubMed Google Scholar
Sanz, E., Bean, J. C., Carey, D. P., Quintana, A. & McKnight, G. S. RiboTag: Ribosomal tagging strategy to analyze cell-type-specific mRNA expression in vivo. Curr. Protocols Neurosci. 88, e77. https://doi.org/10.1002/cpns.77 (2019).
Article CAS Google Scholar
Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198. https://doi.org/10.1093/nar/gkz369 (2019).
Karp, P. D. et al. Pathway Tools version 23.0 update: Software for pathway/genome informatics and systems biology. Brief. Bioinform. 22, 109–126. https://doi.org/10.1093/bib/bbz104 (2021).
Article PubMed Google Scholar
Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947. https://doi.org/10.1523/JNEUROSCI.1860-14.2014 (2014).
Article CAS PubMed PubMed Central Google Scholar
Talbott, J. F. et al. Schwann cell-like differentiation by adult oligodendrocyte precursor cells following engraftment into the demyelinated spinal cord is BMP-dependent. Glia 54, 147–159. https://doi.org/10.1002/glia.20369 (2006).
Article PubMed PubMed Central Google Scholar

Download references

Acknowledgements

This work was supported by NS103433 and NS108529 (MH, SRW), Jewish Heritage Fund for Excellence Research Enhancement Grant (MH, SRW), Norton Healthcare and the Commonwealth of Kentucky Challenge for Excellence (MH, SRW). Sequencing and bioinformatics support for this work was provided by P20 GM103436 and P20 GM106396 (ECR). Excellent technical assistance was provided by Christine Armstrong, Russel Howard, and Jason Beare.

Author information

These authors contributed equally: Michael D. Forston and George Z. Wei.

Authors and Affiliations

Kentucky Spinal Cord Injury Research Center, University of Louisville School of Medicine, Louisville, KY, 40202, USA
Michael D. Forston, George Z. Wei, Tyler Stephenson, Kariena Andres, Charles Glover, Scott R. Whittemore & Michal Hetman
Department of Neurological Surgery, University of Louisville School of Medicine, Louisville, KY, 40202, USA
Tyler Stephenson, Kariena Andres, Charles Glover, Scott R. Whittemore & Michal Hetman
Department of Anatomical Sciences & Neurobiology, University of Louisville School of Medicine, Louisville, KY, 40202, USA
Michael D. Forston, Scott R. Whittemore & Michal Hetman
Department of Pharmacology & Toxicology, University of Louisville School of Medicine, Louisville, KY, 40202, USA
George Z. Wei, Scott R. Whittemore & Michal Hetman
MD/PhD Program, University of Louisville School of Medicine, Louisville, KY, 40202, USA
George Z. Wei, Scott R. Whittemore & Michal Hetman
Kentucky IDeA Networks of Biomedical Research Excellence (KY INBRE) Bioinformatics Core, University of Louisville, Louisville, KY, 40202, USA
Julia H. Chariker & Eric C. Rouchka
Neuroscience Training, University Louisville School of Medicine, Louisville, KY, 40202, USA
Julia H. Chariker
Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, 40202, USA
Eric C. Rouchka

Authors

Michael D. Forston
View author publications
You can also search for this author in PubMed Google Scholar
George Z. Wei
View author publications
You can also search for this author in PubMed Google Scholar
Julia H. Chariker
View author publications
You can also search for this author in PubMed Google Scholar
Tyler Stephenson
View author publications
You can also search for this author in PubMed Google Scholar
Kariena Andres
View author publications
You can also search for this author in PubMed Google Scholar
Charles Glover
View author publications
You can also search for this author in PubMed Google Scholar
Eric C. Rouchka
View author publications
You can also search for this author in PubMed Google Scholar
Scott R. Whittemore
View author publications
You can also search for this author in PubMed Google Scholar
Michal Hetman
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

M.D.F. and G.Z. W. designed and executed experiments, analyzed their results, and wrote the manuscript; T.S., K.A. and C.G. executed experiments and analyzed their results, J.H.C. and E.C.R. performed RNASeq data analysis, S.S.O. designed experiments and edited the manuscript, SRW and MH designed experiments, edited the manuscript and provided financial support.

Corresponding author

Correspondence to Michal Hetman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Information.

Supplementary Table S1.

Supplementary Table S2.

Supplementary Table S3.

Supplementary Table S4.

Supplementary Table S5.

Supplementary Table S6.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Forston, M.D., Wei, G.Z., Chariker, J.H. et al. Enhanced oxidative phosphorylation, re-organized intracellular signaling, and epigenetic de-silencing as revealed by oligodendrocyte translatome analysis after contusive spinal cord injury. Sci Rep 13, 21254 (2023). https://doi.org/10.1038/s41598-023-48425-6

Download citation

Received: 12 July 2023
Accepted: 27 November 2023
Published: 01 December 2023
DOI: https://doi.org/10.1038/s41598-023-48425-6

This article is cited by

STEAP3 promotes colon cancer cell proliferation and migration via regulating histone acetylation
- Jinjuan Lv
- Xiaoqian Liu
- Yunfei Zuo
Human Genetics (2024)

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.