The brain is the most cholesterol-rich organ, accounting for 23% of the total cholesterol in the body even though the brain represents only 2% of the total body weight (Dietschy and Turley, 2004). About 70–80% of the brain cholesterol resides in myelin, a compact multilayer membrane structure that is generated by oligodendrocytes in the central nervous system (CNS), and myelin is critical for rapid saltatory nerve conduction (Armati and Mathey, 2010). Myelin is a lipid-rich material, and cholesterol accounts for the highest molar percentage (~52%) among all myelin lipids (Chrast et al., 2011). Consistently, the rate of cholesterol biosynthesis in the mouse brain is highest during the first three weeks after birth (Dietschy and Turley, 2004; Quan et al., 2003), the same period in which the rate of myelinogenesis is highest (Armati and Mathey, 2010).

The brain depends extensively on de novo cholesterol biosynthesis, which is mainly carried out by oligodendrocytes and astrocytes, as the blood-brain barrier blocks the uptake of cholesterol from the circulation (Camargo et al., 2017; Saher et al., 2005). Genetic ablation of squalene synthase (Fdft1), an enzyme involved in the early step of cholesterol biosynthesis, in oligodendrocyte lineage cells has resulted in failure of proper myelination (Saher et al., 2005). The importance of cholesterol biosynthesis in myelination is also implicated in various neurological disorders accompanied by myelin defects. For example, hereditary diseases such as Smith–Lemli–Opitz syndrome (SLOS), desmosterolosis, and lathosterolosis are caused by mutations of the cholesterol biosynthesis genes encoding 7-dehydrocholesterol reductase (DHCR7), 24-dehydrocholesterol reductase (DHCR24), and sterol-C5-desaturase (SC5D), respectively (Kanungo et al., 2013; Nwokoro et al., 2001; Porter and Herman, 2011). In particular, SLOS patients present with cognitive defects and delayed motor and language development along with hypomyelination (Porter and Herman, 2011). Patients with desmosterolosis have defects in the corpus callosum, thinning of white matter, and seizures (Zerenturk et al., 2013). One of the major neurodevelopmental disorders, schizophrenia, usually develops in late adolescence or early adulthood when maturation of the brain, including steps such as myelin biogenesis, occurs (Le Hellard et al., 2010; Steen et al., 2017). White matter abnormalities often occur in schizophrenic patients in association with reduced lipid metabolism (Steen et al., 2017). Of note, among the 108 schizophrenia-associated genomic loci, one is on chromosome 22q13.2, which includes SREBF2 gene that encodes sterol regulatory element-binding protein 2 (SREBP2), the major transcription factor that regulates cholesterol biosynthesis (Horton et al., 2002; Le Hellard et al., 2010; Steen et al., 2017). Antipsychotic drugs are known to increase SREBP2 activity, resulting in upregulated expression of the genes involved in cholesterol biosynthesis (Fernø et al., 2005; Le Hellard et al., 2009), suggesting a potential role of SREBP2-mediated cholesterol biosynthesis in the pathogenesis of schizophrenia. Besides the neurological diseases accompanied by myelination defects, reduction in cholesterol biosynthesis is also associated with neurodegenerative diseases such as Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, and autism spectrum disorders, for which myelin involvement has been documented but less understood (Leoni and Caccia, 2014; Mohamed et al., 2018; Segatto et al., 2019; Tsunemi et al., 2012; Xiang et al., 2011). Taken together, cholesterol biosynthesis plays a pivotal role in brain function, particularly myelination, and dysregulated cholesterol metabolism causes various neurological diseases. Yet the underlying mechanisms of precise control of cholesterol biosynthesis in oligodendrocytes during developmental myelination remain elusive.

Mammalian Quaking (Qk) undergoes alternative splicing to express the RNA-binding proteins Qki-5, Qki-6, and Qki-7 (Darbelli and Richard, 2016). Various studies have extensively demonstrated that Qki regulates the RNA processing of the genes encoding myelin basic protein (MBP), myelin-associated glycoprotein (MAG), p27kip1, and neurofascin 155 in oligodendrocytes (Darbelli et al., 2016; Larocque et al., 2005; Larocque et al., 2002; Li et al., 2000; Zhao et al., 2010). The quaking viable (qk^v) mouse is a spontaneous recessive mutant with an approximate 1-Mbp deletion in the upstream of Qk locus, leading to diminished expression of Qki in oligodendrocytes (Ebersole et al., 1996; Hardy et al., 1996). Qk^v homozygotes suffer from tremor and early death due to severe hypomyelination in the CNS (Sidman et al., 1964). Previous studies showed that qk^v mice exhibited reduced myelin lipid content, including cholesterol (Baumann et al., 1968; Singh et al., 1971), and this phenomenon was thought to be secondary to impaired differentiation and maturation of oligodendrocytes (Chen et al., 2007; Darbelli et al., 2016; Larocque et al., 2005). However, similar numbers of oligodendrocytes in some regions of the CNS in qk^v mice and control mice and even hyperplasia in these regions in the former mice were observed (Doukhanine et al., 2010; Hardy et al., 1996; Myers et al., 2016). These contradictory data suggested that Qki might regulate myelination in the CNS through mechanisms besides controlling oligodendrocyte differentiation and that reduced myelin lipid in qk^v mice could be a direct consequence of Qk loss rather than secondary to impaired oligodendrocyte differentiation. Interestingly, our recent study demonstrated that Qki is not required for mature oligodendrocyte survival in adult mice, and that Qki-5 forms a complex with peroxisome proliferator-activated receptor beta (PPARβ)-retinoid X receptor alpha (RXRα) to transcriptionally control fatty acid metabolism, which is essential for mature myelin maintenance (Zhou et al., 2020). We therefore hypothesized that Qki might transcriptionally regulate lipid metabolism, such as cholesterol biosynthesis, during developmental myelination in young mice.

In the present study, using conditional Qk-knockout mice specifically lacking expression of Qki in either neural stem cells (NSCs) or oligodendrocyte precursor cells (OPCs) during the crucial myelin-forming period of postnatal brain development, we identified a critical, previously uncharacterized phenomenon that Qki-depleted NSCs and OPCs can still differentiate into Aspa⁺/Gstpi⁺ myelinating oligodendrocytes. However, these cells do not form myelin properly due to impaired cholesterol biosynthesis. We also found that Qki-5 interacted with Srebp2 and activated its transcriptional activity for the genes involved in cholesterol biosynthesis. Deletion of Qk almost completely abolished expression of various cholesterol biosynthesis genes in oligodendrocytes and reduced the cholesterol content in the corpus callosum tissues in Qk-depleted mice. Our study highlights a novel function of Qki as a co-activator of Srebp2 in developing brain beyond its canonical function as an RNA-binding protein.

Timely onset of oligodendrocyte myelination is essential for brain development (Armati and Mathey, 2010). Cholesterol is an important functional component of myelin formation, and deficiency in cholesterol biosynthesis is associated with various hypomyelinating diseases reported in human genetic studies (Nwokoro et al., 2001; Porter and Herman, 2011). Brain cholesterol production mainly depends on de novo synthesis to fulfill the high demand for cholesterol due to the restriction of cholesterol entry into the brain by the blood-brain barrier. However, how cholesterol biosynthesis is regulated in oligodendrocytes, the primary myelinating cells in the CNS, is not clear. The present study revealed that Qki functions as a novel transcriptional co-activator of Srebp2 in oligodendrocytes to ensure supply of cholesterol for proper developmental myelination in a timely manner (Figure 8).

Figure 8

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Previous studies showed that cholesterol biosynthesis is highly active during the early stage of brain development (Dietschy and Turley, 2004). However, the cell types primarily responsible for cholesterol biosynthesis in myelin formation were not clear. In the present study, we observed that Aspa and Gstpi were co-expressed with Srebp2, Fdps, and Hmgcs1 in oligodendrocytes, suggesting that Aspa⁺Gstpi⁺ cells represent a subset of myelinating oligodendrocytes with highly active cholesterol biosynthesis (Figure 5F, G). Aspa has been shown to be more abundantly expressed in myelinating oligodendrocytes than in premyelinating oligodendrocytes (Marques et al., 2016; Zhang et al., 2014). Additionally, Canavan disease caused by Aspa deficiency is accompanied by myelin deficiency with reduced level of myelin lipids, including cholesterol, as Aspa enzymatically produces acetate as a source of acetyl-CoA, a precursor for synthesis of cholesterol (Madhavarao et al., 2005). These lines of evidence, in combination with our observations, suggest that Aspa⁺Gstpi⁺ cells represent a subset of myelinating oligodendrocytes. In addition to oligodendrocytes, previous studies reported that cholesterol generated by astrocytes also contributes to myelination (Camargo et al., 2017). However, the uptake of cholesterol from astrocytes was not sufficient to compensate for the reduced cholesterol synthesis in oligodendrocytes with deletion of Scap (Camargo et al., 2017), Fdft1 (Saher et al., 2005), or Qk (current study), indicating that oligodendrocytes are the major cell types producing cholesterol for myelination during early development.

Previous studies showed that qk^v mice had reduced myelin lipid content, including cholesterol (Baumann et al., 1968; Singh et al., 1971). This phenomenon was previously thought to be secondary to loss of mature oligodendrocytes in qk^v mice during development. However, in the present study, we uncovered a previously uncharacterized function of Qki in controlling transcription of the genes involved in cholesterol biosynthesis without affecting the differentiation of Aspa⁺Gstpi⁺ myelinating oligodendrocytes. Further studies are needed to elucidate how Aspa⁺Gstpi⁺ oligodendrocytes specifically regulate cholesterol biosynthesis and how other cell types, including astrocytes, contribute to oligodendroglial myelination.

During the characterization of oligodendroglial lineage cell populations, we observed that the differentiation of Aspa⁺Gstpi⁺ myelinating oligodendrocytes was not impaired upon Qki depletion, but their cholesterol biosynthesis was severely defective. Previous studies showed that all three isoforms of Qki are essential for the differentiation and maturation of oligodendrocytes (Chen et al., 2007; Darbelli et al., 2016; Larocque et al., 2005). Specifically, the number of Olig2⁺ oligodendroglial lineage cells in QKI^FL/FL;^Olig2–Cre mice was about 50% lower than that in control mice (Darbelli et al., 2016). Therefore, we also checked whether oligodendroglial lineage cell populations other than Aspa⁺Gstpi⁺ myelinating oligodendrocytes were affected by Qki loss in our Qk-Nestin-iCKO mice. We found that the number of Olig2⁺ cells was reduced by 50.9% in Qk-Nestin-iCKO mice compared to that in control mice (Figure 2—figure supplement 1B), suggesting that Qki loss affects OPCs differentiation into Olig2⁺Aspa^-Gstpi^- oligodendroglial lineage cells, whose function is unclear. Collectively, we found that Qki plays variable roles in the differentiation of different subpopulations of oligodendrocyte lineage cells, leading to an intriguing question what determines the specific roles of Qki during oligodendrocyte differentiation and myelinogenesis, which needs to be further investigated.

Mammalian Quaking (Qk) undergoes alternative splicing to express the RNA-binding proteins Qki-5, Qki-6, and Qki-7 (Darbelli and Richard, 2016). In the current study, we showed that Qki-5 is required for transcriptional activation of Srebp2-mediated cholesterol biosynthesis in oligodendrocytes. Notably, we observed that expression of myelin proteins such as MBP, PLP, and MAG was greatly reduced upon Qki depletion (Figure 1F). Although we did not find these proteins to be direct transcriptional targets of Qki-5 and Srebp2, the stability and nuclear export of MBP mRNA may be regulated by Qki-6 and Qki-7 as shown in qk^v mice, where expression of Qki-6 and Qki-7 is predominantly depleted (Larocque et al., 2002; Li et al., 2000). Another nonmutually exclusive explanation of decreased expression of MBP is that the reduction in cholesterol level induced by Qki depletion potentially leads to destabilization of myelin because protein:lipid ratio is crucial for proper integration of proteins and lipids in myelin membrane (Aggarwal et al., 2011; Ozgen et al., 2016; Saher et al., 2005; Simons et al., 2000), which is shown by the defect in co-localization of different myelin proteins (Figure 3B). Although we showed that Qki-5 transcriptionally regulates cholesterol biosynthesis, it is still unclear if Qki-6 and Qki-7 also play a role in the enhancement of cholesterol biosynthesis pathway by regulating mRNA of cholesterol biosynthesis-related genes as we observed more significant decrease of these genes at the protein level compared to the mRNA level (Figure 5E–H). Therefore, further investigation is needed to determine the specific roles of different Qki isoforms during oligodendrocyte differentiation and myelinogenesis in early brain developmental stages.

Previous studies showed that cholesterol biosynthesis in oligodendrocytes and Schwann cells could be regulated by transcriptional regulators such as Tcf7l2, Chd8, and Maf (Kim et al., 2018; Zhao et al., 2016; Zhao et al., 2018). However, how Srebp2, the master transcription factor in cholesterol biosynthesis, is regulated in oligodendrocytes is unclear. In the present study, we demonstrated that Qki-5 functions as a co-activator of Srebp2 to enhance transcription of the genes involved in cholesterol biosynthesis in myelinating oligodendrocytes and that depletion of Qki leads to reduced promoter occupancy of Srebp2 and Pol II and decreased transcription of the genes involved in cholesterol biosynthesis (Figure 7). Further studies are needed to determine how Qki enhances transcriptional activity of Srebp2.

In this study, we found that Qki-5 functions as a co-activator of Srebp2 to regulate transcription of the genes involved in cholesterol biosynthesis, which is essential for proper myelinogenesis during development. Although cholesterol biosynthesis was the major downstream of Qki-5, we noticed that the molecular functions related to fatty acid metabolism, concentration of fatty acid, and synthesis of fatty acid were also markedly inhibited after Qki depletion in neonatal mice (Figure 5—figure supplement 1A). We assume that both disturbed cholesterol biosynthesis and fatty acid metabolism upon Qki depletion contributed to the hypomyelination and neurological deficits of Qk-iCKO mice during development (Figure 8). Interestingly, our recent study demonstrated that Qki-5 forms a complex with PPARβ-RXRα and coactivates transcription of the genes for fatty acid metabolism, which is essential for the maintenance of mature myelin homeostasis (Zhou et al., 2020; Figure 8). However, continuous biosynthesis of cholesterol may not be necessary to maintain the homeostasis of mature myelin, probably due to the very slow turnover of myelin cholesterol in adult rodents (Ando et al., 2003; Smith, 1968; Figure 8). It remains unclear what is the determinant factor of Qki-5 interaction and co-activation of Srebp2 or PPARβ-RXRα on the chromatin under different contexts including the brains from mice of different ages, and whether Qki-5 can interact with other transcription factors. Taken together, the universal occupancy of Qki-5 on chromatin greatly expands our understanding of Qki itself, as well as the biology of transcriptional control by an RNA-binding protein.

Notably, we observed that Qki was depleted in nearly all Pdgfrα⁺ OPCs (92.6%) in Qk-Nestin-iCKO mice (Figure 2A), which led to the question of how most of the OPCs were targeted under the control of Nestin-Cre after birth. Cortically derived OPCs are considered to make up about 80% of all OPCs in the cortex, which are called late-born OPCs because they arise as a third wave of OPCs initiating at birth and reaching maximum in number 2–4 weeks after birth in mice (van Tilborg et al., 2018). This population of OPCs is known to replace the OPCs originating at embryonic stages in the forebrain (Bergles and Richardson, 2015), suggesting an active generation of OPCs from NSCs during the first month after birth, potentially leading to highly efficient Qki depletion in NSC-derived Pdgfrα⁺ OPCs in our study. On the basis of these findings, it is not difficult to understand that although Qki was efficiently depleted in OPCs of Qk-Plp-iCKO mice at P4 and P5, an active generation of WT OPCs from WT NSCs diluted the OPC pool, leading to Qki depletion in only a subset of OPCs in Qk-Plp-iCKO mice at P18 (Figure 4I). Thus, although both Qk-Nestin-iCKO mice and Qk-Plp-iCKO mice showed dysmyelinating phenotype, Qk-Nestin-iCKO mice exhibited a more severe phenotype than did Qk-Plp-iCKO mice.

In this study, the median survival duration in Qk-Nestin-iCKO mice was 13 days after tamoxifen injection due to severe dysmyelination, yet in our previous study (Shingu et al., 2017), Qk-Nestin-iCKO mice could survive through adulthood. We believe that the difference in the survival times between these two cohorts of Qk-Nestin-iCKO was due to the background of the mice. In the present study, the mice were on a pure C57BL/6J background, whereas the mice in the previous study were on a mixed C57BL/6J and FVB background. OPCs have been shown to repopulate as a third wave of cortically arising OPCs from NSCs initiating at birth and reaching the maximum number around 2–4 weeks after birth (van Tilborg et al., 2018). However, the exact time point of this wave of OPC generation can be greatly influenced by the mouse background (van Tilborg et al., 2018), resulting in highly variable Qk deletion in OPCs with P7 injection of tam in Qk-Nestin-iCKO cohorts of different backgrounds.

Effort has been made to treat hypomyelinating disorders such as SLOS, Pelizaeus–Merzbacher disease, and Charcot–Marie–Tooth disease type 1A with dietary supplementation of lipids, including cholesterol (Fledrich et al., 2018; Saher et al., 2012; Tierney et al., 2010). However, challenges still remain, as cholesterol rarely crosses the blood-brain barrier, and long-term benefits of cholesterol supplementation have not been clearly examined (Tierney et al., 2010). Whether dietary supplementation of cholesterol can rescue hypomyelination in Qk-iCKO mice would be interesting to know. Of note, we previously demonstrated that Qki plays an important role in intracellular vesicle trafficking in NSCs (Shingu et al., 2017). If the same function of Qki exists in oligodendrocytes, cholesterol transport and uptake would be impaired due to defective intracellular vesicle trafficking in Qki-depleted oligodendrocytes, which may lead to inefficiencies in high-cholesterol diet-based therapy. The present findings will potentially provide understanding of brain-specific cholesterol biosynthesis and shed light on mechanism-based therapeutics for enhancing oligodendrocyte myelination. As QKI is one of the genes most associated with various neurological diseases (Darbelli and Richard, 2016), understanding the mechanisms underpinning Qki/Srebp2-mediated cholesterol biosynthesis and myelination will help identify tissue-specific therapeutic opportunities for neurological diseases associated with myelin defects (Nwokoro et al., 2001; Porter and Herman, 2011).

Sequencing data have been deposited in GEO under accession codes GSE145116, GSE145117 and GSE144756.

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Author details

1. Xin Zhou

   1. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, United States
   2. Cancer Research Institute of Jilin University, The First Hospital of Jilin University, Changchun, Jilin, China

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Seula Shin and Chenxi He

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9362-8272

2. Seula Shin

   1. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, United States
   2. Cancer Biology Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Xin Zhou and Chenxi He

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3593-5901

3. Chenxi He

   Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China

   Contribution

   Formal analysis, Visualization, Methodology, Writing - review and editing

   Contributed equally with

   Xin Zhou and Seula Shin

   Competing interests

   No competing interests declared

4. Qiang Zhang

   Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

5. Matthew N Rasband

   Department of Neuroscience, Baylor College of Medicine, Houston, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8184-2477

6. Jiangong Ren

   Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

7. Congxin Dai

   1. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, United States
   2. Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China

   Contribution

   Resources

   Competing interests

   No competing interests declared

8. Rocío I Zorrilla-Veloz

   1. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, United States
   2. Cancer Biology Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, United States

   Contribution

   Diagram illustration

   Competing interests

   No competing interests declared

9. Takashi Shingu

   Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

10. Liang Yuan

    1. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, United States
    2. Graduate School of Biomedical Sciences, Tufts University, Boston, United States

    Contribution

    Genotyping

    Competing interests

    No competing interests declared

11. Yunfei Wang

    Clinical Science Division, H. Lee Moffitt Cancer Center & Research Institute, Tampa, United States

    Contribution

    Software, Formal analysis

    Competing interests

    No competing interests declared

12. Yiwen Chen

    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, United States

    Contribution

    Resources, Formal analysis, Writing - review and editing

    Competing interests

    No competing interests declared

13. Fei Lan

    Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, and Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China

    Contribution

    Resources, Formal analysis, Writing - review and editing

    Competing interests

    No competing interests declared

14. Jian Hu

    1. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, United States
    2. Cancer Biology Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, United States
    3. Neuroscience Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Writing - review and editing

    For correspondence

    JHu3@mdanderson.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9760-2013

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