STAG2 promotes the myelination transcriptional program in oligodendrocytes

  1. Ningyan Cheng
  2. Guanchen Li
  3. Mohammed Kanchwala
  4. Bret M Evers
  5. Chao Xing
  6. Hongtao Yu  Is a corresponding author
  1. Department of Pharmacology, University of Texas Southwestern Medical Center, United States
  2. Westlake Laboratory of Life Sciences and Biomedicine, China
  3. School of Life Sciences, Westlake University, China
  4. Institute of Biology, Westlake Institute for Advanced Study, China
  5. Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, United States
  6. Division of Neuropathology, University of Texas Southwestern Medical Center, United States
  7. Department of Bioinformatics, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, United States

Abstract

Cohesin folds chromosomes via DNA loop extrusion. Cohesin-mediated chromosome loops regulate transcription by shaping long-range enhancer–promoter interactions, among other mechanisms. Mutations of cohesin subunits and regulators cause human developmental diseases termed cohesinopathy. Vertebrate cohesin consists of SMC1, SMC3, RAD21, and either STAG1 or STAG2. To probe the physiological functions of cohesin, we created conditional knockout (cKO) mice with Stag2 deleted in the nervous system. Stag2 cKO mice exhibit growth retardation, neurological defects, and premature death, in part due to insufficient myelination of nerve fibers. Stag2 cKO oligodendrocytes exhibit delayed maturation and downregulation of myelination-related genes. Stag2 loss reduces promoter-anchored loops at downregulated genes in oligodendrocytes. Thus, STAG2-cohesin generates promoter-anchored loops at myelination-promoting genes to facilitate their transcription. Our study implicates defective myelination as a contributing factor to cohesinopathy and establishes oligodendrocytes as a relevant cell type to explore the mechanisms by which cohesin regulates transcription.

Editor's evaluation

This manuscript will be of interest to scientists working on genome organisation and transcriptional control of myelination during mammalian brain development. The authors combine diverse and complementary experimental approaches to generate insights into how DNA looping contributes to transcriptional regulation in functionally specialised cell types. The experiments have been rigorously performed and the main conclusions are justified.

https://doi.org/10.7554/eLife.77848.sa0

Introduction

Chromosomes in a single human diploid cell, if linearly stitched together, span a length of more than 2 m. They need to be properly folded to be housed in the cell nucleus with a diameter of 10 µm. Chromosome folding occurs in a dynamic, structured way that regulates gene expression, and DNA replication and repair. Initially discovered as the molecular glue that tethers sister chromatids for segregation during mitosis (Haarhuis et al., 2014; Uhlmann, 2016; Yatskevich et al., 2019; Zheng and Yu, 2015), the cohesin complex has later been shown to be critical for structured chromosome folding and gene expression (Haarhuis et al., 2017; Rao et al., 2017; Schwarzer et al., 2017; Wutz et al., 2017).

Cohesin is loaded on chromosomes by the cohesin loader NIPBL. The cohesin–NIPBL complex can extrude DNA loops bidirectionally in an ATP-dependent manner (Davidson et al., 2019; Kim et al., 2019; Vian et al., 2018). The chromatin insulator CTCF has been proposed to block loop extrusion by cohesin, establishing topologically associated domains (TADs) and marking TAD boundaries. Chromatin interactions within each TAD are favored whereas inter-TAD interactions are disfavored. Thus, chromosome loops and TADs shape long-range cis–element interactions, such as promoter–enhancer interactions, thereby regulating transcription.

The vertebrate cohesin complex contains four core subunits: the SMC1–SMC3 heterodimeric ATPase, the kleisin subunit RAD21 that links the ATPase heads, and the HEAT-repeat protein STAG1 or STAG2. STAG1 and STAG2 bind to RAD21 in a mutually exclusive manner and create docking sites for several regulatory proteins, including CTCF (Hara et al., 2014; Li et al., 2020). STAG1 and STAG2 also interact with DNA and the SMC1–SMC3 hinge domains (Shi et al., 2020). STAG1 and STAG2 play redundant roles in sister-chromatid cohesion in cultured human cells, as both need to be simultaneously depleted to produce overt cohesion defects (Hara et al., 2014).

Mutations of NIPBL and cohesin subunits, including STAG2, result in human developmental diseases termed cohesinopathies, which affect multiple organs and systems (Remeseiro et al., 2013b; Soardi et al., 2017). In patients with cohesinopathies, mental retardation and neurological abnormalities caused by brain development defects are common (Piché et al., 2019). Dysregulation of gene transcription as a result of reduced cohesin functions has been suggested to underlie these developmental defects (De Koninck and Losada, 2016; Remeseiro et al., 2013a). In addition, several cohesin genes, including STAG2, are frequently mutated in a variety of human cancers (Martincorena and Campbell, 2015).

In this study, we deleted Stag2 specifically in the nervous system in the mouse. The Stag2 cKO mice exhibited deficient myelination. Loss of STAG2 delayed the maturation of oligodendrocytes and reduced chromosome loops in oligodendrocytes and impaired the transcription of myelination-related genes. Our findings establish the requirement for cohesin in proper gene expression in specific cell types and implicate defective myelination as a potential contributing factor to cohesinopathy.

Results

Stag2 ablation in the nervous system causes growth retardation and neurological defects

Stag1 is required for mammalian embryonic development (Remeseiro et al., 2012), indicating that Stag2 cannot compensate for the loss of Stag1. To examine the physiological functions of Stag2 in the mouse, we created a Stag2 ‘floxed’ mouse line (Stag2f/f) by homologous recombination with a template that contained two LoxP sites flanking exon 8 (Figure 1A, B) and targeted a critical exon (exon 8) of Stag2, which is located on the X chromosome, using CRISPR–Cas9 (Figure 1—figure supplement 1A). The Stag2null embryos showed severe developmental defects and underwent necrosis by E11.5 days (Figure 1—figure supplement 1B). Thus, Stag2 is required for mouse embryonic development, consistent with a previous report (De Koninck et al., 2020). Stag1 and Stag2 have nonredundant developmental functions.

Figure 1 with 2 supplements see all
Stag2 ablation in the mouse nervous system causes growth retardation and neurological defects.

(A) Scheme for creating the ‘floxed’ Stag2 allele by gene targeting. The genomic structure of the wild-type (WT) Stag2 locus, the targeting vector, the knockin allele, the disrupted allele after Cre-mediated recombination, and the positions of the genotyping primers are shown. The amino acid sequence of the knockout allele in the targeted region is shown and aligned with that of the WT allele. (B) PCR analysis of the genomic DNA extracted from the tails of indicated mice with the primers in (A). (C) PCR analysis of genomic DNA extracted from brains (B) or livers (L) of indicated mice. (D) Immunoblots of brain lysates of Stag2f/y and Stag2f/y;NesCre mice with antibodies recognizing cohesin subunits and TUBULIN (as the loading control). (E) Representative images of Stag2f/y and Stag2f/y;NesCre mice. Scale bar = 2 cm. (F) Body weight of Stag2f/y and Stag2f/y;NesCre mice at different age. Mean ± standard deviation (SD) of at least three mice of the same age. (G) Survival curves of Stag2f/y (n = 12) and Stag2f/y;NesCre (n = 21) mice. Food (H) and water (I) consumption of 7- to 8-week-old Stag2f/y (n = 6) and Stag2f/y;NesCre (n = 4) mice. Mean ± SD; ns, not significant. (J) Plasma IGF-1 levels of 2-month-old Stag2f/y (n = 5) and Stag2f/y;NesCre (n = 6) mice. Mean ± SD; ****p < 0.0001. (K) Representative images of limb-clasping responses of Stag2f/y and Stag2f/y;NesCre mice. The uncropped images of blots in (B–D) are included in Figure 1—source data 1.

To study the functions of STAG2 in adult mice, we crossed the Stag2f/f mice with mice bearing the Rosa26CreErt2 genomic insertion and generated Stag2f/y;Rosa26CreErt2 progenies. The Stag2f/y;Rosa26CreErt2 adult mice were injected with tamoxifen to induce Stag2 deletion in the whole body (Figure 1—figure supplement 2A). Genotyping analysis of blood extracts showed that tamoxifen-induced efficient disruption of the Stag2 gene locus in Stag2f/y;Rosa26CreErt2 mice (Figure 1—figure supplement 2B, C). These Stag2-deficient adult mice did not show early onset of spontaneous tumor formation, indicating that Stag2 mutation alone in somatic cells of mice is insufficient to induce tumorigenesis. The Stag2-deficient mice also did not have other obvious adverse phenotypes (Figure 1—figure supplement 2D), except that they had slightly lower body weight (Figure 1—figure supplement 2E, F), probably due to tissue homeostasis alterations reported by others (De Koninck et al., 2020).

STAG2 mutations are found in human cohesinopathy patients with mental retardation and neuropsychiatric behaviors (Soardi et al., 2017). To study the function of STAG2 in the nervous system, we generated Stag2 conditional knockout mice (Stag2 cKO) by crossing Stag2f/f mice with Nestin-Cre mice (Giusti et al., 2014; Figure 1C, D). The progenies were born in the Mendelian ratio, but Stag2f/y;NesCre pups presented growth retardation and premature death (Figure 1E, G). More than 50% Stag2f/y;NesCre mice died aged about 3 weeks while the rest died at about 4 months. Stag2f/y mice did not show differences discernible from wild-type (WT) littermates. Although Stag2f/y;NesCre mice did not present microcephaly, they exhibited frequent hydrocephaly that might contribute to their premature death. The Stag2f/y;NesCre mice displayed normal drinking and feeding behaviors (Figure 1H), but showed reduced plasma IGF-1 levels compared to the control mice (Figure 1J). Stag2f/y;NesCre mice showed forepaw and hindlimb clasping (Figure 1K) and limb tremors (Video 1), which were not seen in Stag2f/y mice. These data indicate that Stag2 deficiency in the nervous system causes growth retardation and neurological defects.

Video 1
Neurological defects of brain-specific Stag2 KO mice.

Stag2 ablation causes hypomyelination

Hematoxylin and eosin (H&E) staining of brain sections of Stag2f/y;NesCre mice did not reveal overt anatomical defects (Figure 2—figure supplement 1A). As revealed by immunohistochemistry assays using neuron- or astrocyte-specific antibodies, the differentiation of neurons and astrocytes in Stag2-deleted brains was largely normal (Figure 2—figure supplement 1B–E). To understand the origins of neurological defects caused by Stag2 deletion, we analyzed the gene expression changes in Stag2f/y;NesCre mouse brains at post-natal day 21 by RNA-sequencing (RNA-seq) (Figure 2A). Compared with the control groups, 105 and 62 genes were significantly down- or upregulated by more than twofolds, respectively, in the Stag2-deficient brains. The decreased expression of top differentially expressed genes (DEGs) was confirmed by reverse transcription quantitative PCR (RT-qPCR) (Figure 2B). Among the 105 downregulated DEGs in the brains of Stag2 cKO mice, 44 were enriched in myelin (Figure 2C; Thakurela et al., 2016). The ingenuity pathway analysis (IPA) pinpoints cholesterol biosynthesis pathways as the most affected canonical pathways (Figure 2D and Supplementary file 1). We further confirmed that the cholesterol biosynthesis precursors were reduced in Stag2f/y;NesCre brains (Figure 2—figure supplement 1F).

Figure 2 with 2 supplements see all
Stag2 ablation in mouse brains downregulates the expression of myelin genes.

(A) Volcano plot of bulk RNA-sequencing results of Stag2f/y and Stag2f/y;NesCre brain extracts. Top differentially expressed genes (DEGs) are colored blue and labeled. n = 4 pairs of P21 Stag2f/y and Stag2f/y;NesCre brain hemispheres were used for the comparison. (B) Reverse transcription quantitative PCR (RT-qPCR) analysis of the top downregulated genes in the brain extracts. n = 4 pairs of Stag2f/y and Stag2f/y;NesCre littermates were used. Mean ± standard deviation (SD). (C) Heatmap of the expression of myelin-enriched genes that were downregulated by more than twofolds in Stag2f/y;NesCre brains. L1 and R1, left and right brain hemispheres of the Stag2f/y#1 mouse. L2 and R2, left and right brain hemispheres of the Stag2f/y#2 mouse. L1’ and R1’, left and right brain hemispheres of the Stag2f/y;NesCre #1 mouse. L2’ and R2’, left and right brain hemispheres of the Stag2f/y;NesCre#2 mouse. The biological pathways of these genes are labeled on the right. (D) The top 5 canonical pathways identified by ingenuity pathway analysis (IPA) of the DEGs. The complete gene list is used as the background.

Myelin is the membrane sheath that wraps around axons to facilitate rapid nerve conduction and maintain metabolic supply (Williamson and Lyons, 2018). Dynamic myelination in the central nervous system (CNS) is critical for proper neurodevelopment, and defective myelination is associated with autoimmune and neurodegenerative diseases (Mathys et al., 2019; Wolf et al., 2021). Cholesterol biosynthesis is essential for normal myelination (Hubler et al., 2018; Saher et al., 2005). Ensheathment of neurons and gliogenesis were among the top enriched biological pathways in downregulated DEGs (Figure 2—figure supplement 2). The innate immune response was among the top enriched pathways in the upregulated DEGs. We hypothesized that depletion of STAG2 caused myelination defects in the nervous system.

Indeed, brain sections of Stag2f/y;NesCre mice showed greatly reduced luxol fast blue (LFB) staining compared to those of Stag2f/y and NesCre heterozygous mice at about P21 (Figure 3A and Figure 3—figure supplement 1A). Immunohistochemistry using antibodies against myelin proteins, Myelin basic protein (MBP) and Proteolipid protein 1 (PLP1), confirmed that Stag2 cKO mice had significant defects in myelin fiber formation at P18-P21 (Figure 3B–F). In both cerebral cortex and cerebellum, there were fewer and sparser myelin fibers in Stag2f/y;NesCre mice, as compared to the Stag2f/y controls. Axon myelin ensheathment was further examined using transmission electron microscopy (Figure 3G). Stag2f/y;NesCre mice at P18 had significantly fewer myelin-wrapped axons at optic nerves. Collectively, these data indicate insufficient myelination in the Stag2 cKO mice. Myelination predominantly occurs at 3 weeks after birth in the mouse. The timing of premature death of Stag2 cKO mice is thus consistent with defective myelination as a contributing factor to the lethality.

Figure 3 with 1 supplement see all
Stag2 ablation in the nervous system compromises myelination during early postnatal development.

(A) Luxol fast blue staining of the sagittal sections of Stag2f/y and Stag2f/y;NesCre brains. n = 3 animals per genotype. Scale bar = 1 mm. (B) Immunohistochemistry staining with the anti-MBP antibody in the cerebral cortex (left panel). Antibody-stained areas and DAPI staining regions are marked with red and yellow dashed lines, respectively. Scale bar = 200 μm. Quantification of the percentage of the myelinated cortex is shown in the right panel. n = 4 pairs of Stag2f/y and Stag2f/y;NesCre littermates were used (P18 or P21) for the comparison. **p < 0.01; mean ± standard deviation (SD). (C) Immunohistochemistry staining with the anti-PLP1 antibody in the cerebellum (left panel). Antibody-stained areas and DAPI staining regions are marked with red and yellow dashed lines, respectively. Scale bar = 200 μm. Quantification of the percentage of the myelinated cerebellum granular layer is shown in the right panel. n = 3 pairs of Stag2f/y and Stag2f/y;NesCre littermates were used (P20 or P25) for the comparison. *p < 0.05; mean ± SD. (D) Higher magnification images (left panel) of the immunohistochemistry staining with the anti-MBP antibody in (B). Images processed through axial thinning are shown in the right panel. Scale bar = 50 μm. Total fiber length (E) and fiber coherency (F) measured using the processed images in (D). n = 4 pairs of Stag2f/y and Stag2f/y;NesCre littermates were used (P18 or P21). *p < 0.05, **p < 0.01; mean ± SD. (G) Transmission electron microscopy images of the optic nerves (left panel). Scale bar = 2 μm. Quantification of myelinated axon distributions is shown in the right panel. n = 4 pairs of P18 Stag2f/y and Stag2f/y;NesCre littermates were used. n ≥ 10 fields of each mouse were taken, and the average distribution of myelinated axons were calculated for each mouse and plotted. **p < 0.01; mean ± SD.

We examined Stag1 and Stag2 expression patterns in P18 WT mouse brains by in situ hybridization using isotope-labeled RNA probes (Figure 3—figure supplement 1B). Both Stag1 and Stag2 were expressed at high levels in hippocampus, medial habenula, neocortex, and cerebellum granular layer. Aside from these regions, the Stag2 transcripts were detected at relatively high levels in subventricular zone, thalamus, fiber tracts, midbrain, and hindbrain regions. Stag2 is thus ubiquitously expressed in the brain.

STAG2 regulates transcription in OLs

Oligodendrocytes (OLs) are responsible for myelination in the CNS. To examine whether the OL lineage was affected by Stag2 deletion, we performed single-cell RNA-sequencing (scRNA-seq) analysis of Stag2f/y;NesCre and Stag2f/y forebrains at P13. As revealed by clusters in the t-SNE plot, the two genotype groups had similar cellular compositions (Figure 4A, B). All cell clusters were present in Stag2f/y;NesCre brains, again indicating generally normal neural cell differentiation. Cell-type identities were discovered with feature gene expression (Figure 4—figure supplement 1A). Based on the expression changes of Stag2 and other cohesin genes in OLs, astrocytes, and neuronal lineages (Figure 4—figure supplement 1B–D), it is clear that Stag2 ablation occurred early in the NPC stage and was maintained in all differentiated cell lineages.

Figure 4 with 6 supplements see all
Deletion of Stag2 in mouse brains causes differentiation delay and transcriptional changes in oligodendrocytes.

(A) t-SNE plot of cell clusters in Stag2f/y and Stag2f/y;NesCre forebrains analyzed by single-cell RNA-sequencing (scRNA-seq). n = 2 mice of each genotype were used in the scRNA-seq analysis. aNSCs/NPCs, active neural stem cells or neural progenitor cells; Astrocytes/qNSCs, astrocytes or quiescent neural stem cells; OPCcycs, cycling oligodendrocyte (OL) progenitor cells; OPCs, OL progenitor cells; NFOLs, newly formed OLs; mFOLs, myelin-forming OLs; MOLs, matured OLs; VLMCs, vascular and leptomeningeal cells; vSMCs, vascular smooth muscle cells. (B) t-SNE clustering as in (A) but colored by genotype. (C) Left panel: cell-type composition and percentage as colored in (A). Right panel: percentage of cell clusters of the oligodendrocyte lineage. (D) FeaturePlot of a representative gene (Mal) specifically suppressed in MOLs of Stag2f/y;NesCre forebrains. A maximum cutoff of 3 was used. (E) Experimental scheme of the magnetic-activated cell sorting (MACS) of primary OLs. (F) Volcano plot of bulk RNA-sequencing (RNA-seq) results of Stag2f/y and Stag2f/y;NesCre primary OLs. (G) The top 5 canonical pathways identified by ingenuity pathway analysis (IPA) of the differentially expressed genes (DEGs) with more than twofold change in (F). The complete gene list is used as the background. (H) Commons DEGs shared between bulk RNA-seq analyses of the whole brains (WB) and primary OLs.

The OL lineage consisted of five clusters: cycling OL progenitors (OPCcycs), OL progenitors (OPCs), newly formed OLs (NFOLs), myelin-forming OLs (mFOLs), and fully matured OLs (MOLs). Quantification of the distributions of these five cell types within the OL lineage revealed a mild reduction in the proportion of MOLs in Stag2f/y forebrains (Figure 4C). We noticed that a higher percentage of neurons was recovered in the Stag2f/y;NesCre group. Since the bulk RNA-seq results did not show global upregulation of neuron-specific genes, we suspect that neurons in Stag2f/y;NesCre had fewer myelin-wrapped axons and were easier to be dissociated and kept alive during our library preparation for scRNA-seq. Thus, from the transcriptome analysis, we did not observe overt defects in most neural cell differentiation in the Stag2-deficient forebrain regions.

We then performed trajectory inference and pseudotime analysis of the OL lineage (Figure 4—figure supplement 2A, B). Consistent with our cell-type assignment, pseudotime variables indicated continuous differentiation from OPCs to NFOLs, mFOLs, and MOLs (Figure 4—figure supplement 2C,D). The reclustering of single cells in the OL lineage along the pseudotime path revealed that more cells were present in the terminal maturation stages in the Stag2f/y brains (Figure 4—figure supplement 2E, F). Conversely, more cells were retained at the undifferentiated stages in the Stag2f/y;NesCre brains. Strikingly, some myelination genes, including Myelin and lymphocyte protein (Mal), were specifically repressed in Stag2f/y;NesCre MOLs, with their expression in nonneural cells unaltered (Figure 4D and Figure 4—figure supplement 3A). These observations suggest that STAG2 deficiency delays the maturation of OLs and compromises myelination-specific gene expression in mature OLs. Interestingly, compared to Stag2 and genes encoding other cohesin core subunits, Stag1 transcripts are less abundant in the OL lineage, except for cycling OPCs (Figure 4—figure supplement 3B, C). The low expression of Stag1 in mature OLs might make these cells more dependent on Stag2 for function.

To confirm the transcriptional defects in the OL lineage caused by Stag2 deletion, we isolated primary OLs at intermediate differentiation stages from Stag2f/y;NesCre and Stag2f/y forebrains at P12-P14 with antibody-conjugated magnetic beads and conducted bulk RNA-seq analysis (Figure 4E). For both genotypes, the marker genes for NFOL and mFOLs were highly expressed in the isolated primary OLs (Figure 4—figure supplement 4A), suggesting that they mainly contained these two cell types. In Stag2-deleted OLs, 271 and 292 genes were downregulated or upregulated by more than two folds, respectively (Figure 4F and Supplementary file 2). Intriguingly, the downregulated genes were generally highly expressed in WT cells, whereas the upregulated genes had low expression levels in WT cells (Figure 4—figure supplement 4B–D). The top pathways enriched in the downregulated DEGs included the cholesterol and small molecule biosynthetic pathways and oligodendrocyte differentiation (Figure 4G and Figure 4—figure supplement 5). Cilium organization and assembly are the top enriched pathways in the upregulated DEGs (Figure 4—figure supplement 6). Among the 105 downregulated DEGs identified by RNA-seq analysis of the whole brain of Stag2-deficient mice, 42 were also differentially expressed in primary oligodendrocytes (Figure 4H). The cholesterol biosynthetic pathways were recognized as the major altered pathways (Figure 4—figure supplement 4E). Thus, defective cholesterol biosynthesis and oligodendrocyte differentiation likely underly hypomyelination and neurological defects in Stag2 cKO mice.

We performed chromatin immunoprecipitation sequencing (ChIP-seq) experiments to examine the enrichment of the active transcription mark H3K27ac in Stag2f/y and Stag2f/y;NesCre OLs and found that Stag2 loss did not appreciably affect H3K27Ac enrichment at transcription start sites (TSSs) (Figure 5A, B). Consistent with our RNA-seq results, the upregulated genes had much lower H3K27ac enrichment near their TSS, indicating that they were less active. We then checked the genomic distribution of STAG2 by ChIP-seq. Among other genomic loci, STAG2 was enriched at TSS of stable and downregulated genes, including genes in the cholesterol biosynthesis and myelination pathways (Figure 5C, D, Figure 5—figure supplement 1, and Supplementary file 2). Among the 271 downregulated DEGs, there were 210 genes (77%) with STAG2 enrichment near the transcriptional start site (TSS ± 2 kb). Thus, STAG2 occupied the promoter regions of many downregulated DEGs in oligodendrocytes. It was enriched at the TSS of upregulated genes to a lesser extent, with only 117 of the 292 (40%) upregulated DEGs exhibiting STAG2 ChIP-seq peaks at their TSS ± 2 kb regions. Stag2 loss might have indirectly affected the expression of these less active genes.

Figure 5 with 1 supplement see all
Enrichment of STAG2 and histone modifications at gene promoters.

(A) Heatmap of H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) signal enrichment in the promoter regions of genes in the indicated categories. (B) Density profile of H3K27ac ChIP-seq signal enrichment in the promoter regions of genes in the indicated categories as in (A). (C) Density profile of STAG2 ChIP-seq signal enrichment in the promoter regions of genes in the indicated categories as in (A). (D) Binding of STAG2 at the genomic loci of downregulated genes that encode cholesterol biosynthetic enzymes as revealed by ChIP-seq.

Stag2 deletion does not alter compartments or TADs in OLs

To investigate whether chromosome conformation was altered by Stag2 deletion and whether that caused transcription dysregulation, we performed high-dimensional chromosome conformational capture (Hi-C) analysis of primary OLs isolated from Stag2f/y and Stag2f/y;NesCre mice in biological replicates (Figure 6 and Figure 6—figure supplement 1). We observed few compartment switching events in Stag2-deleted cells (Figure 6A–C). Virtually all genomic regions in Stag2-deleted cells were kept in their original compartment categories (AA or BB) (Figure 6C). Only a very small number of genomic regions switched compartments (AB or BA). Consistent with the RNA-seq data, analysis of average gene expression changes of DEGs in these genomic regions revealed that more genes located in the transcriptionally active A compartment (AA) were repressed in Stag2-deleted cells and more genes in the transcriptionally silent B compartment (BB) were upregulated (Figure 6D and Figure 6—figure supplement 1C). Genes that switched from the A compartment to the B compartment were not more repressed compared to those that remained in the A compartment. Likewise, compared to genes that stayed in the B compartment, genes located in chromatin regions that switched from compartment B to A were not significantly activated. Acute depletion of all forms of cohesin eliminates TAD formation (Wutz et al., 2017). In contrast, deletion of Stag2 had minimal impact on TAD formation in oligodendrocytes (Figure 6E–G and Figure 6—figure supplement 1D), suggesting that STAG1-cohesin compensates for the loss of STAG2-cohesin in spatial organization of chromatin at larger than megabase scales. Therefore, our analyses did not uncover evidence for compartment switching and TAD alterations being the underlying cause for the observed gene expression changes in STAG2-deficient OLs.

Figure 6 with 1 supplement see all
Loss of Stag2 does not alter compartments and topologically associated domains (TADs) in oligodendrocytes.

(A) Representative snapshots of balanced Hi-C contact matrices of chromosome 2. Tracks of eigenvector-1 fixed with housekeeping genes are shown below, with A and B compartments shown in red and blue, respectively. (B) Hexbin plot of eigenvector-1 for genomic bins (100 kb) in Stag2f/y and Stag2f/y;NesCre oligodendrocytes (OLs). (C) Chromatin bins were classified into four categories based on the eigenvector sign and whether it has switched with a delta bigger than 1.5. AB, changing from compartment A in Stag2f/y to compartment B in Stag2f/y;NesCre; BA, from B in Stag2f/y to A in Stag2f/y;NesCre; AA, A in both Stag2f/y and Stag2f/y;NesCre; BB, B in both Stag2f/y and Stag2f/y;NesCre. (D) Boxplot of averaged gene expression change of differentially expressed genes (DEGs) (RNA logFC cutoff of ±0.58) inside each genomic bin. Bins counted: AA, 1646; AB, 56; BA, 69; BB, 910. Red dots represent the mean value. An unpaired Wilcoxon test was used for the statistical analysis. **p < 0.01; ***p < 0.001; ns, not significant. Principal component (E) and similarity (F) analysis performed using the insulation score at 10 kb resolution. (G) Aggregate TAD analysis on the 10 kb merged Hi-C matrices using TADs called from the merged samples of Stag2f/y at 10 kb resolution.

Promoter-anchored loops were reduced in Stag2-deleted OLs

While TAD boundaries are largely conserved among species and cell types, chromatin interactions within each TAD are more flexible and variable in cells undergoing differentiation, tumorigenesis, and reprogramming (Dixon et al., 2015; Dixon et al., 2012). Among the intra-TAD chromatin interactions, the enhancer–promoter loops are particularly important for transcription and are often cell-type specific. We examined whether chromatin loops in OLs were affected by Stag2 loss. Compared to Stag2f/y OLs, Stag2f/y;NesCre OLs had significantly fewer loops across almost all genomic distances (Figure 7A, B and Figure 7—figure supplement 1). The common and genotype-specific loops are reproducible in each replicate. Loops specific to Stag2f/y;NesCre OLs, which were likely mediated by STAG1-cohesin, were longer than STAG2-dependent Stag2f/y-specific loops. When genomic distances exceeded 0.25 Mb, the loops from Stag2f/y;NesCre cells gradually gained higher scores over loops from Stag2f/y cells (Figure 7C). Therefore, STAG1-cohesin cannot completely compensate for STAG2-cohesin during loop formation. STAG1-cohesin-mediated loops are relatively longer than STAG2-cohesin-mediated loops, consistent with published findings in HeLa cells (Wutz et al., 2020).

Figure 7 with 3 supplements see all
Stag2 deletion impairs the formation of total and promoter-anchored loops in oligodendrocytes.

(A) Loop counts (left panel) and length (right panel) in the indicated categories of Stag2f/y and Stag2f/y;NesCre oligodendrocytes (OLs). ***p < 0.001. (B) Loop counts plotted against loop length (from 0 to 5 Mb) of Stag2f/y and Stag2f/y;NesCre OLs. (C) Normalized contact counts for loops across different genomic distances in Stag2f/y and Stag2f/y;NesCre OLs. (D) Representative snapshots of contact maps at the Mal gene locus.hic files generated by HiC-Pro were converted to.cool format for plotting at 5 kb resolution. Tracks and narrow peaks from STAG2 and H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) as well as the loops are plotted below. Transcription direction is indicated by the black arrow. (E) Pile-up analysis of loop ‘dots’-centered local maps for the promoter-anchored loops of genes in the indicated categories. The maps are balanced, normalized by distance, and plotted at 5 kb resolution. The numbers indicate the enrichment of the central pixel over the upper left and bottom right corners. (F) Pile-up analysis of the local contact maps centered around the transcription start site (TSS) of genes in the indicated categories. Transcription directions are indicated below. 1000 stable genes are chosen randomly and used for the analysis. The maps are balanced, normalized by distance, and plotted at 5 kb resolution. Diagonal pixels are omitted.

We then tested whether the loop number decrease in Stag2-deficient cells could be a cause for transcriptional changes. When examining the local Hi-C maps, we noticed that loops anchored at gene promoters, including those of downregulated genes, were reduced in Stag2f/y;NesCre oligodendrocytes (Figure 7D, Figure 7—figure supplement 2A–C, and Supplementary file 2). The effects were again reproducible in each replicate (Figure 7—figure supplement 2D). Promoter-anchored loops (P-loops) can potentially be promoter–promoter links, promoter–enhancer links, and gene loops. The total number of P-loops was proportionally decreased in Stag2f/y;NesCre cells (Figure 7—figure supplement 3A). Moreover, the loops anchored at the downregulated genes were stronger than those at upregulated and stable genes (Figure 7—figure supplement 3B). We then compared P-loops associated with DEGs using pile-up analysis of local contact maps. Loop enrichment at promoters of downregulated genes was reduced in Stag2f/y;NesCre cells to a greater extent than that at promoters of upregulated and stable genes (Figure 7E). Among the 162 downregulated DEGs with reduced promoter-anchored loops in the Stag2-depleted cells, 137 genes (85%) had STAG2 peaks in their promoter regions (TSS ± 2 kb). The loops anchored at downregulated genes with STAG2 binding had significantly higher loop scores, compared to those with no STAG2 binding (Figure 7—figure supplement 3C, D). This difference was still observed in Stag2-deleted cells, suggesting that the stronger looping at these gene promoters might be maintained by STAG1-cohesin or other factors in these cells. By contrast, the loops anchored at upregulated genes with STAG2 binding had lower loop scores. These differences became insignificant in the Stag2-deleted cells. The loop scores of loops anchored at stable genes were not affected by STAG2 occupancy. Taken together, our results suggest that Stag2 loss diminishes the number of, but not the strength, short chromosome loops, including promoter-anchored loops. Highly expressed genes might be more reliant on these loops for transcription and are preferentially downregulated by Stag2 loss (Figure 4—figure supplement 4B-D).

We also performed pile-up analysis of local chromatin regions flanking TSSs (Figure 7F). Strikingly, we observed a clear stripe that extended from the TSS of downregulated gene only in the direction of transcription. The formation of promoter-anchored stripes (P-stripes) on aggregated plots is consistent with one-sided loop extrusion from the promoter to the gene body. The P-stripe was still present in Stag2f/y;NesCre cells, suggesting that STAG1 could compensate for the loss of STAG2 and mediate its formation (Figure 7F and Figure 7—figure supplement 3E).

Discussion

Cohesin is critical for the three-dimensional (3D) organization of the genome by extruding chromosome loops. Acute depletion of cohesin abolishes chromosome loops and TADs, but has moderate effects on transcription. The two forms of cohesin in vertebrate somatic cells, namely STAG1-cohesin and STAG2-cohesin, have largely redundant functions in supporting sister-chromatid cohesion and cell viability, but they have nonredundant functions during development. In this study, we have established a myelination-promoting function of STAG2 in the CNS in the mouse. We further provide evidence linking hypomyelination caused by STAG2 loss to reduced promoter-anchored loops at myelination genes in oligodendrocytes.

Myelination functions of STAG2 and implications for cohesinopathy

Selective ablation of Stag2 in the nervous system in the mouse causes growth retardation, neurological defects, and premature death. STAG2 loss delays the maturation of oligodendrocytes and reduces the expression of highly active myelin and cholesterol biosynthesis genes in oligodendrocytes, resulting in hypomyelination in the CNS. Hypomyelination disorders in humans and mice are known to produce abnormal neurological behaviors similar to those seen in our Stag2 cKO mice, suggesting that hypomyelination is a major underlying cause for the phenotypes in Stag2 cKO mice. The growth retardation in these mice can be explained by insufficient secretion of growth hormones, which may be a consequence of defective neuronal signaling.

Mutations of cohesin subunits and regulators, including STAG2, cause the Cornelia de Lange syndrome (CdLS) and other similar developmental diseases, collectively termed cohesinopathy. CdLS patients exhibit short stature and developmental defects in multiple tissues and organs, including the brain. Although STAG2 mutations are implicated in human cohesinopathy, these mutations are rare and hypomorphic (Soardi et al., 2017). The cohesin loader NIPBL is the most frequently mutated cohesin regulator in cohesinopathy (Mannini et al., 2013). NIPBL deficiency is expected to affect the functions of both STAG1- and STAG2-cohesin. It is possible that the partial loss of STAG2-cohesin function leads to subtle myelination defects in patients with cohesinopathy. Indeed, lack of myelination in certain brain regions of CdLS patients has been reported (Avagliano et al., 2017; Vuilleumier et al., 2002). As myelination of the CNS mostly occurs after birth and during childhood, strategies aimed at enhancing myelination might help to alleviate certain disease phenotypes and symptoms.

Mechanisms by which STAG2 promotes myelination

STAG2 promotes oligodendrocyte maturation and the expression of myelination genes in mature oligodendrocytes. Because STAG2 does not have an established cohesin-independent function, it most likely activates the myelination-promoting transcriptional program as a core component of cohesin. Consistent with previous reports (Rao et al., 2017), loss of STAG2-cohesin in oligodendrocytes does not affect genome compartmentalization, but reduces the number of relatively short chromosome loops, including promoter-anchored loops. Promoter-anchored loops at downregulated genes are reduced to a greater extent than those at stable and upregulated genes. These findings suggest that STAG2-cohesin promotes the myelination transcriptional program by forming promoter-anchored loops.

Pile-up analysis of Hi-C maps reveals the formation of asymmetric promoter-anchored stripes in the direction of transcription at downregulated genes, indicative of active loading of cohesin at TSSs followed by one-sided loop extrusion from the promoter to the gene body. The stripes are, however, not reduced in STAG2-deficient cells. Because both forms of cohesin are capable of loop extrusion, it is possible that STAG1-cohesin can compensate for the loss of STAG2-cohesin in loop extrusion. It remains to be tested whether the intrinsic kinetics and processivity of loop extrusion mediated by the two forms of cohesin are differentially regulated by cellular factors or posttranslational modifications and whether these differences contribute to their nonredundant roles in transcription regulation.

We envision three possibilities that may account for why oligodendrocytes, but not other cell types, are more severely affected by Stag2 loss in the CNS. First, STAG2-cohesin may be more abundant than STAG1-cohesin in postmitotic OLs, making them more dependent on STAG2 for proper functions. Second, STAG1-cohesin preferentially localizes to CTCF-enriched TAD boundaries whereas STAG2-cohesin is more enriched at enhancers lacking CTCF (Kojic et al., 2018). Enhancers are critical for cell-type-specific gene transcriptional programs. To cooperate with the axonal growth during postnatal neurodevelopment, enhancer-enriched transcription factors induce timely and robust gene expression in oligodendrocytes for proper myelination (Mitew et al., 2014). The high demand for enhancer function may render the transcription of myelination genes more reliant on STAG2-cohesin. Finally, the C-terminal regions of STAG1 and STAG2 are divergent in sequence and may bind to different interacting proteins and be subjected to differential regulation. STAG2 may interact with oligodendrocyte-specific transcription factors and be preferentially recruited to myelination genes. It will be interesting to investigate the interactomes of STAG1 and STAG2 in oligodendrocytes using mass spectrometry.

STAG2-mediated chromosome looping and transcription

The mechanisms by which STAG2-dependent chromosome looping facilitates transcription are unclear at present. We propose several models that are not mutually exclusive (Figure 8). First, by forming promoter–enhancer loops, STAG2-cohesin brings the mediator complex and other enhancer-binding factors to the spatial proximity of the general transcriptional machinery at the promoter, thereby enhancing RNA polymerase II recruitment and transcription initiation. The existence of P-stripes at STAG2-dependent genes in the Hi-C maps suggests that STAG2-mediated promoter–enhancer loops may involve enhancers located in the gene body. Second, loop extrusion by STAG2-cohesin may promote transcription elongation by regulating transcription-coupled pre-mRNA processing. For example, STAG2 has been shown to interact with RNA–DNA hybrid structures termed R-loops in vitro and in cells (Pan et al., 2020; Porter et al., 2021). R-loops formed between the nascent pre-mRNA and the DNA template impede transcription elongation and need to be suppressed (Moore and Proudfoot, 2009). When traveling with the transcription machinery on DNA, STAG2-cohesin might directly suppress R-loop formation or recruit other factors, such as the spliceosome, for cotranscriptional pre-mRNA processing and R-loop resolution. Third, STAG2-cohesin may establish promoter–terminator gene loops to recycle the RNA polymerase II that has finished one cycle of transcription back to the TSS for another round of transcription. Future experiments using high-resolution Hi-C methods in oligodendrocytes and ChIP-seq experiments with additional enhancer- and promoter-specific histone marks will allow us to better define the nature of STAG2-dependent promoter-anchored loops and stripes. It will also be interesting to examine whether Stag2 deletion causes the accumulation of R-loops in downregulated genes and the incomplete splicing of their pre-mRNAs.

Proposed roles of STAG2-cohesin-mediated loop extrusion during transcription in oligodendrocytes.

(A) STAG2-cohesin-mediated chromosome looping connects the enhancer and the promoter, thus facilitating interactions among oligodendrocyte-specific transcription factors, the mediator complex, and the general transcription machinery including RNA polymerase II. (B) STAG2-cohesin travels along the gene body via transcription-coupled loop extrusion to facilitate pre-mRNA processing. (C) STAG2-cohesin mediates the formation of gene loops that bring the terminator close to the promoter and facilitate Pol II recycling for multiple rounds of transcription.

Conclusion

We have discovered a requirement for the cohesin subunit STAG2 in the myelination of the CNS in mammals. Our findings implicate hypomyelination as a contributing factor to certain phenotypes of cohesinopathy, including growth retardation and neurological disorders. We provide evidence to suggest that STAG2 promotes the myelination transcriptional program in oligodendrocytes through the formation of promoter-anchored loops. Our study establishes oligodendrocytes as a physiologically relevant cell system for dissecting the cellular functions and regulatory mechanisms of cohesin-mediated chromosome folding and genome organization.

Materials and methods

Generation of mouse lines and mouse husbandry

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All animals were handled in accordance with institutional guidelines of the Institutional Animal Care and Use Committee (IACUC; AAALAC unit number 000673) of University of Texas (UT) Southwestern Medical Center under the animal protocol number (APN) 102335. The Stag2 locus was targeted by inserting one neo cassette and two loxP sites flanking exon 8 via homologous recombination in the mouse embryonic stem (ES) cells. G418-selected positive ES clones were screened for successful targeting by nested PCR tests on both 5′ and 3′ integration sites of loxP. Four confirmed ES clones were then microinjected into mouse blastocysts. The chimeras were bred to the R26FLP mouse line for the removal of the neo cassette. Stag2f/+ mice with the 129/B6 background were crossed with Stag2f/y or WT C57BL/6J mice and maintained on this background. For the generation of the inducible system of Stag2f/y;Rosa26CreErt2 mice, Stag2f/f mice were bred to the mouse strain that contains two alleles of the conditional Cre-ERT2 cassette (B6.129-Gt(ROSA)26Sortm1(cre/ERT2)Tyj/J, JAX stock #008463) (Ventura et al., 2007). For the generation of the nervous system-specific Stag2f/y;NesCre mice, the Stag2f/f mice were crossed with the transgenic mice carrying one allele of Cre recombinase driven by the rat nestin promoter and enhancer (Tg(Nes-cre)1Kln, JAX stock #003771) (Giusti et al., 2014; Tronche et al., 1999).

Whole-body knockout mice were generated by CRISPR–Cas9 gene editing technology. Briefly, a pair of guide RNAs (sgRNA; sequences listed in the Key Resource Table) targeting genomic sequence flanking exon 8 of the Stag2 locus were tested for cutting efficiency in cell culture, transcribed in vitro, purified, checked for integrity, and microinjected into B6C3F1 mouse zygotes along with the Cas9 mRNA (5-methylcytidine, pseudouridine, TriLink). 20 ng/μl of Cas9 mRNA and 10 or 20 μg/μl each of sgRNA were used. The injected embryos were transferred to the surrogate mother on the same day. Mosaic F0 founders carrying the Stag2null allele were identified by PCR genotyping. The reduction of the STAG2 protein was confirmed by western blotting in multiple tissues. The F0 founders were crossed with WT C57BL/6J mice to generate the Stag2+/− F1. The mutations in F1 were identified by Sanger Sequencing. Two mouse lines carrying genomic deletions between the Cas9 cleavage sites were chosen for the generation of Stag2null mouse embryos.

All mice were housed in the antigen-free barrier facility with 12 hr light/dark cycles (6 AM on and 6 PM off). Mice were fed a standard rodent chow (2016 Teklad Global 16% protein rodent diet, Harlan Laboratories).

Immunoblotting

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The C-terminal fragment of human STAG2 protein was expressed and purified from Escherichia coli and used as the antigen to generate rabbit polyclonal antibodies against STAG2 at YenZym. Other antibodies were purchased from the following commercial sources: anti-SMC1 (Bethyl Laboratories, A300-055A), anti-SMC3 (Bethyl Laboratories, A300-060A), anti-RAD21 (Bethyl Laboratories, A300-080A), anti-SA1 (Bethyl Laboratories, A302-579A), anti-SA2 (Bethyl Laboratories, A302-581A), anti-α-TUBULIN (Sigma-Aldrich, DM1A), anti-MBP (Abcam, ab7349), anti-PLP1 (Abcam, ab28486), and anti-H3K27ac (Abcam, ab4729).

For immunoblotting, brain hemispheres were homogenized in a Precellys tissue homogenizer (Bertin Instruments) with the lysis buffer [20 mM Tris–HCl (pH 7.7), 137 mM NaCl, 2 mM Ethylenediamine tetraacetic acid (EDTA), 10% (vol/vol) glycerol, 1% (vol/vol) Triton X-100, 0.5 mM dithiothreitol, 1 mM Phenylmethylsulfonyl fluoride (PMSF), 1 mM Na3VO4, 10 mM β-glycerophosphate, 5 mM NaF, and protease inhibitors (Roche)]. Homogenized brain tissues were lysed on ice for 1 hr. The lysate was then subjected to centrifugation at 20,817 × g at 4°C for 20 min and further cleared by filtering through a 0.45 μm filter. The cleared lysate was analyzed by sodium dodecyl sulfate–polyacrylamide gel electrophoresis and transferred to membranes, which was then incubated with the appropriate primary and secondary antibodies. The blots were imaged with the Odyssey Infrared Imaging System (LI-COR).

Tissue histology and immunohistochemistry

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Mouse brains were fixed in 10% neutral buffered formalin solution for 48 hr followed by paraffin embedding and coronal or sagittal sectioning at 5 μm. H&E staining and LFB staining were performed by the Molecular Pathology Core at UT Southwestern Medical Center. Investigators were blinded to the genotype. Images were acquired with the DM2000 microscope (Leica) at ×1.25 resolution.

Immunohistochemistry was performed as previously described (Choi et al., 2016). Briefly, deparaffinized sections were fixed with 4% paraformaldehyde, subjected to antigen retrieval by boiling with 10 mM sodium citrate (pH 6.0), and then incubated with the indicated antibodies at 1:100 dilution. The slides were scanned with an Axioscan.Z1 microscope (Zeiss) at ×40 resolution at the Whole Brain Microscopy Facility at UT Southwestern Medical Center. Images were processed and quantified with ImageJ. For the myelinated fiber length measurement and coherency analysis, coronal sections of the brain cortex stained with the anti-MBP antibody were processed as previously described (van Tilborg et al., 2017). The myelinated axial thinning and fiber length measurement were performed by the plugin DiameterJ. The coherency analysis of myelinated axons was performed with the plugin OrientationJ.

Isolation of primary oligodendrocytes

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The immunomagnetic isolation of oligodendrocytes from Stag2f/y and Stag2f/y;NesCre P12-P14 pups was conducted using anti-O4 microbeads (Miltenyi Biotec) according to a published protocol (Flores-Obando et al., 2018). Briefly, brain cortices were dissected, pooled, minced into 1 mm3 cubes, and incubated with the Papain dissociation solution (neurobasal medium with 1% penicillin–streptomycin, 1% L-glutamine, 2% B27 supplement, 20–30 U/ml of Papain, and 2500 U DNase I) in a 37°C, 5% CO2 incubator for more than 20 min. The enzymatic digestion was inactivated by the addition of 1 ml of fetal bovine serum (FBS). Gentle trituration by 10 ml, 5 ml and 1-ml pipettes was applied to break up cell clumps. Cells were collected by centrifugation (200 × g, 10 min), washed first with serum-containing medium (neurobasal medium with 1% penicillin–streptomycin, 1% L-glutamine, 2% B27 supplement, and 10% FBS), and then with the magnetic cell sorting (MCS) buffer (phosphate-buffered saline, pH 7.2, with 0.5% bovine serum albumin [BSA], 0.5 mM EDTA, 5 μg/ml insulin and 1 g/l glucose). The cell pellet was resuspended in the MCS buffer and incubated with anti-O4 microbeads at 10 μl/107 cells at 4°C for 15 min followed by 1× wash with the MCS buffer. The O4+ immature oligodendrocytes were sorted through the magnetic LS columns according to the manufacturer’s instruction. Freshly prepared oligodendrocytes were directly used or fixed for subsequent analysis.

Metabolic cage analysis

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Mice were singly housed in shoebox-sized cages with a 5-day acclimation period followed with a 4-day recording period. Recorded parameters were analyzed by the TSE system and normalized to body weight. The experiments were conducted by the core personnel under the core protocol at the Metabolic Phenotyping Core at UT Southwestern Medical Center. Investigators were blinded to the genotype.

Growth hormone and IGF-1 detection

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Blood samples were collected from facial bleeding without fasting. Plasma growth hormone levels were determined with the rat/mouse growth hormone ELISA kit (EMD Milipore, EZRMGH-45K). Plasma IGF-1 concentrations were measured using the mouse/rat IGF1 Quantikine ELISA kit (R&D Systems).

Sterol and oxysterol composition analysis

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Brain hemispheres were preweighed and snap-frozen for extraction and measurement by mass spectrometry. The sterol extraction and quantitative analysis were conducted at the Center of Human Nutrition at UT Southwestern Medical Center as described previously (McDonald et al., 2012).

Electron microscopy

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Stag2f/y and Stag2f/y;NesCre P18 pups were transcardially perfused with 4% paraformaldehyde, 1% glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.4). Tissues were dissected and fixed with 2.5% (vol/vol) glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.4) for at least 2 hr. After three rinses with the 0.1 M sodium cacodylate buffer, optic nerve samples were embedded in 3% agarose and sliced into small blocks. All samples were again rinsed with the 0.1 M sodium cacodylate buffer three times and postfixed with 1% osmium tetroxide and 0.8% potassium ferricyanide in the 0.1 M sodium cacodylate buffer for 3 hr at room temperature. Blocks were rinsed with water and en bloc stained with 4% uranyl acetate in 50% ethanol for 2 hr. Samples were dehydrated with increasing concentrations of ethanol, transitioned into propylene oxide, infiltrated with Embed-812 resin, and polymerized in a 60°C oven overnight. Blocks were sectioned with a diamond knife (Diatome) on a Leica Ultracut 7 ultramicrotome (Leica Microsystems) and collected onto copper grids, poststained with 2% aqueous uranyl acetate and lead citrate. Images were acquired on a Tecnai G2 Spirit transmission electron microscope (Thermo Fisher) equipped with a LaB6 source using a voltage of 120 kV. Tissue processing, sectioning, and staining were completed by the Electron Microscopy Core at UT Southwestern Medical Center.

RNA-seq library preparation and sequencing

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Total RNA was extracted from brain hemispheres or isolated oligodendrocytes with Trizol. RNA integrity was determined by the Agilent BioAnalyzer 2100. TruSeq Stranded mRNA library prep kit (Illumina) was used to generate the mRNA libraries. The libraries were analyzed by the Bioanalyzer and multiplexed and sequenced using the NextSeq 500 high output kit (400 M reads) for the brain libraries or NextSeq 500 mid output kit (130 M reads) for the isolated oligodendrocytes libraries at the Next Generation Sequencing Core at UT Southwestern Medical Center.

Differential expression and pathway analysis

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Raw data from the sequencer were demultiplexed and converted to fastq files using bcl2fastq (v2.17, Illumina). The fastq files were checked for quality using fastqc (v0.11.2) Andrews, 2010 and fastq_screen (v0.4.4) (Wingett, 2011). Fastq files were mapped to the mm10 mouse reference genome (from iGenomes) using STAR (Dobin et al., 2013). Read counts were then generated using featureCounts (Liao et al., 2014). TMM normalization and differential expression analysis were performed using edgeR (Robinson et al., 2010). Pathway analysis was performed with the IPA software. Genes with more than 1.5-fold change and false-discovery rate FDR <0.01 were included in the brain RNA-seq pathway analysis. Genes with more than twofold change and FDR <0.05 were used for the pathway analysis of the RNA-seq data from oligodendrocytes.

RT-qPCR analysis

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Single-stranded cDNAs were converted from 2 µg of total RNA extracted from mouse brains with the high-capacity cDNA reverse transcription kit (Applied Biosystems). Quantitative PCR was conducted to determine transcript levels using gene-specific TaqMan probes (Applied Biosystems).

Single-cell RNA-seq

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Single-cell suspension was prepared from forebrains of P13 Stag2f/y or Stag2f/y;NesCre pups using the Papain Dissociation System (Worthington Biochemical, LK003150) according to the manufacturer’s instructions. Biological duplicates were made for each genotype. Single-cell RNA-seq libraries were generated with the Chromium Single Cell 3′ GEM, Library & Gel Bead Kit v3 (10× Genomics) according to the manufacturer’s guidelines. Cell density and viability were checked by the TC-20 Cell Counter (Bio-Rad). Cells were then loaded onto Chip B in the Chromium Controller (10× Genomics). 10,000 cells were targeted for each sample. The libraries were analyzed by the Bioanalyzer (Agilent) and pair-end sequenced in two flowcells of the NextSeq 500 High Output (400 M) run. The sequencing was performed at the Next Generation Sequencing Core at UT Southwestern Medical Center.

Data demultiplexing and alignment were performed using the Cell Ranger pipeline (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/ mkfastq) (10× Genomics). The raw features, barcodes, and matrixes were used as input for further analysis using the R package Seurat3 (Butler et al., 2018; Stuart et al., 2019) (https://satijalab.org/seurat/). Cells were filtered by the following criteria: nFeature_RNA (200–9500) and percent.mt <10. After filtering, a total of 5834 cells in Stag2f/y#1, 4699 cells in Stag2f/y#2, 9050 cells in Stag2f/y;NesCre#1, and 3073 cells in Stag2f/y;NesCre#2 were used for downstream analysis. 2000 variable features were found from each normalized dataset. All datasets were then integrated using identified anchors (dims = 1:30). Standard scaling and principal component analysis, clustering (resolution = 0.5), and tSNE reduction (dims = 1:30) were performed on the integrated dataset. Cluster biomarkers were identified, and top features were examined. Clusters were then manually assigned to distinct cell-type identities with knowledge from previous studies (Cahoy et al., 2008; Dulken et al., 2019; Marques et al., 2018; Marques et al., 2016; Marton et al., 2019; Saunders et al., 2018; Zeisel et al., 2018; Zywitza et al., 2018) (http://www.brainrnaseq.org/) (http://dropviz.org/). Clusters with the same cell-type identities were merged. Five clusters of oligodendrocyte lineage [cycling oligodendrocyte progenitors (OPCcycs), oligodendrocyte progenitors (OPCs), newly formed oligodendrocytes (NFOLs), myelin-forming oligodendrocytes (mFOLs), and fully matured oligodendrocytes (MFOLs)] were identified and selected for indicated gene expression comparison and plotting using Vlnplot or FeaturePlot functions. The trajectory analysis was performed using Monocle3 (Cao et al., 2019) in the oligodendrocyte cell population. Gene density plot over pseudotime was generated as previously described (Luecken and Theis, 2019).

ChIP-seq

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Chromatin immunoprecipitation (ChIP) was performed as previously described (Liu et al., 2017). Briefly, isolated oligodendrocytes were fixed with 1% formaldehyde and fragmented with a sonicator (Branson 450). The fragmented chromatin was incubated with antibodies overnight at 4°C. Dynabeads Protein A (Thermo Fisher Scientific) was used for the immunoprecipitation. Libraries were generated by the Next Gen DNA Library Kit (Active Motif) with the Next Gen Indexing Kit (Active Motif) for STAG2 ChIP-seq or the KAPA HyperPrep Kits (KAPA Systems) for histone ChIP-seq. The libraries were analyzed by the Bioanalyzer and pool sequenced with the NextSeq 500 mid output (130 M) kit. After mapping reads to the mouse genome (mm10) by bowtie2 (v2.2.3) (Langmead and Salzberg, 2012) with the parameter ‘–sensitive’, we performed filtering by removing alignments with mapping quality less than 10 and then removing duplicate reads identified by Picard MarkDuplicates (v1.127). For STAG2 ChIP-seq, Picard MarkDuplicate was used to remove duplicates together with options to use molecular identifiers (MIDs) information in the reads. Enriched regions (peaks) were identified using MACS2 (v2.0.10) (Zhang et al., 2008), with a q-value cutoff of 0.05 for peaks. Peak regions were annotated by HOMER (Ross-Innes et al., 2012).

Hi-C library generation, sequencing, and analysis

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Hi-C was performed at the Genome Technology Center at NYU Langone Health from 3.5 to 4.0 μg of DNA isolated from cells cross-linked with 2% formaldehyde at room temperature for 10 min. Experiments were performed in duplicates following the instructions from the Arima Hi-C kit (Arima Genomics, San Diego, CA). Subsequently, Illumina-compatible sequencing libraries were prepared by using a modified version of the KAPA HyperPrep library kit (KAPA BioSystems, Willmington, MA). Quality check steps were performed to assess the fraction of proximally ligated DNA labeled with biotin, and the optimal number of PCR reactions needed to make libraries. The libraries were loaded into an Illumina flowcell (Illumina, San Diego, CA) on a NovaSeq 6000 instrument for paired-end 50 reads.

Hi-C analysis was performed using the HiC-Bench pipeline (Lazaris et al., 2017; Tsirigos et al., 2012) (https://github.com/NYU-BFX/hic-bench) and HiC-Pro v3.1.0 (Servant et al., 2015). The read pairs were aligned and filtered with the following parameters: Genome-build=mm10; –very-sensitive-local –local; mapq = 20; –min-dist 25000 –max-offset 500. The Juicer ‘pre’ tool (Durand et al., 2016) (RRID: SCR_017226, v1.11.09; https://github.com/aidenlab/juicer) was used to generate the.hic file with default parameters. Sample duplicates were combined. The compartment analysis was done using the HOMER tool (Heinz et al., 2010) (http://homer.ucsd.edu/homer/index.html) with 100 kb bins. H3K27ac ChIP-seq data were used to assign A/B compartments. Eigenvector-1 bins were considered shifted (AB and BA) when the bin sign changed and the delta value was greater than 1.5. TADs and boundaries were identified at 40 kb resolution with the HiCRatio method with the follow parameters: –min-lambda=0.0 –max-lambda=1.0 –n-lambda=6 –gamma = 0 –distance = 500 kb –fdr = 0.1. TADs were also identified using the Juicer tools (v1.22.01) arrowhead at 10 and 25 kb resolution. Aggregate TAD analysis was performed on TAD boundaries by coolpup.py (Flyamer et al., 2020) or GENOVA (van der Weide et al., 2021). The.hic files were converted to.cool format for visualization and plotting with pyGenomeTracks (Lopez-Delisle et al., 2021) at 5 kb resolution.

Loop analysis and RNA-seq integration

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The loops were classified into group-specific loops and common loops by using the significance cutoffs provided by Fit-HiC (Ay et al., 2014). A q-value cutoff of 0.01 was used to identify significant loops in both groups. A loop is considered ‘group-specific’ if it is only present in one group with a q value <0.01 and not present in the other group with cutoff of q val <0.1. Loop anchors were annotated with the gene promoter information (promoter defined as ±2 kb from the TSS). The genes were classified into ‘down’ and ‘up’ regulated genes using an FDR cutoff of 0.05, logFC cutoff of ±0.58 and logCPM >0. ‘stable’ or less changed genes are defined as logFC <0.38, and logCPM >0. Random 1000 genes were chosen for analysis and plotting. The active genes (logCPM >0) were also grouped in ‘high’, ‘mid’, and ‘low’ expression groups by separating the genes in three quantiles according to the logCPM values. For the loop enrichment scores, normalized contact scores were computed using Fit-HiC at 10 kb resolution and bias corrected. Pile-up analysis was performed with coolpup.py (Flyamer et al., 2020) with the KR method to balance the weight and random shift controls for distance normalization at 5 kb or using GENOVA.

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Mus musculus, female)Stag2+/−This paperExon8 of Stag2 was targeted
by CRISPR–Cas9
(see Materials and methods)
Strain, strain background (Mus. musculus, both sex)Stag2f/y; Stag2f/fThis paperExon8 of Stag2 genomic locus
was flanked by loxP sites
(see Materials and methods)
Strain, strain background (Mus. musculus, both sex)C57BL/6JThe Jackson Laboratory000664;
RRID:IMSR_JAX:000664
Strain, strain background (Mus. musculus, both sex)B6.129-Gt(ROSA)26
Sortm1(cre/ERT2)Tyj/J
The Jackson Laboratory008463;
RRID:IMSR_JAX:008463
Strain, strain background (Mus. musculus, male)B6.Cg-Tg(Nes-cre)1Kln/JThe Jackson Laboratory003771;
RRID:IMSR_JAX:003771
Antibodyanti-STAG2
(Rabbit polyclonal)
This paperThe C-terminus recombinant
protein of STAG2 (Homo sapiens)
was used to generate the antibody;
WB (1:1000)
Antibodyanti-α-TUBULIN
(Mouse monoclonal)
Sigma-AldrichT9026;
RRID:AB_477593
WB (1:1000)
Antibodyanti-SA1
(Rabbit polyclonal)
Bethyl LaboratoriesA302-579A;
RRID:AB_2034857
WB (1:1000)
Antibodyanti-SMC1
(Rabbit polyclonal)
Bethyl LaboratoriesA300-055A
RRID:AB_2192467
WB (1:1000)
Antibodyanti-SMC3
(Rabbit polyclonal)
Bethyl LaboratoriesA300-060A;
RRID:AB_67579
WB (1:1000)
Antibodyanti-RAD21
(Rabbit polyclonal)
Bethyl LaboratoriesA300-080a;
RRID:AB_2176615
WB (1:1000)
Antibodyanti-MBP
(Rat monoclonal)
Abcamab7349;
RRID:AB_305869
IHC (1:100)
Antibodyanti-PLP1
(Rabbit polyclonal)
Abcamab28486;
RRID:AB_776593
IHC (1:100)
Antibodyanti-GFAP
(Rabbit polyclonal)
Abcamab7260;
RRID:AB_305808
IHC (1:100)
Antibodyanti-MAP2
(Rabbit polyclonal)
Abcamab32454;
RRID:AB_776174
IHC (1:50)
Antibodyanti-H3K27ac
(Rabbit polyclonal)
Abcamab4729;
RRID:AB_2118291
ChIP (5 μl per test)
Antibodyanti-O4 Microbeads
(Mouse monoclonal)
Miltenyi Biotec130-094-543;
RRID:AB_2847907
MACS (10 μl per 107 cells)
Antibodyanti-rabbit IgG (H+L), DyLight 800 Conjugate (Goat polyclonal)Cell Signaling Technology5151 S;
RRID:AB_10697505
WB (1:5000)
Antibodyanti-mouse IgG (H+L), DyLight 680 Conjugate (Goat polyclonal)Cell Signaling Technology5470 S;
AB_10696895
WB (1:5000)
Antibodyanti-rat IgG (H+L), Alexa Fluor 568 (Goat polyclonal)Thermo Fisher ScientificA-11077;
RRID:AB_2534121
IHC (1:500)
Antibodyanti-rabbit IgG (H+L), Alexa Fluor 488 (Goat polyclonal)Thermo Fisher ScientificA-11008;
RRID:AB_143165
IHC(1:500)
Sequence-based reagentsgRNA#1 target on Stag2This paperCRISPR single-guide
RNA target sequence
Target sequence: TAGCCAACCTCTTTCTCTATTGG
Sequence-based reagentsgRNA#2 target on Stag2This paperCRISPR single-guide
RNA target sequence
Target sequence:
CAGACAGTATACTGTAATGGAGG
Sequence-based reagentTaqMan probes: Stag2Thermo Fisher ScientificMm01311611_m1
Sequence-based reagentTaqMan probes: Klk6Thermo Fisher ScientificMm00478322_m1
Sequence-based reagentTaqMan probes: Ninj2Thermo Fisher ScientificMm00450216_m1
Sequence-based reagentTaqMan probes: CpmThermo Fisher ScientificMm01250802_m1
Sequence-based reagentTaqMan probes: Fa2hThermo Fisher ScientificMm00626259_m1
Sequence-based reagentTaqMan probes: GapdhThermo Fisher ScientificMm99999915_g1
Sequence-based reagentStag2 gt 5 FThis paperGenotype sequence
primers
GGTATTTACTTGATAGCCAACC
Sequence-based reagentStag2 gt 5 RThis paperGenotype sequence
primers
CTCATCTTGATTTTCCTGAAGC
Sequence-based reagentStag2 gt 3 FThis paperGenotype sequence
primers
GGTTGAGACAGACAGTATAC
Sequence-based reagentStag2 gt 3 RThis paperGenotype sequence
primers
AGGCTGGACTATGACAACTC
Sequence-based reagentISH Probe Stag2 P1 FThis paperRiboprobe synthesis
primers
TACGGTACCGACCTTTCAGATGTCACTCCG
Sequence-based reagentISH Probe Stag2 P1 RThis paperRiboprobe synthesis
primers
GAAGGATCCGCATCGGATAGACACTCATGA
Sequence-based reagentISH Probe Stag2 P2 FThis paperRiboprobe synthesis
primers
TACGGATCCGACCTTTCAGATGTCACTCCG
Sequence-based reagentISH Probe Stag2 P2 RThis paperRiboprobe synthesis
primers
GAAGGTACCGCATCGGATAGACACTCATGA
Sequence-based reagentISH Probe Stag1 P1 FThis paperRiboprobe synthesis
primers
TTAGGTACCTTACAATGCCTGGTCCTCAGT
Sequence-based reagentISH Probe Stag1 P1 RThis paperRiboprobe synthesis
primers
GAAGGATCCCTTTCATTGGCTCTCTTCCC
Sequence-based reagentISH Probe Stag1 P2 FThis paperRiboprobe synthesis
primers
TTAGGATCCTTACAATGCCTGGTCCTCAGT
Sequence-based reagentISH Probe Stag1 P2 RThis paperRiboprobe synthesis
primers
GAAGGTACCCTTTCATTGGCTCTCTTCCC
Commercial assay or kitArima-HiC KitArima Genomics510008
Chemical compound, drugTamoxifenSigma-AldrichT5648
Chemical compound, drug4-HydroxytamoxifenSigma-AldrichH7904
Software, algorithmGraphPad PrismGraphPad SoftwareRRID:SCR_002798;
https://www.graphpad.com/scientific-software/prism/
Software, algorithmImageJ (Fiji)ImageJRRID:SCR_002285;
https://imagej.net/software/fiji/
Software, algorithmRStudioThe R FoundationRRID:SCR_000432;
https://www.rstudio.com/
Software, algorithmBcl2fastqIlluminaRRID:SCR_015058v2.17
Software, algorithmFastqcAndrews, 2010; PMID:24501021RRID:SCR_014583v0.11.2
Software, algorithmFastq_screenWingett, 2011RRID:SCR_000141;
https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
v0.4.4
Software, algorithmSTARDobin et al., 2013; PMID:23104886RRID:SCR_004463;
https://github.com/alexdobin/STAR
v2.5.3a
Software, algorithmFeatureCountsLiao et al., 2014; PMID:24227677RRID:SCR_012919;
https://bioconductor.org/packages/release/bioc/html/Rsubread.html
Software, algorithmedgeRRobinson et al., 2010; PMID:19910308RRID:SCR_012802;
https://bioconductor.org/packages/release/bioc/html/edgeR.html
Software, algorithmIngenuity pathway analysisQIAGEN, Krämer et al., 2014; PMID:24336805RRID:SCR_008653; https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis
Software, algorithmMACS2Zhang et al., 2008; PMID:18798982RRID:SCR_013291v2.0.10
Software, algorithmBowtie2Langmead and Salzberg, 2012; PMID:22388286RRID:SCR_016368v2.2.3
Software, algorithmPicard MarkDuplicatesBroad Institute, GitHub RepositoryRRID:SCR_006525; http://broadinstitute.github.io/picard/v1.127
Software, algorithmHOMERHeinz et al., 2010, Ross-Innes et al., 2012; PMID:20513432RRID:SCR_010881;
http://homer.ucsd.edu/homer/
Software, algorithmDeeptoolsRamírez et al., 2016; PMID:27079975RRID:SCR_016366;
https://deeptools.readthedocs.io/en/develop/
Software, algorithmGalaxyAfgan et al., 2018; PMID:29790989RRID:SCR_006281;
https://usegalaxy.org
Software, algorithmCell Ranger10× GenomicsRRID:SCR_017344;
https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/mkfastq
Software, algorithmSeuratNew York Genome Center; Stuart et al., 2019; PMID:31178118RRID:SCR_016341; https://satijalab.org/seuratSatija Lab
Software, algorithmMonocle3UW Genome Sciences; Cao et al., 2019; PMID:30787437RRID:SCR_018685; https://cole-trapnell-lab.github.io/monocle3/Cole Trapnell’s Lab, v3.0
Software, algorithmHiC-Bench pipelineLazaris et al., 2017, Tsirigos et al., 2012; PMID:22113082https://github.com/NYU-BFX/hic-benchv0.1
Software, algorithmJuicer ‘pre’ toolDurand et al., 2016; PMID:27467249RRID:SCR_017226; https://github.com/aidenlab/juicerAiden Lab, v1.11.09
Software, algorithmJuiceboxAiden Lab, BCMRRID:SCR_021172; https://github.com/aidenlab/Juiceboxv1.5.1
Software, algorithmHic2coolAbdennur and Mirny, 2020; PMID:31290943https://github.com/4dn-dcic/hic2coolv0.8.3
Software, algorithmpyGenomeTracksLopez-Delisle et al., 2021; PMID:32745185https://github.com/deeptools/pyGenomeTracksv3.7
Software, algorithmFit-HiCAy et al., 2014; PMID:24501021https://github.com/ay-lab/fithicv2.0.7
Software, algorithmCoolpup.pyFlyamer et al., 2020; PMID:32003791https://github.com/open2c/coolpuppyv0.9.5
Software, algorithmclusterProfilerBioinformatics Group, Southern Medical University; Wu et al., 2021; PMID:34557778RRID:SCR_016884;
https://github.com/YuLab-SMU/clusterProfiler
v4.4.1
Software, algorithmHiC-ProServant et al., 2015; PMID:26619908RRID:SCR_017643v3.1.0
Software, algorithmHiCRepYang et al., 2017; PMID:28855260https://github.com/TaoYang-dev/hicrepv1.11.0
Software, algorithmGENOVAvan der Weide et al., 2021; PMID:34046591https://github.com/robinweide/GENOVAv1.0.0.9

Data availability

The RNA-seq, scRNA-seq, ChIP-seq, and Hi-C datasets generated and analyzed during the current study are available in the GEO repository, with the accession number GSE186894.

The following data sets were generated
    1. Cheng N
    2. Kanchwala M
    3. Evers BM
    4. Xing C
    5. Yu H
    (2021) NCBI Gene Expression Omnibus
    ID GSE186894. STAG2 promotes the myelination transcriptional program in oligodendrocytes.

References

Decision letter

  1. Adèle L Marston
    Reviewing Editor; University of Edinburgh, United Kingdom
  2. Jessica K Tyler
    Senior Editor; Weill Cornell Medicine, United States
  3. Adèle L Marston
    Reviewer; University of Edinburgh, United Kingdom
  4. Andrew J Wood
    Reviewer; University of Edinburgh, United Kingdom
  5. Simone Di Giovanni
    Reviewer; Imperial College London, United Kingdom

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "STAG2 promotes the myelination transcriptional program in oligodendrocytes" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Adèle L Marston as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jessica Tyler as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Andrew J Wood (Reviewer #2); Simone Di Giovanni (Reviewer #3).

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

Essential revisions:

1. Although the NexCre system is widely used, there is no clear consensus in the literature where the Cre recombinase is expressed. While Giusti et al. 2014 (J. of psychiatric research) reported neuronal and astroglial expression, Goebbels et al. 2006 (Genesis) reported neuronal only expression. A more detailed characterization of the cell type specificity and timing of Stag2 expression and ablation in the NPCs lineage would be useful. This is important to understand whether OLs are particularly sensitive to Stag2 deletion or whether Stag2 has not been deleted in neurons and astrocytes. From Figure 4A and FigS6B, neurons and astrocytes are hardly retrieved from WT brains, so it is difficult to compare Stag2 expression level and ablation in these cells. While it is clear that OLs are affected by Stag2 KO, it is not certain that these are the only and main cell type affected. Along the same lines, since it is difficult to quantify cell composition from the RNAseq, due to different cell survival and purification, a better characterization of cell type numbers on histological section is needed to clarify if Stag2Cre mice have indeed a generally normal neuronal cell differentiation.

2. More details are required for the 3D chromatin structure analysis. Although the data are mainly in line with the literature, opposite to other work (Wutz et al., EMBOJ 2017; Pekowska et al., Nature 2017; Casa et al., Genome Res 2020), the authors did not find any TAD alterations. This might be related to the resolution used; the methods or legends do not state which resolution has been used. Please clarify and include this information in the figure legend, and make sure that at least 25 kb resolution is used for TAD analysis. You should report a summary of the sequencing data including the total reads obtained and, importantly the number of valid cis-pairs in each of their libraries etc.

3. Also for the Hi-C data: from Figure 5F and 5E, the correlation between the 2 biological replicates seems to be not very high; please comment. Since the effect is only on the loops and is quite modest, it is important that you demonstrate that it is reproducible and report whether this effect was seen in independent datasets, and whether the same loops were affected in each case.

4. Regarding the single-cell and purified OL RNAseq, only two biological replicates have been used, but three would be recommended.

5. Please provide a better discussion of what distinguishes genes that are or are not regulated by STAG2. On page 15, you speculate that STAG2 might interact with oligodendrocyte-specific transcription factors and be preferentially recruited to myelination genes. Preferential recruitment to myelination genes should be tested using the existing ChIP-seq data represented in Figure S8.

6. Similarly, all three of the models shown in Figure 7 appear to indicate that STAG2 ordinarily regulates transcription through the formation of promoter-anchored loops, via a mechanism that involves direct binding to the relevant promoter in question. STAG2 binding sites should therefore be enriched at promoters where loops are lost in the STAG2 mutant condition. You should determine whether this is the case and discuss the implications for their models if it is not.

7. Similarly, you hypothesise three possible ways for Stag2 mediated gene expression regulation. One is by assisting enhancer-promoter loops. The authors have generated H3K27ac Chip from purified OLs, and this could be used to map E-P loops and test their hypothesis.

8., Figure 6D – A gene with a better-defined role in myelination than Pls1 would be preferable to use as an exemplar locus here.

8. Please provide the list of DE genes, STAG2 genomic occupancy, or promoters anchored loops as a supplementary file. This will help the readability of the paper and will allow full disclosure of the dataset to the community. Furthermore, please provide the reviewer passkey for the GEO link.

10. The authors are recommended to perform GO and pathway analysis using some other tools in addition to IPA, like GSEA. Also, since the pathways shown in the chart in Fig2Da and 4G are the top enriched pathways, it would be useful if the authors could provide a list of all the enriched pathways. Furthermore, please clarify what you used as background for the enrichment analysis.

11. From the purified OL RNAseq, the authors found 271 down and 292 upregulated genes. Please clarify what these genes are. In Fig4G, you showed a pathway analysis of all the DE, and it is not clear which gene set has been used for FigS7E. It would be useful to characterize the UP and DOWN genes separately.

12. In the single-cell RNAseq, you have used nFeature_RNA(200-9500). This seems to be a very high threshold, with the risk to include cell doublets. Please disclose whether you can reproduce the same analysis using a lower threshold.

13. It would be useful to test using the Stag2 ChIP data in isolated OLs, whether loop loss is observed preferentially on Stag2 occupied sites and how STAg2 occupancy correlates with loop score of up/down/no DE genes.

14. A more detailed Methods section is required: for example, how many replicates for the sequencing, sequencing depth, the sequence of the probes, PCR primers, antibodies (codes and quantity); more details for the OL isolation procedure. Methods for Cas9 mediated KO are missing.

15. Similarly, Figure Legends need more details for clarity

Reviewer #2 (Recommendations for the authors):

(1) Page 8 – the statement " a mild reduction in the number of MOLs" should be changed to "a mild reduction in the proportion of MOLs"

(2) Page 12 contains the statement "Highly expressed genes might be more reliant on these loops for transcription and are preferentially downregulated by Stag2 loss". Please provide a reference to the data which support the second part of this statement.

https://doi.org/10.7554/eLife.77848.sa1

Author response

Essential revisions:

1. Although the NexCre system is widely used, there is no clear consensus in the literature where the Cre recombinase is expressed. While Giusti et al. 2014 (J. of psychiatric research) reported neuronal and astroglial expression, Goebbels et al. 2006 (Genesis) reported neuronal only expression. A more detailed characterization of the cell type specificity and timing of Stag2 expression and ablation in the NPCs lineage would be useful. This is important to understand whether OLs are particularly sensitive to Stag2 deletion or whether Stag2 has not been deleted in neurons and astrocytes. From Figure 4A and FigS6B, neurons and astrocytes are hardly retrieved from WT brains, so it is difficult to compare Stag2 expression level and ablation in these cells. While it is clear that OLs are affected by Stag2 KO, it is not certain that these are the only and main cell type affected. Along the same lines, since it is difficult to quantify cell composition from the RNAseq, due to different cell survival and purification, a better characterization of cell type numbers on histological section is needed to clarify if Stag2Cre mice have indeed a generally normal neuronal cell differentiation.

In the initial submission, we have shown in the original Figure S6B,C that Stag2 expression was robust in most cell types in the brain and its level was greatly reduced in neuronal lineages in NesCre mice. Based on single-cell RNA-seq, the levels of Stag2 transcripts were reduced in “aNSCS/NPCs”, “Astrocytes/qNSCs”, “Astrocytes”, “Neuroblasts”, and OLs populations, but not in non-neural cell clusters, such as “Endothelials” and “Microglia/Macrophages”. As suggested by the reviewers, we examined the expression changes of Stag2 and other cohesin genes in OLs, astrocytes, and neuronal lineages (Figure 4—figure supplement 1). Based on this analysis, it is clear that Stag2 ablation occurred early in the NPC stage and was maintained in all differentiated cell lineages. We agree that the numbers of neurons and astrocytes in the wild-type sample were low. As the genetic ablation of Stag2 already occurred at the NPC stage, it is very likely that Stag2 ablation is maintained in neurons and astrocytes as both are known to be derived from NPCs. We have discussed this issue in the text. As suggested by the reviewers, we performed immunohistochemistry assays using neuron/astrocyte-specific antibodies to confirm that the differentiation of neurons and astrocytes in Stag2-depleted brains were largely normal (Figure 2—figure supplement 1B–E).

2. More details are required for the 3D chromatin structure analysis. Although the data are mainly in line with the literature, opposite to other work (Wutz et al., EMBOJ 2017; Pekowska et al., Nature 2017; Casa et al., Genome Res 2020), the authors did not find any TAD alterations. This might be related to the resolution used; the methods or legends do not state which resolution has been used. Please clarify and include this information in the figure legend, and make sure that at least 25 kb resolution is used for TAD analysis. You should report a summary of the sequencing data including the total reads obtained and, importantly the number of valid cis-pairs in each of their libraries etc.

For the TAD analysis, we used 40 kb resolution in our Hi-C analysis pipeline. This information has now been added to the figure legends. In our Stag2-depleted oligodendrocytes, we did not find large changes in TADs. As suggested by the reviewers, we re-analyzed our Hi-C datasets at 25 kb and 10 kb resolutions. As shown in Figure 6G and Figure 6—figure supplement 1D, consistent with our original analyses, there was minimal TAD alteration in Stag2-depleted oligodendrocytes. Our initial TAD analysis revealed that a few TADs had altered intra-TAD activities. We did not, however, find a correlation between changes in intra-TAD activities and the gene expression changes for genes located inside these TADs. A summary of Hi-C statistics is now included in Figure 6—figure supplement 1E.

3. Also for the Hi-C data: from Figure 5F and 5E, the correlation between the 2 biological replicates seems to be not very high; please comment. Since the effect is only on the loops and is quite modest, it is important that you demonstrate that it is reproducible and report whether this effect was seen in independent datasets, and whether the same loops were affected in each case.

In the original Figure 5E,F, the PCA and similarity analyses were performed using the insulation score and TAD boundary information, respectively. After the re-analysis of our Hi-C data with higher resolutions, we conducted similar analyses. The results were plotted in Figure 6—figure supplement 1. Overall, the analyses showed that there was high similarity between replicates, especially between the wild-type samples. As the insulation score and TAD boundary information were not ideal parameters to assess sample reproducibility, we calculated the reproducibility score of our replicate samples using HiCrep (v1.11.0) (Yang et al., 2017) at 25 kb resolution (Figure 6—figure supplement 1A). The stratum-adjusted correlation coefficient (SCC) is above 97% for wild-type samples and above 98% for Stag2-depleted samples for most chromosomes, except the Y chromosome. This score is comparable to previous publications (Hsieh et al., 2020; Li et al., 2020). Thus, we conclude that there is high reproducibility for our biological replicates. The relatively low SCC (above 75%) for the Y chromosome is likely due to fewer valid interactions within this chromosome in our dataset.

In the initial submission, we showed a reduction of loop numbers at gene promoters, including myelin gene promoters in Stag2-depleted oligodendrocytes. The reviewers wanted to know whether this reduction was reproducible in independent datasets. To address this question, we performed an aggregated peak analysis (APA) using GENOVA (van der Weide et al., 2021) on our replicates of Hi-C matrices in Figure 7—figure supplement 1B–D. The biological replicates showed good reproducibility on called loops, as well as genotype-specific loops. Examples of loop reduction at myelination genes for the duplicated Hi-C matrices are shown in Figure 7—figure supplement 2D.

4. Regarding the single-cell and purified OL RNAseq, only two biological replicates have been used, but three would be recommended.

It is common practice to use one or two transcriptomic libraries for each genotype of mice in single-cell transcriptomic analyses (Chang et al., 2022; Fang et al., 2019; Yang et al., 2020). We feel that two libraries for each genotype of mice are adequate, as the results from the two biological replicates are highly similar. For the bulk RNA-seq from purified OLs, we enriched primary oligodendrocytes using MACS system. Brain samples from 3-4 P12-14 pups were pooled together during the purification of primary cells. Although there were only two replicates of each genotype, each replicate sample contained biological materials from multiple mouse brains. Furthermore, the results of OL samples were consistent with the whole-brain RNA-seq data, which were obtained with more biological replicates.

5. Please provide a better discussion of what distinguishes genes that are or are not regulated by STAG2. On page 15, you speculate that STAG2 might interact with oligodendrocyte-specific transcription factors and be preferentially recruited to myelination genes. Preferential recruitment to myelination genes should be tested using the existing ChIP-seq data represented in Figure S8.

As suggested by reviewers, we integrated our RNA-seq analysis of OLs with the

STAG2 ChIP-seq results (Supplementary File 2). In our pathway analysis using both IPA (Figure 4—figure supplement 4E) and gene ontology (Figure 4—figure supplement 5 and 6), the axon ensheathment and oligodendrocyte differentiation-related genes were enriched in the down-regulated group, but not in the up-regulated group. Among the 271 >2-fold downregulated genes, there were 210 genes (77%) with STAG2 enrichment near the transcriptional start site (TSS ± 2 kb). Only 117 out of the 292 >2-fold upregulated genes (40%) had STAG2 ChIP-seq peaks at their TSS ±2 kb regions. Thus, STAG2 occupied many oligodendrocyte-specific gene promoters. Representative plots of STAG2 tracks at genomic loci of cholesterol biosynthesis genes and myelin genes were shown in Figure 5D and Figure 5—figure supplement 1, respectively.

6. Similarly, all three of the models shown in Figure 7 appear to indicate that STAG2 ordinarily regulates transcription through the formation of promoter-anchored loops, via a mechanism that involves direct binding to the relevant promoter in question. STAG2 binding sites should therefore be enriched at promoters where loops are lost in the STAG2 mutant condition. You should determine whether this is the case and discuss the implications for their models if it is not.

We hypothesized that STAG2-formed loops at relevant gene promoters facilitate their transcription. As the reviewers mentioned, it is important to test if STAG2 was enriched at gene promoters that were anchored with loops lost in the Stag2-depleted OLs. We integrated the STAG2 ChIP-seq data and the RNA-seq analysis with the total loops called from the OL samples (Supplementary File 2). As mentioned above, among the 271 >2-fold downregulated genes, 77% of genes were bound with STAG2 near their promoters (TSS ± 2 kb). Among the 162 downregulated genes with reduced promoter-anchored loops in the Stag2-depleted cells, 137 genes (85%) had STAG2 peaks at their promoters (TSS ± 2kb). These results are consistent with the proposed model.

7. Similarly, you hypothesise three possible ways for Stag2 mediated gene expression regulation. One is by assisting enhancer-promoter loops. The authors have generated H3K27ac Chip from purified OLs, and this could be used to map E-P loops and test their hypothesis.

We hypothesized that STAG2 might extrude promoter-anchored loops (P-loops) at myelination genes to enhance their transcription during development. As suggested by the reviewers, we examined whether the enhancer-promoter (E-P) loops were perturbed by the loss of STAG2. Using the H3K27ac peaks from OLs, we defined the enhancer regions by filtering out the peaks overlapping with promoters and identified E-P loops (Author response image 1A,B). Specifically, promoter regions were defined as ±2kb from TSS for all transcripts in the UCSC mm10 mouse reference genome. The H3K27ac narrow peaks called by MACS2 (v2.0.10) with a q-value cut-off of 0.05 was used to define the enhancer region after removing the peaks overlapping with the promoter region. Enhancer-promoter loops were identified if one end was anchored to a H3K27ac peak, and the opposite end was anchored to a promoter region. E-P loops anchored genes from Stag2f/y and Stag2f/y;NesCre cells were compared and classified into “Stag2f/yonly”, “Stag2f/y;NesCre-only”, and “Common” categories. The significant active genes (logCPM >0, FDR < 0.05) were used for gene expression comparison analysis. Gene expression changes of these categories were compared (Figure-for-reviewers 1C).

We found that genes only associated with E-P loops in the Stag2-depleted cells were more activated, compared to genes associated with E-P loops in both Stag2f/y and Stag2f/y;NesCre OLs. In other words, STAG1-mediated E-P loops might be more conducive for gene activation. During the revision process of our manuscript, a newly published study (Chu et al., 2022) showed that H3K27ac increased at STAG1-STAG2 switch sites and enhanced loop anchors in STAG2-knockdown melanoma cells. Consistent with this published study, our ChIP-seq analysis indicated a slight increase of H3K27ac binding at the promoters of up-regulated genes, but not at promoters of stable or down-regulated genes in Stag2-depleted cells (Figure 5B). STAG1 substitution in Stag2-depleted cells might induce ectopic E-P loops formation, elevated level of H3K27ac, and gene activation. Alternatively, gained E-P loops could result from weakened TAD boundary insulation as proposed previously (Lupianez et al., 2015).

We did not find that the genes losing E-P loops in the Stag2-depleted cells became more repressed than “Common” genes. Thus, loss of direct contact with non-promoter distal genomic regulatory elements do not always lead to transcription repression in Stag2-depleted cells. About 17% of the down-regulated genes had lost E-P loops in the Stag2-depleted OLs (Author response image 1D–F), but we did not find overrepresentation of myelination or cholesterol biosynthesis pathways in “Stag2f/y-only” group by GO analysis. Thus, loss of E-P loops is not the only underlying reason for the repression of myelination genes in Stag2-depleted cells.

There are, however, several major caveats with our analysis. One caveat is that H3K27ac peaks alone are not sufficient to define active enhancers. ChIP-seq experiments of enhancer binding transcription factors would provide more accurate features for identifying cell-type specific enhancer elements or “Super-enhancers” (Whyte et al., 2013). Another caveat is that, when we filtered out H3K27ac peaks overlapping with promoters, the promoter-to-promoter (PP) loops were left out. Active gene promoters, when interacting with each other, could also potentially serve as active genomic regulatory elements (Bonev et al., 2017; Chepelev et al., 2012; Hsieh et al., 2020). The contribution of STAG2-mediated P-P loops to the transcriptional regulation of OL genes was thus neglected from our H3K27ac peaks-based analysis. Due to these major caveats, we are not confident about the validity of our analysis and do not wish to include this analysis in the revised manuscript. This issue will be further investigated in the lab in future studies. We hope the reviewers would understand.

Author response image 1
Enhancer-promoter loops and E-P loop-anchored gene expression change.

(A) Scheme for identifying enhancer-promoter (E-P) loops. H3K27ac peaks at gene TSS ±2kb region are removed. E-P loops are anchored to promoters on one end and to the remaining H3K27ac peaks on the other end. (B) Aggregate peak analysis of E-P loops identified from Stag2f/y or Stag2f/y;NesCre OLs on the 10 kb Hi-C matrices using GENOVA. (C) Boxplot of the expression levels for genes in the indicated categories. Stag2f/y, genes only anchored to E-P loops in Stag2f/y cells; Stag2f/y;NesCre, genes only anchored to E-P loops in Stag2f/y;NesCre cells; Common, genes anchored to E-P loops in both Stag2f/y and Stag2f/y;NesCre cells. Significant active genes with FDR<0.05 were included in the analysis. Unpaired Wilcoxon test was used for the statistical analysis. **p < 0.01, ****p < 0.0001, ns, not significant. (D) Venn diagram showing the comparison of down-regulated gene list, up-regulated gene list in Stag2f/y;NesCre OLs, and the EP loop-anchored active gene lists specific to Stag2f/y or Stag2f/y;NesCre OLs, respectively. Gene counts are shown. (E) Snapshots of the contact maps of genomic locus of repressed genes with lost E-P loops and H3K27ac peaks in the Stag2f/y;NesCre cells. Tracks and narrow peaks from STAG2 and H3K27ac ChIP-seq as well as the loops are plotted below. Genes of interest are highlighted in red. The transcription directions are indicated by the arrows. Lost loop anchors and H3K27ac peaks are framed with grey dashed lines. (F) A snapshot of the contact maps of genomic locus of an activated gene Cybrd1 with gained E-P loop in the Stag2f/y;NesCre cells. Tracks and narrow peaks from STAG2 and H3K27ac ChIP-seq as well as the loops are plotted below. The Cybrd1 gene body and TSS are highlighted in red. The gained loop anchor is framed with grey dashed lines.

8. Figure 6D – A gene with a better-defined role in myelination than Pls1 would be preferable to use as an exemplar locus here.

As the reviewers suggested, we have replaced the original graph with the Hi-C matrix plotting of a genomic region surrounding Mal, a gene encoding a proteolipid localized in compact myelin. It has a well-established function in myelin biogenesis.

9. Please provide the list of DE genes, STAG2 genomic occupancy, or promoters anchored loops as a supplementary file. This will help the readability of the paper and will allow full disclosure of the dataset to the community. Furthermore, please provide the reviewer passkey for the GEO link.

As the reviewers suggested, we have added Supplementary File 2 that included the list of differential-expressed gene ID, the distance of nearby STAG2 peaks to their TSS, and the promoter-anchored loops in the wildtype and Stag2-depleted samples. To review GEO accession GSE186894, go to https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE186894 and enter token almhoaomxhkntsz into the box.

10. The authors are recommended to perform GO and pathway analysis using some other tools in addition to IPA, like GSEA. Also, since the pathways shown in the chart in Fig2Da and 4G are the top enriched pathways, it would be useful if the authors could provide a list of all the enriched pathways. Furthermore, please clarify what you used as background for the enrichment analysis.

As the reviewers suggested, we performed over-representation analysis for gene ontology (GO) using ClusterProfiler (Wu et al., 2021) in addition to the IPA analysis. The results were plotted in Figure 4—figure supplement 5 and 6. Ensheathment of neurons and gliogenesis were among the top over-represented biological pathways from downregulated genes in both datasets, consistent with a function of STAG2 in regulating myelin-related genes. The complete list of the enriched pathways related to the original Figure 2D and 4G are now provided in Supplementary File 1 and Figure 2—figure supplement 2. The complete gene lists were used as background for the enrichment analysis.

11. From the purified OL RNAseq, the authors found 271 down and 292 upregulated genes. Please clarify what these genes are. In Fig4G, you showed a pathway analysis of all the DE, and it is not clear which gene set has been used for FigS7E. It would be useful to characterize the UP and DOWN genes separately.

The down- and up-regulated genes are now listed in Supplementary File 2. The down-regulated genes of OL RNA-seq experiment were used for Ingenuity Pathway analysis in Figure S7E. We also did GO analysis using ClusterProfiler for the UP and DOWN genes separately in Figure 4—figure supplement 5 and 6. The down-regulated genes were enriched for oligodendrocyte differentiation and cholesterol metabolic process, while the up-regulated genes were enriched for cilium assembly and microtubule formation.

12. In the single-cell RNAseq, you have used nFeature_RNA(200-9500). This seems to be a very high threshold, with the risk to include cell doublets. Please disclose whether you can reproduce the same analysis using a lower threshold.

Based on the previous literature, a median number of about 7,000 genes can be detected per neuron by the Chromium Single Cell kit (10x Genomics, v3) (Armand et al., 2021; Yao et al., 2021), which was used in our study. We feel that the nFeature_RNA (200-9500) threshold is reasonable. On the other hand, we agree with the reviewers that our criteria may risk including some cell doublets. We thus used a lower threshold [nFeature_RNA (200-6500)] to reanalyze the data (Author response image 2). Most cell clusters from the initial analysis were rediscovered and the initial fundings were confirmed. We thus decided to keep the original figure.

13. It would be useful to test using the Stag2 ChIP data in isolated OLs, whether loop loss is observed preferentially on Stag2 occupied sites and how STAg2 occupancy correlates with loop score of up/down/no DE genes.

To check if loop loss was enriched at gene promoters occupied by STAG2, we integrated our STAG2 ChIP-seq, RNA-seq results of OLs, and loops anchored at DEGs and stable gene promoters. STAG2 occupied 90% (3,333 out of 3,689) gene promoters, which contained anchors of Stag2f/y-specific loops (i.e. lost loops). Thus, in most cases, loop loss does occur at STAG2 occupied sites. In the original submission, we showed that STAG2 is preferentially enriched at promoters of the down-regulated and stable genes, whereas it is less enriched at the promoters of up-regulated genes (Figure 5C). In our integration analysis, the down-regulated genes and the stable genes have frequent STAG2 occupancy at promoters anchored with loops, but a lower percentage of the up-regulated genes is occupied by STAG2 near the TSS. It is also consistent with the loop score comparison, which shows up-regulated gene promoters are associated with loops of lower scores (Figure 7—figure supplement 3B). We further compared the loop scores of loops anchored at DEGs with or without STAG2 enrichment (Figure 7—figure supplement 3C,D). The loops anchored at down-regulated genes with STAG2 binding had significantly higher loop scores, compared to those with no STAG2 binding. Surprisingly, this difference was still observed in Stag2-deleted cells, suggesting that the stronger looping observed at these gene promoters might be independent of STAG2 binding. By contrast, the loops anchored at up-regulated genes with STAG2 binding had lower loop scores. These differences became insignificant in the Stag2-deleted cells. The loop scores of loops anchored at stable genes were not affected by STAG2 occupancy.

Author response image 2
Transcriptome analysis of Stag2-depleted forebrains.

(A) t-SNE plot of cell clusters with the filtering criteria of nFeature_RNA (200-6500) in the single-cell analysis by Seurat. (B) t-SNE clustering as in (A) but colored by genotype. (C) Left panel: cell type composition and percentage as colored in (A). Right panel: percentage of cell clusters of the oligodendrocyte lineage. (D) Heatmap showing the expression levels of cell-type signature genes. (E) FeaturePlot of the representative genes (Mal and Nkx6-2) specifically suppressed in the late stages of OLs in Stag2-depleted forebrains. A maximum cutoff of 3 was used.

14. A more detailed Methods section is required: for example, how many replicates for the sequencing, sequencing depth, the sequence of the probes, PCR primers, antibodies (codes and quantity); more details for the OL isolation procedure. Methods for Cas9 mediated KO are missing.

As the reviewers suggested, we have added more details to the Methods section. The information of sequencing details is included in Figure 6—figure supplement 1E. The primer sequences, probe sequences, and the information of antibodies and software used in this manuscript are now included in the Key Resources Table.

15. Similarly, Figure Legends need more details for clarity

As suggested by the reviewers, we have added more details to the figure legends.

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https://doi.org/10.7554/eLife.77848.sa2

Article and author information

Author details

  1. Ningyan Cheng

    Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8764-552X
  2. Guanchen Li

    1. Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
    2. School of Life Sciences, Westlake University, Hangzhou, China
    3. Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China
    Contribution
    Formal analysis, Visualization, Methodology
    Competing interests
    No competing interests declared
  3. Mohammed Kanchwala

    Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Formal analysis, Visualization, Methodology
    Competing interests
    No competing interests declared
  4. Bret M Evers

    Division of Neuropathology, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Formal analysis, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5686-0315
  5. Chao Xing

    1. Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, United States
    2. Department of Bioinformatics, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, United States
    Contribution
    Formal analysis, Supervision, Funding acquisition, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1838-0502
  6. Hongtao Yu

    1. Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
    2. School of Life Sciences, Westlake University, Hangzhou, China
    3. Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China
    Contribution
    Conceptualization, Software, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    For correspondence
    yuhongtao@westlake.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8861-049X

Funding

National Natural Science Foundation of China (Project 32130053)

  • Hongtao Yu

National Institutes of Health (1R01GM124096)

  • Hongtao Yu

Cancer Prevention and Research Institute of Texas (RP160667-P2)

  • Hongtao Yu

Welch Foundation (I-1441)

  • Hongtao Yu

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

Acknowledgements

We thank Sung Jun Bae for taking the mouse photos and John Shelton for help with histology and in situ hybridization. We are grateful to Jeffrey McDonald for the sterol composition analysis, Richard Lu and Lu Sun for providing reagents and advice for the isolation of oligodendrocytes, and Applied Bioinformatics Laboratories at NYU Langone Health for the Hi-C analysis. We also thank the Yu lab members for helpful discussions and for reading the manuscript critically. This study was supported by the National Natural Science Foundation of China (Project 32130053), the U.S. National Institutes of Health (1R01GM124096), the Cancer Prevention and Research Institute of Texas (CPRIT) (RP160667-P2), and the Welch foundation (I-1441).

Ethics

All animals were handled in accordance with institutional guidelines of the Institutional Animal Care and Use Committee (IACUC; AAALAC unit number 000673) of University of Texas (UT) Southwestern Medical Center under the animal protocol number (APN) 102335.

Senior Editor

  1. Jessica K Tyler, Weill Cornell Medicine, United States

Reviewing Editor

  1. Adèle L Marston, University of Edinburgh, United Kingdom

Reviewers

  1. Adèle L Marston, University of Edinburgh, United Kingdom
  2. Andrew J Wood, University of Edinburgh, United Kingdom
  3. Simone Di Giovanni, Imperial College London, United Kingdom

Publication history

  1. Preprint posted: October 12, 2021 (view preprint)
  2. Received: February 12, 2022
  3. Accepted: August 11, 2022
  4. Accepted Manuscript published: August 12, 2022 (version 1)
  5. Version of Record published: September 1, 2022 (version 2)

Copyright

© 2022, Cheng et al.

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

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  1. Ningyan Cheng
  2. Guanchen Li
  3. Mohammed Kanchwala
  4. Bret M Evers
  5. Chao Xing
  6. Hongtao Yu
(2022)
STAG2 promotes the myelination transcriptional program in oligodendrocytes
eLife 11:e77848.
https://doi.org/10.7554/eLife.77848

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