1. Chromosomes and Gene Expression
  2. Neuroscience

HAT cofactor TRRAP modulates microtubule dynamics via SP1 signaling to prevent neurodegeneration

  1. Alicia Tapias
  2. David Lázaro
  3. Bo-Kun Yin
  4. Seyed Mohammad Mahdi Rasa
  5. Anna Krepelova
  6. Erika Kelmer Sacramento
  7. Paulius Grigaravicius
  8. Philipp Koch
  9. Joanna Kirkpatrick
  10. Alessandro Ori
  11. Francesco Neri
  12. Zhao-Qi Wang  Is a corresponding author
  1. Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Germany
  2. Faculty of Biological Sciences, Friedrich-Schiller-University of Jena, Germany
https://doi.org/10.7554/eLife.61531
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Cite this article
  1. Alicia Tapias
  2. David Lázaro
  3. Bo-Kun Yin
  4. Seyed Mohammad Mahdi Rasa
  5. Anna Krepelova
  6. Erika Kelmer Sacramento
  7. Paulius Grigaravicius
  8. Philipp Koch
  9. Joanna Kirkpatrick
  10. Alessandro Ori
  11. Francesco Neri
  12. Zhao-Qi Wang
(2021)
HAT cofactor TRRAP modulates microtubule dynamics via SP1 signaling to prevent neurodegeneration
eLife 10:e61531.
https://doi.org/10.7554/eLife.61531
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Abstract

Brain homeostasis is regulated by the viability and functionality of neurons. HAT (histone acetyltransferase) and HDAC (histone deacetylase) inhibitors have been applied to treat neurological deficits in humans; yet, the epigenetic regulation in neurodegeneration remains elusive. Mutations of HAT cofactor TRRAP (transformation/transcription domain-associated protein) cause human neuropathies, including psychosis, intellectual disability, autism, and epilepsy, with unknown mechanism. Here we show that Trrap deletion in Purkinje neurons results in neurodegeneration of old mice. Integrated transcriptomics, epigenomics, and proteomics reveal that TRRAP via SP1 conducts a conserved transcriptomic program. TRRAP is required for SP1 binding at the promoter proximity of target genes, especially microtubule dynamics. The ectopic expression of Stathmin3/4 ameliorates defects of TRRAP-deficient neurons, indicating that the microtubule dynamics is particularly vulnerable to the action of SP1 activity. This study unravels a network linking three well-known, but up-to-date unconnected, signaling pathways, namely TRRAP, HAT, and SP1 with microtubule dynamics, in neuroprotection.

Introduction

Neurodegenerative diseases are a range of incurable and debilitating conditions strongly linked with age, which represent a social and economic burden given the burgeoning elderly population. The key features of the brain are its adaptability and plasticity, which facilitate rapid, coordinated responses to changes in the environment, all of which require delicate brain functionality and maintenance. Progressive neuronal loss, synaptic deficits, disintegration of neuronal networks due to axonal and dendritic retraction, and failure of neurological functions are hallmarks of neurodegeneration (Palop et al., 2006; Gan et al., 2018). Molecular causes of neurodegeneration are believed to include protein misfolding and degradation, neuroinflammation, oxidative stress, DNA damage accumulation, mitochondrial dysfunction, as well as programmed cell death (Palop et al., 2006; Gan et al., 2018; Kurtishi et al., 2019; Burté et al., 2015; Chi et al., 2018).

Epigenetic mechanisms, including DNA methylation, histone modifications, non-coding or small RNAs, have been linked to brain development and neurological disorders, such as autism, intellectual disability (ID), and epilepsy, as well as neurodegenerative processes (Meaney and Ferguson-Smith, 2010; Berson et al., 2018; Christopher et al., 2017; Tapias and Wang, 2017). Histone acetylation, which is modulated by a range of histone acetyltransferase (HAT) families, is a major epigenetic modification controlling a wide range of cellular processes (Tapias and Wang, 2017; Choudhary et al., 2014). HATs and histone deacetylases (HDACs) maintain a proper acetylation kinetics of histones, yet also other protein substrates, which can coordinate histone dynamics over large regions of chromatin to regulate the global gene expression as well as target gene-specific regions or promoters (Vogelauer et al., 2000; Nagy and Tora, 2007). HAT-HDAC-mediated histone modifications have been suggested to play a role in brain functionality, including memory formation, mood, drug addiction, and neuroprotection (Meaney and Ferguson-Smith, 2010; Berson et al., 2018; Christopher et al., 2017; Delgado-Morales et al., 2017; Levenson and Sweatt, 2005; Renthal and Nestler, 2008). For example, the pharmacological inhibition of HDACs has been used for their anti-epileptic, anti-convulsive, and mood-stabilizing effects (Chiu et al., 2013). In addition, HDAC inhibitors or HAT activators have been employed in clinics to treat the neurological symptoms of neurodegenerative diseases and psychiatric disorders, as well as autism, memory loss, and cognitive function, although not always successfully (Christopher et al., 2017; Delgado-Morales et al., 2017; Selvi et al., 2010; Ganai et al., 2016). Genome-wide approaches have revealed global and local changes in multiple histone marks; yet, the impact and meaning of these alterations in the pathophysiological processes are obscure. A major hurdle is the lack of specificity of these pharmacological interventions causing adverse side effects in clinical treatment. Interestingly, alterations of the acetylation profiles have been found in neurodegenerative disorders including Huntington’s disease (HD) (reviewed in Valor, 2017), Amyotrophic lateral sclerosis (ALS) (reviewed in Garbes et al., 2013), spinal muscular atrophy (SMA) (Kernochan et al., 2005), Parkinson’s disease (PD) (Harrison et al., 2018; Park et al., 2016), and Alzheimer’s disease (AD) (Klein et al., 2019; Marzi et al., 2018). These findings highlight the involvement of HATs in the etiology of neurodegenerative processes, yet through various mechanisms (Cobos et al., 2019). Although widely discussed (Saha and Pahan, 2006; Konsoula and Barile, 2012), the role of histone acetylation and deacetylation in the adult central nervous system (CNS) and brain homeostasis remains largely unknown.

To gain insight into how the alteration of histone acetylation maintains neuronal homeostasis and prevents neurodegeneration, we used mouse and cellular models, in which the HAT essential cofactor TRRAP is deleted, so that the general HAT activity is disturbed. TRRAP (transformation/transcription domain-associated protein) interacts with E2F1 and c-Myc at gene promoters and is a critical component shared by several HAT complexes, including those from the GNAT and MYST families, which facilitates the recruitment of HAT complexes to target proteins for acetylation (Tapias and Wang, 2017; Knutson and Hahn, 2011). The complete deletion of Trrap in mice and cells is incompatible with the life of proliferating cells and mouse development, because of severe defects in the spindle checkpoint and cell cycle control (Herceg et al., 2001; Dhanalakshmi et al., 2004). A tissue specific deletion of Trrap in embryonic neural stem cells leads to a dysregulation of the cell cycle length which drives the premature differentiation of neuroprogenitors (Tapias et al., 2014). In humans, missense variants of TRRAP have been recently reported to associate with neuropathological symptoms, including psychosis, ID, autism spectrum disorder (ASD), and epilepsy (Cogné et al., 2019; Mavros et al., 2018). These basic and clinical studies point to a potential involvement of TRRAP in the manifestation of these neuropathies in humans. Because the known function of TRRAP in the mitotic checkpoint and cell cycle control does not apply to postmitotic cells, i.e., neurons, how TRRAP and its mediated HAT regulate adult neuronal fitness and affect neurodegeneration remains elusive.

In this study, we attempt to elucidate the molecular pathways that are governed by Trrap-HAT in postmitotic neural tissues. We find that Trrap deletion in the mouse model (Mus musculus) causes an age-dependent loss of existing neurons leading to neurodegeneration. We show that Trrap-HAT specifically regulates the Sp1 pathway that controls various neural processes, among which microtubule dynamics is particularly affected. Our study discloses the Trrap-HAT-Sp1 axis as a novel regulator of neuronal arborization and neuroprotection.

Results

Trrap deletion in Purkinje cells results in cerebellar degeneration

To study the role of histone acetylation and Trrap in postmitotic neurons, we generated two mouse models. First we crossed mice carrying the Trrap floxed allele (Trrapf/f) (Herceg et al., 2001) with Pcp2-Cre mice (Tg(Pcp2-cre)2Mpin) (Barski et al., 2000), to delete Trrap in Purkinje cells (Trrap-PCΔ). Trrap+/f mice with the Cre transgene, or Trrapf/f without the Cre transgene, were phenotypically normal, and thus they were used as controls. Trrap-PCΔ mice were born healthy and exhibited normal (in rotarod tests) or mild defective (in beam balance) motor coordination at young age (1–2 months). However, they displayed an evident miscoordination at mid age (3–6 months), which became more severe after 9 months (old group) (Figure 1A). By the age of 1 year, Trrap-PCΔ mice developed an age-dependent locomotor dysfunction characterized by signs of ataxia, namely impaired coordination and unsteady gait (data not shown).

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Deletion of Trrap in Purkinje cells causes neurodegeneration.

(A) The rotarod test and the beam balance were used to assess the motor coordination of control and Trrap-PCΔ mice. The left panel depicts the time that the mice stayed in the rod before falling off. The right panel shows the quantification of the time taken by mice to cross the beam. (N) Indicates the number of mice analyzed. Young: 1–2 months; mid age: 3–6 months; old: 9–12 months. (B) Immunostaining of the cerebellar sections of 9-month-old mice using an antibody against calbindin (red, Purkinje cells). ml: molecular layer; gcl: granule cell layer; PCl: Purkinje Cell layer. (C) The quantification of the number of Purkinje cells in the cerebellum at the indicated ages. The mm of the Purkinje cell layer analyzed are indicated in the table. Young: 1–2 months; mid age: 3–6 months; old: 9–12 months. (D) The representative of the western blot analysis for markers for Purkinje cells (calbindin), astrocytes (GFAP), oligodendrocytes (CNPase), and activated microglia (Galectin-3). (E) The quantification of western blots of the cerebellum (from D). Signal intensities are normalized to α-Tubulin. The numbers inside the bars indicate the number of mice analyzed. Young: 1 month; mid age: 4 months; old: 9 months. Co.: control; PCΔ: Trrap-PCΔ. Mean ± standard error of the mean is shown. Two-way ANOVA and Holm–Sidak test was performed for statistical analysis in (A), (B) and Student's t-test or one-way ANOVA for (C), (E). n.s.: not significant. *p≤0.05, **p≤0.01, ***p≤0.001.

Immunostaining of the Purkinje cell marker calbindin revealed a progressive loss of Purkinje cells in Trrap-PCΔ cerebella – starting from 3 months old mice – which became severe at 9 months (Figure 1B,C). Western blot analysis of mutant brains of different ages confirmed a progressive Purkinje cell loss, as judged by the downregulation of calbindin (Figure 1D,E). While there was no significant change in the expression of the oligodendrocyte marker CNPase, a progressive increase of the astrocyte marker GFAP, namely astrogliosis, and the activated microglia marker Galectin-3 were evident in mutant cerebella, both of which are hallmarks of neurodegeneration (Figure 1D,E). Immunostaining of the cerebella of 2-month-old and 9-month-old mice confirmed a loss of Purkinje cells and a great increase of astrocytes (GFAP+ signals), a sign of astrogliosis, in Trrap-PCΔ cerebella at old age, whereas Trrap-PCΔ cerebella of young mice were normal (Figure 1—figure supplement 1a,b). Also, TUNEL staining detected more cell death in all cerebellar lobes of old mice (Figure 1—figure supplement 1a,b). The microglia activation and astrogliosis could be due to a homeostatic response to Purkinje cell loss, but they closely resemble neurodegeneration.

Trrap deleted Purkinje cells exhibit age-dependent axonal swellings and dendrite retraction

To examine the neurodegenerative process, we analyzed Trrap-deleted Purkinje cells during early postnatal life. Immunostaining of brain sections using antibodies against calbindin and myelin-binding protein (MBP) detected axonal swellings of Trrap-deleted Purkinje cells readily at 1 month of age, prior to Purkinje cell loss (Figure 2A). Axonal swellings were generally myelinated at this age (Figure 2A), although a loss of myelination was observed occasionally in a few severe cases (data not shown). Furthermore, transmission electron microscopy (TEM) revealed the myelination index of Purkinje cell axons as normal in young (1-month-old) mice, but significantly lower, as judged by a higher g-ratio, in mid age (6-month-old) Trrap-PCΔ mice compared to controls (Figure 2B,C).

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Deletion of Trrap in Purkinje cells leads to defects in their axons and dendrites.

(A) Cerebellar sections from 1-month-old mice were stained with antibodies against Calbindin (green, Purkinje cells) and myelin-binding protein (MBP, red, Myelin sheets) and counterstained with DAPI. White arrows indicate axonal swellings. (B) Representative images of electromicrographs showing axon myelination in the cerebellar white matter of 6-month-old control and Trrap-PCΔ mice. M: Myelin sheet. (C) The quantification of the myelination index at the indicated ages by g-ratio, which is measured by ImageJ as ag-factor (the square-root of the area of the inner surface of an axon divided by the area of the outer surface including the myelin). Thus, a high g-ratio indicates a low myelination index. (D) Single Purkinje cells were analyzed by tracing the expression of the Confetti transgene (RFP). Representative Purkinje cell images of maximum intensity projection (MIP) from Z-stacks (upper panel) of 10-month-old mice are shown after reconstruction (lower panel) based on RFP expression in Trrap-PCΔ mice. (E) The quantification of the maximum radius after Sholl analysis, at the indicated ages, demonstrating that Purkinje cells retract their dendrites in Trrap-PCΔ mice. Young: 1–4 months; old: 10 months. (F) The graph shows the critical value measured by the Sholl analysis, at the indicated ages, indicative of the complexity of Purkinje cells. (G) The quantification of the molecular layer thickness of all cerebellar lobes. Young: 1–4 months; old: 10 months. Co.: control; PCΔ:Trrap-PCΔ. N: the number of mice analyzed. The numbers inside the bars indicate the number of cells analyzed. Mean ± standard error of the mean is shown. Student's t-test or one-way ANOVA was performed for statistical analysis. n.s.: not significant. *p≤0.05, **p≤0.01, ***p≤0.001.

To quantify the morphological changes of Trrap-deleted Purkinje cells, we generated Trrap-PCΔ mice expressing a Confetti transgene (B6.Cg-Tg(Thy1-Brainbow1.0)HLich/J) (thereafter Trrap-PCΔ-Confetti), which enables individual Purkinje cells to stochastically express one of four fluorescent proteins upon Cre expression (Snippert et al., 2010). This allows distinguishing single neuron morphology from adjacent cells and reconstructing the dendritic trees of individual Purkinje cells (Figure 2D). A Sholl analysis (Figure 2—figure supplement 1a) of Trrap-deleted Purkinje cells at young age (1–4 months) and at old age (10 months) showed a progressive decrease in the size of their dendritic trees without great effects on their complexity as judged by the critical value (Figure 2E,F, Figure 2—figure supplement 1b). Consistent with the Sholl analysis data, the molecular layer became thinner at older age compared to young age (Figure 2G, Figure 1—figure supplement 1b). These observations indicate that Trrap deletion does not affect arborization, but rather causes a retraction of already formed dendrites of neurons.

Trrap deletion changes transcriptional programs in neurons

The scarcity of Purkinje cell neurons in the Trrap-PCΔ cerebellar model limited the searching for the molecular mechanism related to the Trrap-HAT function. To gain a feasible approach, we devised another mouse model in which Trrap was deleted in a large subset of neurons, which would facilitate the molecular analysis of the HAT function in the brain. To this end, we crossed Trrapf/f mice with Camk2-Cre transgenic mice (Tg(Camk2a-cre/ERT2)2Gsc) to generate mice with a Trrap deletion in pyramidal neurons in the cortex and striatum of the forebrain (designated as Trrap-FBΔ). Trrap-FBΔ brains were normal and had an efficient deletion of Trrap already at day 10 of the postnatal life (P10) (Figure 3A, see below for protein analysis in Figure 4A,B, and for qPCR analysis Figure 4—figure supplement 1f). We then carried out RNA-seq and proteomic analyses using cortices and striata from P10 Trrap-FBΔ and control mice. Trrap deletion resulted in highly reproducible changes in the transcriptome of cortices and striata with 5090 and 4389 differentially expressed genes (DEGs) respectively (cutoff adjusted, p<0.05) (Figure 3B, Figure 3—figure supplement 1a, Supplementary file 1). The Trrap-FBΔ cortex and striatum shared 2695 common DEGs, corresponding to 52.9% and 61.4% of the respective tissues. The directionality of the changes was conserved in 99.3% of the genes (Figure 3C, Supplementary file 1). Among the common DEGs, 1122 upregulated and 1554 downregulated genes were overlapping between these two parts of the brain (Figure 3C). These results strongly suggest that similar mechanisms operate in the neurons from both brain regions. Gene ontology (GO) analyses of the common DEGs in RNA-seq data sets of both the cortex and striatum revealed alterations in multiple signaling pathways important for neuronal processes (Figure 3D, Source data 1A, Supplementary file 1). Intriguingly, about a half of the Top50 pathways were linked with microtubule dynamics and its related cellular processes (Figure 3D).

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Deletion of Trrap in pyramid neurons of the forebrain results in a progress degeneration of the cortex and striatum.

(A) Nissl staining of the coronal session of Trrap-FBΔ brain at postnatal day 0 (P0) and 10 (P10). Ctx: cortex; str: striatum; v: ventricle. (B) The Venn diagram depicts the overlap between the differentially expressed genes (DEGs) measured by RNA-seq in the cortex and striatum. The numbers refer to the DEGs in the indicated data sets. (C) Log2 of the fold changes of the 2695 common DEGs in Trrap-FBΔ cortex and striatum. (D) Top50 GO terms of the 2695 overlapping hits identified in the RNA-seq data set of the cortex and striatum. Note that microtubule dynamics related processes are highlighted in red. (E) Transcription factor binding site (TFBS) enrichment analysis of the 1261 common DEGs in aNSCs, the cortex, and the striatum identified by RNA-seq. (F) Luciferase assays using a Sp1-responsive construct. The graph shows the luciferase activity normalized by Bradford assay. N: the number of cell lines analyzed; Mock: empty vector, Sp1: overexpression; luc: luciferase. Co.: control; aNSCsΔ: Trrap-aNSCsΔ. n: the number of cell lines analyzed. Mean ± standard error of the mean is shown. Unpaired t-test was performed for statistical analysis. n.s.: not significant; *p≤0.05, **p≤0.01.

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Trrap regulates the expression of STMNs via Sp1.

(A) Western blot analysis of the Trrap deletion and expression of STMNs in the forebrain of indicated genotype at postnatal day 10 (P10). β-actin is a loading control. Co: control; FBΔ: Trrap-FBΔ. (B) The quantification of the expression of the indicated proteins in mutant forebrains measured by western blots are related value to adjacent controls after normalization to β-actin. N: the number of mice analyzed. The error bar presents the standard error. Paired t-test was used for statistical analysis. *p≤0.05, **p≤0.01. (C and D) Sagittal sections of 4-month-old Trrap-PCΔ mice were stained against STMN3 (green, C) and STMN4 (green, D), the Purkinje cell marker Calbindin (red) and counterstained with DAPI (blue). The right panel shows the average intensity of STMN3 or STMN4 in Purkinje cells normalized by the intensity in the neighboring cells (not Trrap-deleted). n: the number of cells analyzed; N = 4 mice analyzed. (E) ChIP analysis on the STMN3 and STMN4 promoters in control striata using antibodies against Sp1, AcH4, and IgG. qPCR analysis was performed to quantify the binding of the indicated factors to the promoter. The binding enrichment was calculated as fold enrichment over IgG. N = 3 mice analyzed. The primers that contain the Sp1 site for ChIP assays are marked in Figure 4—figure supplement 1g. (F and G) ChIP analysis on the proximity of STMN3 (F) and STMN4 (G) promoters in control and TrrapΔ aNSCs. Protein binding value is presented in percentage of input. The large error bars in Oct4 ChIP are due to an inclusion of a high value from one pair of samples. N = 3–4 mice analyzed. (H) Western blot analysis of STMN3 and STMN4 expression after siRNA-mediated knockdown of Sp1 in aNSCs. Oct4 is an Sp1 independent transcription factor control and β-actin controls loading. (C–H) Mean ± standard error of the mean is shown. Unpaired t-test was performed for statistical analysis. *p≤0.05, **p≤0.01.

Whole proteomic analysis of P10 Trrap-FBΔ cortices identified 122 out of 6919 proteins to be significantly altered after Trrap deletion (cutoff, q < 0.1) (Figure 3B, Figure 3—figure supplement 1b, Supplementary file 2). Notably, a comparison between RNA-seq and proteomics data sets showed that 85% of the proteins altered by Trrap deletion, i.e., 33 upregulated and 71 downregulated genes, were altered in the same way at the RNA level, which resembled 100% directionality (Figure 3B, Figure 3—figure supplement 1c, Supplementary file 2). These results indicate that most changes in the proteome were due to changes in the transcriptome.

Trrap deletion alters Sp1 pathway

TRRAP is a cofactor interacting with various transcription factors and recruits HAT activity to their target gene promoters. To understand through which transcription factors Trrap was mediating its function, we performed a transcription factor binding site (TFBS) enrichment analysis on the common DEGs in Trrap-deleted cortices and striata. We found that most of these transcriptional changes after Trrap deletion were mediated mainly by limited transcription factors, among which transcription factors Sp1 and TCFs appeared to be the most relevant upstream factors (Figure 3E, Supplementary file 3). They mediated Trrap-dependent changes not only upstream of the common DEGs, but also upstream of the DEGs from each data set of cortices and striata (Supplementary file 3). Transcription factors TCF1, TCF3, and NFAT are known effectors of the Wnt signaling pathways (Cadigan and Waterman, 2012) and were indeed downstream of the Sp1-mediated transcription regulation upon Trrap deletion (Supplementary file 1). Hence, our data suggest that Sp1 is likely the main regulator for all these changes in Trrap-deleted brains.

The Sp1 pathway is a conserved transcriptomic network in Trrap-deleted neural cells

Sp1 is a key transcription factor capable of regulating many cellular processes, including proliferation, cell differentiation, apoptosis, immune responses, DNA damage responses, and chromatin remodeling (Li and Davie, 2010). We attempted to analyze the transcriptional activity of Sp1 in the absence of Trrap. To achieve this, we had to adopt an in vitro culture approach and used replicating adult neural stem cells (aNSCs) from Trrapf/f mice expressing the CreERT2 transgene (Rosa26-CreERT2 Gt(ROSA)26Sortm1(cre/ERT2)Tyj) (designated as Trrap-iΔ). Addition of 4-hydroxytamoxifen (4-OHT) in cultured Trrap-iΔ NSCs induced an efficient deletion of Trrap (Figure 3—figure supplement 2a,b). We first validated the transcriptome of aNSCs in comparison with that of Trrap-FBΔ cortex and striatum and found 1261 common DEGs among these three samples. The directionality is very conserved in 93.1% between the cortex and aNSCs (Figure 3—figure supplement 2c,d, Supplementary file 1). Intriguingly, TFBS analysis revealed a remarkable commonality of DEGs among Trrap-deleted cortices, striata, and aNSCs as the transcriptional targets of Sp1 (Figure 3—figure supplement 2e, Supplementary file 3). Thus, Trrap-HAT regulates a very conserved transcriptomic network in different brain regions as well as in NSCs. Using these cells, we then performed a luciferase reporter assay to investigate whether Sp1 is directly regulated by Trrap and detected a dramatic decrease in Sp1 activity in Trrap-deleted NSCs, compared to control cells. Strikingly, ectopic overexpression of Sp1 in Trrap-deleted cells failed to activate the Sp1-reporter (Figure 3F). These data indicate that Trrap is indeed required for Sp1-mediated transcriptional activation.

Alteration of Sp1-targets after Trrap deletion

RNA-seq analysis suggests that Trrap ablation leads to changes of the Sp1-dependent transcription regulation of its downstream targets in various neurological processes (Source data 1A). We then compared the Sp1 targets identified in our transcriptome (DEGs) analyses (Supplementary file 1) with Sp1 targets identified by ChIP-seq from the Harmonizome database (Rouillard et al., 2016) (https://amp.pharm.mssm.edu/Harmonizome/gene_set/SP1/ENCODE+Transcription+Factor+Targets). We found that a high degree of the DEGs from Trrap-deleted brains as well as aNSCs were putative Sp1 targets (Figure 3—figure supplement 2f, Supplementary file 4). GO analysis of the common DEGs revealed that more than 30% of the Top50 pathways under the control of Sp1 were linked with microtubule dynamics-related cellular processes (Figure 3—figure supplement 2g, Supplementary file 4).

To further examine how Trrap affects HATs at target gene promoters, we performed a ChIP-seq study of acetylated histones H3 and H4 using Trrap-deleted aNSCs. Depletion of Trrap led to a downregulation of 2274 AcH3 and 1355 AcH4 peaks that were associated with coding genes (equivalent to 10% of the most depleted regions in TrrapΔ versus controls) (Figure 4—figure supplement 1a, Supplementary file 5). Only 12.6% and 10.2% respectively of these depleted peaks correlated with changes in the RNA level of the corresponding genes in Trrap-FBΔ brains (Figure 4—figure supplement 1a, Supplementary file 5). ChIP-seq analyses revealed no significant difference of AcH3 on Sp1-site between control and Trrap-deleted aNSCs, whereas H4Ac on Sp1-site in control (mean = 12.37) is slightly lower than in Trrap mutants (mean = 12.54) (Figure 4—figure supplement 1b). Interestingly, the downregulated genes had a significantly lower acetylation level of H3 and H4 in the Sp1 promoter area in Trrap deleted cells (Figure 4—figure supplement 1c). Twenty-two genes exhibited downregulated histone H3 and H4 acetylation and were also downregulated in the RNA-seq from brains. Among them, 11 genes were Sp1 targets according to the Harmonizome database (Supplementary file 5). The microtubule dynamics proteins STMN3 (SCLIP) and STMN4 (RB3) are of special interest (Figure 4—figure supplement 1d,e), because microtubule dynamics have been proposed to be involved in brain homeostasis (Chauvin and Sobel, 2015; Dubey et al., 2015) and defects in microtubule dynamics often cause axonal swellings and dendrite retraction in postmitotic neurons (Dubey et al., 2015; Voelzmann et al., 2016). Together with the high incidence of the dysregulated processes associated with microtubule dynamics, which are regulated by Sp1 (Figure 3E, Figure 3—figure supplement 2f), the finding of these two microtubule destabilizing proteins postulates this particular cellular process as the main route affected by Trrap deletion in the brain.

Trrap-HAT mediates Sp1 transcriptional control of microtubule dynamic genes

The microtubule destabilizing proteins STMN3 and STMN4 are members of the Stathmin protein family (Chauvin and Sobel, 2015). STMNs 3 and 4 were found within the Top30 changes in our RNA-seq and proteomics data sets of brain samples (Source data 1B and C, Supplementary file 2). qPCR analysis confirmed a great downregulation of these genes in Trrap deleted cells of forebrain tissues (Figure 4—figure supplement 1f). Western blot analysis also confirmed a great reduction of both STMN3 and STMN4 proteins in Trrap-deleted forebrains at P10 (Figure 4A,B). We next turned our analysis to our neurodegeneration model Trrap-PCΔ mice. Co-staining of STMN3 or STMN4 with calbindin in the brain sections detected a significant decrease of both proteins in Trrap-deleted Purkinje cells compared to controls (Figure 4C,D). To explore the mechanism, ChIP-seq was performed and showed that the level of AcH3 and AcH4 at the promoters of these genes was greatly reduced (Figure 4—figure supplement 1g). To validate the RNA-seq and ChIP-seq data, we performed ChIP experiments in brain samples and found both Sp1 binding and histone H4 acetylation were enriched at the STMN3 and STMN4 promoters in controls (Figure 4E). Upon Trrap deletion there was a dramatic decrease in Sp1 binding, as well as in the acetylation of histone H4 in the STMN3 and STMN4 promoters (Figure 4F,G). We also noted that Trrap deletion did not change the H3K4m2 level in the STMN3 promoter, yet decreased mildly in the STMN4 promoter. Moreover, Trrap deficiency did not compromise binding of Sp1-unrelated transcription factor Oct4 at the promoter proximities of these STMNs genes (Figure 4F,G). These data together indicate an essentiality of Trrap for loading Sp1 and HATs to the promoters of these Sp1 target genes. Furthermore, siRNA-mediated Sp1 knockdown indeed decreased expression of STMN3 and STMN4 proteins (Figure 4H). These results demonstrate that the Trrap-HAT-Sp1 axis directly controls the expression of STMN3 and STMN4 in neurons.

Functional test of STMNs in neuronal defects by Trrap deficiency

Stathmin family proteins STMN3 and STMN4 are mostly or exclusively expressed in the nervous system where they control microtubule dynamics, an essential process for neuronal differentiation, morphogenesis, and functionality (Chauvin and Sobel, 2015; Dubey et al., 2015). To investigate if Trrap deficiency especially affecting neuronal homeostasis was indeed mediated by STMNs, first we knocked down Trrap by siRNAs in primary neurons isolated from wild-type E16.5 cortex (Figure 5—figure supplement 1a), co-transfected with GFP or STMNs 3- or 4-expressing vectors at day 6 in vitro culture (DIV6), designated day 0 post transfection (DPT0), and analyzed the neuronal phenotype at DPT6 (Figure 5—figure supplement 1b). Immunofluorescence analysis revealed that the Trrap knockdown evidently reduced the neurite length and the branching numbers of neurons (Figure 5A–C). This was also confirmed by the IncuCyte assay at DPT6 (Figure 5—figure supplement 1c,d), which allows scoring a large number of cells. These findings indicate that Trrap deficiency also compromises neuronal arborization in vitro. To examine whether the downregulation of STMNs is indeed responsible for the neuropathies of Trrap deleted neurons, we ectopically expressed STMN3 in Trrap knockdown neurons (Figure 5—figure supplement 1e). Intriguingly, ectopic expression of STMN3 is sufficient to rescue these neuronal defects caused by Trrap knockdown (Figure 5A,B). Similarly, ectopic expression of STMN4 also corrected the neurite length and branching defects in Trrap knockdown primary neurons (Figure 5A,C). Interestingly, we note a co-upregulation of both STMNs when either STMN3 or STMN4 was overexpressed (Figure 5—figure supplement 1e), which suggests a co-stabilization or cooperative action of both STMNs in microtubule dynamics in the brain; yet the underlying mechanism requires further investigation. Taken together, these experiments demonstrate that Trrap prevents neuropathy by regulating Stathmin associated with microtubule dynamics.

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Trrap deletion causes neuronal defects in vitro that can be rescued by ectopic expression of STMN3.

(A) Immunofluorescent images of primary neurons isolated from E16.5 forebrains at 6 days post co-transfection of siRNA (siScramble, siTrrap-4) with GFP, or with the GFP-STMN3, or with GFP-STMN4 expressing vector. (B) The neurite length after the Trrap knockdown and rescue by the STMN3 or STMN4 overexpression was analyzed at 6 days post co-transfection of the indicated siRNA with the GFP-, STMN3-, or STMN4-expressing vector. The neurite length is measured with NeuronJ (ImageJ plug-in). Only GFP-positive neurons (indicative of transfection) were analyzed. Each bar represents the data from four to six mouse embryos; the experiments were repeated more than three times. Unpaired t-test was performed for statistical analysis. **p≤0.01, ***p≤0.001, n.s., not significant. (C) The neurite branching after the Trrap knockdown and rescue by STMN3 or STMN4 overexpression was analyzed at 6 days post co-transfection of the indicated siRNA with the GFP, STMN3-, or STMN4-expressing vector. The neurite length is measured with NeuronJ (ImageJ software). Only GFP-positive neurons were scored and are shown. Each bar represents the data from four to six mouse embryos; the experiments were repeated more than three times. Unpaired t-test was performed for statistical analysis. *p≤0.05, **p≤0.01, ***p≤0.001, n.s., not significant. (D) Working model of Trrap-HAT-Sp1 in brain homeostasis and neurodegeneration. The Trrap deletion compromises HAT to acetylate histones resulting in insufficient binding of Sp1 and the subsequent downregulation of target genes involved in microtubule dynamics (STMNs). The dysregulation of STMNs provokes the axonal swelling, declines of neurite lengths and branching of postmitotic neurons, ultimately, leading to defective neuronal homeostasis and neurodegeneration.

Discussion

The maintenance of neuron function and numbers are important for proper adult brain homeostasis. A loss of control of these processes prompts age-related neurological deficits, including neurodegeneration. Various mechanisms, including protein folding/stability, neuroinflammations, or DNA damage accumulation, have been implicated in the maintenance of brain homoeostasis and functionality. The acetylation modulations of proteins have been proposed to be important for the maintenance and function of neural cells (Tapias and Wang, 2017) as well as in neurodegenerative disorders, including HD, AD, PD, and ALS, yet through different mechanisms (Cobos et al., 2019). These studies highlight the involvement of HATs in the etiology of neurodegenerative processes. Despite the assumption that HAT/HDAC conducts a general regulatory function in transcription, how acetylation and deacetylation modulate the functionality, fitness, and even the survival of adult neurons is largely unknown. The current study shows that the HAT cofactor TRRAP is vital for preventing neurodegeneration of the Trrap-PCΔ mouse model. Trrap is an essential gene in proliferating cells because a Trrap null mutation causes lethality in cells and mice (Herceg et al., 2001). Unexpectedly, the deletion of Trrap in postmitotic neural cells (i.e., Purkinje neurons) is compatible with life, but elicits a full range of age-dependent neurodegenerative symptoms – axonal demyelination, dendrite retraction, progressive neuronal death, reactive astrogliosis, and the activation of microglial cells. Trrap deletion in non-dividing neurons, avoiding lethality, allows the identification of Sp1 as a specific master regulator, which is under the control of Trrap-HAT, to ensure proper neuronal arborization and to prevent neuron loss (Figure 5D). Although our omics studies detect a range of genes that have been altered, microtubule dynamics seems to be particularly vulnerable to Trrap deletion, because the major changes in the Trrap deleted brains are the processes involving microtubule dynamics and that ectopic expression of microtubule destabilizing proteins STMNs can largely ameliorate the arborization defects of Trrap-deficient neurons.

The TFBS enrichment analysis of these commonly dysregulated genes in Trrap-deleted brains points to selective transcription programs of the Trrap-HAT downstream. Our integrated omics analyses revealed a remarkable commonality of Trrap-HAT-regulated genes via Sp1 in the neurons of the cortex and striatum, and even with neural stem cells. Intriguingly, the Sp1 pathway is on the top of the changes by Trrap deletion and Trrap is required for the Sp1 transcriptional activity. The action of Trrap-HAT in postmitotic neurons is to regulate, via Sp1, the expression of neuroprotection and brain homeostasis genes, among which microtubule dynamics is most affected (Dubey et al., 2015; Noelanders and Vleminckx, 2017). Although we have not completely confirmed that all these molecular pathways governed by Trrap-HAT-Sp1 in pyramidal neurons (Trrap-FBΔ mice) would be identical in Purkinje neurons (due to the technical limitation), STMN3/4 were indeed downregulated in Trrap-PCΔ models. In agreement with the ChIP data, knockdown of Sp1 repressed STMN3/4 expression. Thus, it is likely that the Sp1-mediated specific transcriptome could also function in the prevention of neurodegeneration.

Sp1 is a master transcription factor binding to the GC box of promoters and can also regulate by itself. It has many downstream target genes, among which the regulation of cancer and cell proliferation have been well studied (Li and Davie, 2010; Vizcaíno et al., 2015; Suske, 2017). However, the upstream regulatory mechanism of Sp1 has not been defined previously. Here we show that Trrap-HAT is upstream of Sp1 and has a specific regulatory role in the Sp1-mediated transcription. Although Sp1 has been reported to bind to the promoter of some genes in neural cells, such as Slit2 (Saunders et al., 2016), P2 × 7 (García-Huerta et al., 2012), and Reelin (Chen et al., 2007), it has not been linked directly with neural development and degeneration. Our transcriptome analyses reveal that many neurological processes are indeed regulated by Sp1 downstream targets, many of which are connected with microtubule dynamics (see Figure 3D, Figure 3—figure supplement 2g, Source data 1A). For example, Trrap facilitates Sp1 binding to the gene loci of the microtubule destabilizing proteins STMN3 and STMN4. These findings are particularly interesting, because Sp1 has been implicated in neurodegeneration disorders; yet previously published data are often controversial. A GWAS analysis detected Sp1 among candidates mediating transcriptional activity changes in AD and PD patients (Ramanan and Saykin, 2013). Sp1 seems to prevent neurotoxicity in HD (Dunah et al., 2002), whereas others showed that a downregulation is protective in HD development (Qiu et al., 2006). Sp1 is found upregulated in AD patients and also in an AD mouse model (Citron et al., 2008); however, when it was chemically inhibited, memory deficits were even enhanced in AD transgenic mice (Citron et al., 2015), ruling out an instrumental role of Sp1 in AD. These controversial findings are perhaps not surprising, because the expression changes of the Sp1 gene and protein can be regulated by multiple mechanisms, such as transcriptional regulation, epigenetics, and posttranscriptional modifications (Li and Davie, 2010), or can be secondary to the manifestation of disease processes in a very heterogenous genetic background in human studies.

Our analyses identify the Trrap-HAT-Sp1 axis as a specific regulator of the microtubule dynamics process. Microtubules dynamics, coordinated, but mainly, by a destabilizing processing of microtubules, is a crucial regulator of neurite outgrowth, the maintenance of neuronal morphology and cargo transport along axons, and thereby of brain homeostasis (reviewed in Chauvin and Sobel, 2015; Voelzmann et al., 2016). The destabilizing factors STMNs play an important yet distinct function in neuronal homeostasis. Defects in microtubule dynamics can cause axonal swellings and dendrite retraction; both processes of neurodegeneration and the misregulation of STMNs have been associated with neuron abnormalities (Dubey et al., 2015; Voelzmann et al., 2016). Trrap deletion greatly downregulates STMN3 and STMN4 in Purkinje cells and also in the forebrains. This finding is particularly interesting because that STMN3 and STMN4 express preferentially high during adult life than STMN1 and STMN2 (SCG10), suggesting their important functions in neuronal homeostasis (Chauvin and Sobel, 2015; Voelzmann et al., 2016; Ozon et al., 1998). The repression of STMN3 suppresses the Purkinje cell neurite growth and arborization; when overexpressed, it promotes dendritic elongation and branching (Poulain et al., 2008). In contrast to STMN3, the role of STMN4 in neuronal morphogenesis and arborization has not been extensively studied. Moreover, STMN1 knockout mice showed an age-dependent axonopathy, characterized by a progressive degeneration of axons and demyelination (Liedtke et al., 2002). Also, an overexpression of STMN2 has been shown to enhance neurite outgrowth by favoring microtubules disassembly (Morii et al., 2006). Therefore, it is plausible that downregulation of STMN3 and STMN4 is responsible for axonal swellings and the dendrite retraction of Trrap-deleted Purkinje cells. This conclusion is further supported by our genetic knockdown and rescue experiments showing that an ectopic expression of either STMN3 or STMN4 can effectively rescue the defects of neurite length and branching of primary cortical neurons, which are caused by Trrap knockdown. However, an in vivo rescue of neuron loss in Trrap-PCΔ mice has been impossible due to a technique limitation. Nonetheless, our data demonstrate an instrumental role of the Trrap-Sp1-mediated transcriptional control of microtubule dynamics, likely via STMNs, in neuronal morphogenesis and preventing neurite retraction.

Very recently, human patients carrying de novo mutations of TRRAP are reported to be associated with neuropathological symptoms, such as ID, ASD, and epilepsy (Cogné et al., 2019; Mavros et al., 2018). Although molecular pathways have not been investigated in these genetic studies, our findings hint at the Sp1 transcription regulation network by HAT being a plausible mechanism. This current study is the first report demonstrating that the general transcriptional regulator Sp1 is specifically under the control of Trrap-HAT. Our findings propose that the Trrap-HAT-Sp1 axis orchestrates the program of neuronal microtubule dynamics and neuron morphogenesis and, possibly, neuron survival.

Materials and methods

Key resources table
Reagent type
(species) or
resource		DesignationSource or
reference		IdentifiersAdditional
information
Gene (M. musculus)TrrapGenebankMGI:MGI:2153272
Gene (M. musculus)Sp1GenebankMGI:MGI:98372
Gene (M. musculus)STMN3GenebankMGI:MGI:1277137
Gene (M. musculus)STMN4GenebankMGI:MGI:1931224
Strain, strain background (M. musculus)Trrapf/f; Pcp2-CreThis paperTrrap deletion in Purkinje cells;
M. musculus, male and female;
Please refer to ‘Materials and methods’ in the paper, Section ‘Mice’
Strain, strain background (M. musculus)Trrapf/f; Camk2-CreThis paperTrrap deletion in forebrain;
M. musculus, male and female;
Please refer to ‘Materials and methods’ in this paper, Section ‘Mice’
Strain, strain background (M. musculus)Trrapf/+; Rosa26-CreERT2
Trrapf/f; Rosa26-CreERT2		This paperTrrapf/+ acts as a control to Trrapf/f
Trrap deletion in adult neural stem cells;
M. musculus, male and female;
Please refer to ‘Materials and methods’ in this paper, Section ‘Mice’
Strain, strain background (M. musculus)B6.Cg-Tg(Thy1-Brainbow1.0)HLich/J (R26R-Confetti); Trrapf/f; Pcp2-CreThis paperTracing of the single Purkinje cells;
M. musculus, male and female;
Please refer to ‘Materials and methods’ in this paper, Section ‘Mice’
Genetic reagent (M. musculus)Lipofectamine 2000InvitrogenCat#: 11668027siTrrap and Plasmid co-transfection;
M. musculus
Genetic reagent (M. musculus)Lipofectamine RNAiMAXInvitrogenCat#: 13778075siSp1 transfection;
M. musculus
Cell line (M. musculus)Trrap-aNSCThis paperPrimary cell line;
M. musculus;
Please refer to ‘Materials and methods’ in this paper, Section ‘aNSC cell culture’.
Cell line (M. musculus)E16.5 cortical neuronThis paperPrimary cell line;
M. musculus;
Please refer to ‘Materials and methods’ in the paper, Section ‘Isolation and culture of murine primary neurons’.
Cell line (M. musculus)Neuro-2a Neuroblastoma cellsPMID:4534402ATCC CCL-131Cell line;
M. musculus
Transfected construct (M. musculus)ON-TARGETplus siRNA Reagents -Mouse
(siScramble)		Horizon DiscoveryCat#: D-001810-10-05UGGUUUACAUGUCGACUAA;
M. musculus
Transfected construct (M. musculus)siTrrap-1Horizon DiscoveryCat#: LQ-051873-01-0005CAAAAGUAGUGAACCGCUA;
M. musculus
Transfected construct (M. musculus)siTrrap-2Horizon DiscoveryCat#: LQ-051873-01-0005CCUACAUUGUGGAGCGGUU;
M. musculus
Transfected construct (M. musculus)siTrrap-3Horizon DiscoveryCat#: LQ-051873-01-0005GCCAACUGUCAGACCGUAA;
M. musculus
Transfected construct (M. musculus)siTrrap-4Horizon DiscoveryCat#: LQ-051873-01-0005CGUACCUGGUCAUGAACGA;
M. musculus
AntibodyAnti-Calbindin (Mouse Monoclonal)SigmaCat#:C9848 RRID:AB_476894IF:1:300
WB: 1:1000
AntibodyAnti-GFAP (Mouse Monoclonal)AgilentCat#:G3893 RRID:AB_477010IF:1:300
WB: 1:1000
AntibodyAnti-MBP (Mouse Monoclonal)MilliporeCat#:MAB384 RRID:AB_240837IF:1:300
AntibodyAnti-GFP (Rabbit Monoclonal)Cell Signaling TechnologyCat#:2956
RRID:AB_1196615		IF: 1:200
AntibodyAnti-GFP (Mouse Monoclonal)Santa CruzCat#:sc-390394IF:1:200
WB: 1:400
AntibodyAnti-Sp1 (Mouse Monoclonal)Santa CruzCat#:sc-17824
RRID:AB_628272		IF: 1:50
AntibodyAnti-Sp1 (Rabbit Polyclonal)MilliporeCat#:07–645
RRID:AB_310773		WB:1:1000
ChIP: 1:80
AntibodyAnti-STMN3 (Rabbit Polyclonal)Proteintech,Cat#:11311–1-AP
RRID:AB_2197399		IF:1:100
WB:1:1000
AntibodyAnti-STMN4 (Mouse Monoclonal)Santa CruzCat#:sc-376829IF:1:100
WB:1:1000
AntibodyAnti-Tuj1(Mouse Monoclonal)CovanceCat#: MMS-435P
RRID:AB_2313773		IF:1:400
AntibodyAnti-CNPase (Mouse Monoclonal)SigmaCat#: SAB4200693IF:1:1000
AntibodyAnti-Galectin3 (Rat Monoclonal)eBioscienceCat#:14-5301-82
RRID:AB_837132		WB:1:1000
AntibodyAnti-a-tubulin (Mouse Monoclonal)SigmaCat#:sc-32293
RRID:AB_628412		WB: 1:5000
AntibodyAnti-TRRAP (Mouse) clone TRR-2D5EuromedexID: IG-TRR-2D5WB:1:1000
AntibodyAnti-TRRAP (Mouse) clone TRR-1B3EuromedexID: IG-TRR-1B3ChIP: 1:40
AntibodyAnti-β-actin (Mouse Monoclonal)SigmaCat#:A5441
RRID:AB_476744		WB:1:3000
AntibodyAnti-AcH3 (Rabbit Polyclonal)MilliporeCat#:06–599
RRID:AB_2115283		ChIP: 1:150
AntibodyAnti-AcH4 (Rabbit Polyclonal)MilliporeCat#:06–866
RRID:AB_310270		ChIP: 1:150
AntibodyAnti-H3K4me2(Rabbit Polyclonal)AbcamCat#: ab7766
RRID:AB_2560996		ChIP: 1:100
AntibodyH3 (Rabbit Monoclonal)AbcamCat#: ab1791
RRID:AB_302613		ChIP: 1:150
AntibodyH4 (Rabbit Polyclonal)AbcamCat#: ab7311
RRID:AB_305837		ChIP: 1:150
AntibodyOct-4 (Rabbit Monoclonal)Cell SignalingCat#: 2840
RRID:AB_2167691		ChIP: 1:80
WB:1:1000
AntibodyIgG (Rabbit Polyclonal)SigmaCat#: I8140
RRID:AB_1163661		ChIP: 1:1500 (2 µg antibody)
Recombinant DNA reagentEF1a-GFP-P2A-STMN3-Poly(A) (plasmid)This paperSTMN3 overexpression plasmid;
M. musculus;
Please refer to‘Materials and methods’ in this paper, Section ‘Construction of STMNs expression vectors’.
Recombinant DNA reagentEF1a-GFP-Poly(A)-EF1a-STMN4-Poly(A) (plasmid)This paperSTMN4 overexpression plasmid;
M. musculus;
Please refer to ‘Materials and methods’ in this paper, Section ‘Construction of STMNs expression vectors’.
Recombinant DNA reagent−111 hTF m3AddgeneCat#: 15450Sp1 activity reporter;
H. sapiens
Recombinant DNA reagentpN3-Sp1FLAddgeneCat#: 24543Sp1 overexpression reporter;
H. sapiens
Sequence-based reagentSp1 primerPrimerBankID 7305515a1Fwd, 5’-GCCGCCTTTTCTCAGACTC-3’; Rev, 5’-TTGGGTGACTCAATTCTGCTG-3’
Sequence-based reagentSTMN3 primerPrimerBankID 6677873a1Fwd, 5’-CAGCACCGTATCTGCCTACAA-3’; Rev, 5’-GTAGATGGTGTTCGGGTGAGG-3’;
Sequence-based reagentSTMN4 primerPrimerBankID 9790189a1Fwd, 5’-ATGGAAGTCATCGAGCTGAACA-3’; Rev, 5’-GGGAGGCATTAAACTCAGGCA-3’.
Sequence-based reagentSTMN3 promoter primerThis paperFwd, 5’-CTTGCTACTGCATCAGGCGA-3’; Rev, 5’-AGCCTAGGGGATCATGGGAC-3’;
Sequence-based reagentSTMN4 promoter primerThis paperFwd, 5’-TCGCTTTGGAAACCGGACTG-3’;
Rev, 5’-TTTGTTTAAAACCCCCGCCC-3’.
Commercial assay or kitIncucyte S3Sartorius AGProduct Code: 4695For neurite detection and quantification
Commercial assay or kitRNeasy Lipid Tissue Mini KitQiagenCat #: 74804
Commercial assay or kitRNAeasy Mini KitQiagenCat #: 74104
Commercial assay or kitLightCycler 480 Real-Time PCR SystemRocheProduct No. 05015243001
Commercial assay or kitRNA 6000 nano kitAgilentCat #: 5067–1511
Commercial assay or kitTruSeq Stranded mRNA KitIlluminaCat #: 20020594
Commercial assay or kitDual-Glo Luciferase Assay SystemPromegaCat# E2920
Commercial assay or kitQiaQuick PCR Purification KitQiagenCat# 28106
Commercial assay or kitFragment AnalyzerAgilentCat#: M5310AA
Commercial assay or kitNextSeq500 platformIlluminaRRID:SCR_014983
Commercial assay or kitTruSeq ChIP Sample Preparation KitIlluminaCat#: IP-202–1024
Chemical compound, drugEpoxy resin ‘Epon’SERVAGlycid ether 100 for electron microscopy
Chemical compound, drugcOmplete, Mini, EDTA-freeRocheCat#: 04693159001Protease Inhibitor
Chemical compound, drugPhosSTOPRocheCat#: PHOSS-ROPhosphatase Inhibitor
Chemical compound, drugprotein-A-conjugated magnetic beadsInvitrogenCat#: 10003D
Chemical compound, drugprotein-G-conjugated magnetic beadsInvitrogen10001D
Chemical compound, drugPlatinum SYBR Green qPCR SuperMix-UDGQiagen11733046
Software, algorithmNeuronJ Plug-in by ImageJ softwareNational Institutes of HealthNeurite tracing and quantification
Software, algorithmFiji plugins Simple Neurite TracingNational Institutes of HealthSholl analysis
Software, algorithmbcl2FastQIlluminaRRID:SCR_015058Version 1.8.4
Software, algorithmSTARPMID:23104886RRID:SCR_015899Version 2.5.4b;
RNA sequence mapping parameters:
--alignIntronMax 100000 --outSJfilterReads Unique --outSAMmultNmax 1 --outFilterMismatchNoverLmax 0.04
Software, algorithmFeatureCountsPMID:24227677RRID:SCR_012919Version 1.5.0; parameters: metafeature mode, stranded mode ‘2’, Ensembl 92 annotation
Software, algorithmENSEMBL annotationPMID:31691826RRID:SCR_002344Release 92 for Mus musculus
Software, algorithmMultiQCPMID:27312411RRID:SCR_014982Version 1.6; RNA sequence quality assessment of the raw input data, the read mapping and assignment steps
Software, algorithmR package DESeq2PMID:25516281RRID:SCR_015687Version 1.20.0;
Analysis of differential expressed genes in pairwise comparisons.
Software, algorithmR package VennDiagramPMID:21269502RRID:SCR_002414Version 1.6.20
Software, algorithmDatabase for Annotation, Visualization and Integrated Discovery (DAVID) programshttps://david.ncifcrf.gov/home.jspDAVID v6.7; Gene ontology (GO) and KEGG pathway enrichment analyses
Software, algorithmTFBS enrichment analysisUC San Diego, Broad Institute, USAGSEA 4.1.0Based on GSEA database or Harmonizome database for Sp1 targets
Software, algorithmIngenuity Pathway Analysis (IPA) programQiagenAnalysis of Sp1 targets affected by Trrap deletion
Software, algorithmR package AnnotationDbiBioconductorDOI: 10.18129/B9.bioc.AnnotationDbiVersion 1.42.1
Software, algorithmR package org.Mm.eg.dbBioconductorDOI: 10.18129/B9.bioc.org.Mm.eg.dbVersion 3.6.0
Software, algorithmFastQCBabraham Bioinformatics, UKRRID:SCR_014583Version 0.11.5
Software, algorithmBowtiehttp://bowtie-bio.sourceforge.netRRID:SCR_005476Version 1.1.2
Software, algorithmMACS14https://bio.tools/macsRRID:SCR_013291
Software, algorithmRhttps://www.r-project.org/RRID:SCR_001905Version 3.4.4
OtherBeam walkingHomemade
OtherMouse Rota-rodUgo BasileCat#: 47600
OtherDAPI stainInvitrogenCat#: D13061:5000
OtherBioruptorDiagenodeN/ASonication
Othervibrating microtome HM 650 VThermo Scientific MicromSagittal section cutting
OtherReichert Ultracut SLeicaUltrathin section cutting
OtherJEM 1400 electron microscopeJEOLElectron microscopic imaging
OtherOrius SC 1000 CCD-cameraGATANElectron microscopic imaging
OtherBioanalyzer 2100AgilentQuality check and quantification of RNA

Mice

Mice carrying the conditional (floxed; Trrapf/f) allele (Herceg et al., 2001) were crossed with Pcp2-Cre transgenic mice (Tg(Pcp2-cre)2Mpin) (Barski et al., 2000), Camk2-Cre (Tg(Camk2a-cre/ERT2)2Gsc), or Rosa26-CreERT2 Gt(ROSA)26Sortm1(cre/ERT2)Tyj, to generate mice with a specific deletion in Purkinje cells (Trrap-PCΔ) and forebrain glutamatergic neurons (Trrap-FBΔ), or an inducible deletion in all tissues (Trrap-iΔ). To trace the single cell morphology of Purkinje cells, B6.Cg-Tg(Thy1-Brainbow1.0)HLich/J (R26R-Confetti) knock-in mice were crossed with Trrap-PCΔ mice. The double-fluorescent reporter mT/mG knock-in mice (Muzumdar et al., 2007) were intercrossed with Trrap-aNSCΔ mice, to identify Trrap-deleted cells. The Trrap, Cre, mT/mG, and Confetti genotypes of mice were determined by PCR on DNA extracted from tail tissue, as previously described (Loizou et al., 2009). Animal experiments were conducted according to German animal welfare legislation, and the protocol is approved by Thüringen Landesamt für Verbraucherschutz (TLV) (03-042/16), Germany.

Histology

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Tissues for histology were fixed in 4% paraformaldehyde (PFA), cryoprotected in 30% sucrose and frozen in Richard-Allan Scientific Neg-50 Frozen Section Medium (Thermo Scientific, Waltham, MA, USA). The sections (thickness of 5–20 µm) were later used for immunofluorescence staining.

Construction of STMNs expression vectors

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The EF1a-GFP-P2A-STMN3-Poly(A) plasmid was generated by subcloning the RV-Cre2A-GFP (kindly provided by Xiaobing Qing) and the STMN3 protein coding region into the EF1a-GFP construct (Li et al., 2015). For the EF1a-GFP-Poly(A)-EF1a-STMN4-Poly(A) vector, EF1a-promoter, STMN4 protein coding region, and Poly(A) sequence were subcloned into the EF1a-GFP construct. The DNA fragments were assembled with Gibson Assembly Master Mix (New England Biolabs, Massachusetts, USA). The STMN3 and STMN4 protein coding regions were amplified from cDNA library of the murine 10 days postnatal forebrain samples.

siRNA sequences

Isolation and culture of murine primary neurons

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Murine neurons were isolated from mouse embryos at embryonic stage E16.5 (E16.5). The cortex was removed and was first incubated with 0.05% trypsin under 37°C for 15 min. The tissue was then mechanically disintegrated with 1 ml Eppendorf pipettes in an incubation medium (Eagle's minimal essential medium (MEM) supplemented with 1× FCS, 1× B-27 Supplements, 500 μM L-glutamine, 1 mM sodium pyruvate, 1× penicillin–streptomycin, 10 mM HEPES). The suspension was filtered through a cell strainer (40 μm porosity). After centrifugation (630 rpm for 5 min) the neurons in supernatant were seeded into poly-L-lysine coated multiple well plates at the indicated number (6 × 104 cells/well in 24-well plate, 3 × 105 cells/well in 6-well plate) and cultured in the Neurobasal medium (Thermo Fisher Scientific, Waltham, Massachusetts, USA) supplemented with 1 × B-27 Supplements, 500 μM L-glutamine, and 10 mM HEPES until further use.

Motor coordination tests

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Beam walking: Mice were trained to run along a 1 m long beam (3 cm thick) to their home cage. The test was performed on five consecutive days on a 2 cm thick beam, with three runs each day. The mice were video-taped and timed crossing the beam.

Rotarod test: Mice were habituated to the test situation by placing them on a rotarod (Ugo Basile, Gemonio, Italy) with constant rotation (5 rpm) for 5 min the day prior to the test. In the test phase, two trials per mouse were performed with accelerating rotation (2–50 rpm within 4 min) and maximum duration of 5 min, with the time measured until mice fell off the rod.

N2A cell culture

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N2A cells were cultured in DMEM supplemented with 1× FCS, 1× penicillin–streptomycin, and 10 mM HEPES. When the N2A culture reached ~70% confluency, the cells were trypsinized and the cell suspension was centrifuged. The cells were seeded in 1.5 × 105 cells/well onto 6-well plate.

Transfection of primary neurons or N2A cells

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Primary neurons were transfected on day 6 in vitro (DIV6) and N2A cells on day 1 after passage using lipofectamin 2000 in Opti-MEM (0.4 μg plasmid + 0.8 μl lipofectamine 2000 in 100 μl Opti-MEM/well in 24-well plate, 1.2 μg plasmid + 2.4 μl lipofectamine 2000 in 300 μl Opti-MEM/well in 6-well plate, and/or 25 μM siRNA). After 30 min incubation under 37°C with the Neurobasal medium supplemented with 500 μM L-glutamine (300 μl/well in 24-well plate, 1.2 ml/well in 6-well plate), the plasmid/siRNA-Lipofectamine mix was replaced by the neuronal culture medium.

IncuCyte quantification

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The primary neuron culture was placed into Incucyte S3 (Sartorius AG, Göttingen, Germany) for imaging acquisition of phase contrast and GFP signals (10× magnification, 36 images/well in 24-well plate, and 144 images/well in 6-well plate). The image analysis and the neurite detection parameter were determined for each plate separately through IncuCyte NeuroTrack Software Module for S3 or ZOOM.

Immunofluorescent staining and quantification

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Prior to immunostaining, primary neurons on coverslips were fixed with 4% PFA and incubated with 0.7% Triton in PBS for 15 min. The fixed samples were incubated with primary antibodies under 4°C overnight. After incubation with secondary antibody in 1:5000 DAPI, the samples were conserved by ProLong Gold Antifade reagent (Thermo Fisher Scientific). Images were captured by the ApoTome microscope (Zeiss Jena, Germany) under 20× or 40× objectives. The neurite branching, neurite length, and axonal swelling were then scored with NeuronJ Plug-in by ImageJ software and validated manually.

qRT-PCR analysis

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The total RNA was isolated from tissues or aNSCs using the RNeasy Lipid Tissue Mini Kit (Qiagen) and an RNAeasy Mini Kit (Qiagen) respectively and following the manufacturer’s instructions. cDNA was synthesized using the SuperScript III Reverse Transcriptase (Thermo Fischer Scientific). qPCRs were performed using Platinum SYBR Green qPCR SuperMix-UDG (Thermo Fischer Scientific) and a LightCycler 480 Real-Time PCR System (Roche). The Trrap and Actin primers used for amplification were previously described (Tapias et al., 2014). The remaining primer sequences were obtained from the PrimerBank (Spandidos et al., 2010; Spandidos et al., 2008; Wang, 2003) and were as follows: Sp1 (PrimerBank ID 7305515a1): Fwd, 5’-GCCGCCTTTTCTCAGACTC-3’; Rev, 5’-TTGGGTGACTCAATTCTGCTG −3’; STMN3 (PrimerBank ID 6677873a1): Fwd, 5’- CAGCACCGTATCTGCCTACAA-3’; Rev, 5’-GTAGATGGTGTTCGGGTGAGG-3’; STMN4 (PrimerBank ID 9790189a1): Fwd, 5’- ATGGAAGTCATCGAGCTGAACA-3’; Rev, 5’- GGGAGGCATTAAACTCAGGCA-3’. Quantification of the qPCR data was performed by the ΔΔCp method using actin as an internal control. Gene expression values were expressed relative to the gene expression in control tissues or aNSCs.

TUNEL reaction and immunofluorescence staining in brain sections

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Immunofluorescence and TUNEL staining were performed on cryosections prepared from PFA-fixed brains of the indicated ages, as previously described (Tapias et al., 2014), using the following antibodies: mouse anti-Calbindin (1:300, Sigma), rabbit anti-GFAP (1:300, Agilent, Santa Clara, USA), mouse anti-MBP (1:300, Millipore, Burlington, USA), rabbit anti-Calbindin (1:300, Swant, Marly, Switzerland), rabbit anti-GFP (1:200, Cell Signaling Technology, Danvers, USA) mouse anti-GFP (1:200, Santa Cruz), rabbit anti-Sp1 (1:50, Santa Cruz), mouse anti-STMN4 (1:100, Santa Cruz), rabbit anti-STMN3 (1:100, Proteintech, Rosemont, USA), and mouse anti-Tuj1 (1:400, Covance).

Immunoblot analysis

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Total protein lysates were prepared from brain tissue or aNSCs using the RIPA buffer (50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1% NP40, 0.25% Na-deoxycholate, 1 mM EDTA, 1 mM PMSF), and complete mini protease inhibitor cocktail (Roche Applied Science, Penzberg, Germany). N2A cells or neuron lysates were prepared as follows: the culture was treated with 0.25% trypsin for 5 min under 37°C, resuspend with DMEM medium supplemented with 1× FCS and centrifuged under 1100 rpm for 5 min. The resulting pellets were washed once with PBS and lysed with the RIPA buffer. Immunoblotting was performed as described previously (Tapias et al., 2014), using the following antibodies: mouse anti-Calbindin (1:1000, Sigma), rabbit anti-GFAP (1:1000, Dako-Agilent), mouse anti-CNPase (1:1000, Sigma), rat anti-galectin3 (1:1000, eBioscience, Affymetrix, Santa Clara, USA), mouse anti-α-tubulin (1:5000, Sigma), mouse anti-TRRAP (1;1000, Euromedex, Souffelweyersheim, France), rabbit anti-Sp1 (1:1000, Millipore), mouse anti-RB3/STMN4 (1:1000, Santa Cruz), rabbit anti-STMN3 (1:1000, Proteintech), mouse anti-GAPDH (1:1000, Sigma), mouse anti-β-actin (1:5000, Sigma), mouse anti-GFP (1:400, Santa Cruz), and rabbit anti-Oct4 (1:1000, Cell Signaling).

Dendritic tree analysis

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The tissues were fixed in 4% paraformaldehyde (PFA) and embedded in 4% low-melting agarose. 200 μm sagittal sections were obtained using a vibrating microtome HM 650 V (Thermo Scientific Microm) and mounted in slides. Imaging was performed using a Zeiss LSM 710 Confocal three microscope (Zeiss) and the Sholl analysis (Kroner et al., 2014; Sholl, 1953) achieved using the Fiji plugins Simple Neurite Tracing (Longair et al., 2011).

Transmission electron microscopy

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Mice were sacrificed using CO2 and perfused intracardially with cold fixative (3% glutaraldehyde, 1% paraformaldehyde, 0.5% acrolein, 4% sucrose, 0.05 M CaCl2 in 0.1 M cacodylate buffer, pH 7.3). The cerebellum was isolated and postfixed for a minimum of 1 day. For a secondary fixation, the samples were incubated in 2% OsO4/1% potassium ferrocyanide in 0.1 M cacodylate buffer for 3 hr at 4°C in the dark, followed by dehydration in an ascending water/acetone series – then embedded in epoxy resin ‘Epon’ (glycid ether 100, SERVA, Heidelberg, Germany). The resin was allowed to polymerize for 2 days at 60°C in flat embedding molds. Ultrathin sections (50 nm) were produced using an ultramicrotome (Reichert Ultracut S; Leica, Wetzlar, Germany) and electron micrographs taken on a JEM 1400 electron microscope (JEOL, Akishima, Japan), using an accelerating voltage of 80 kV coupled with Orius SC 1000 CCD-camera (GATAN, Pleasanton, USA).

Transcriptomics

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The total RNA was isolated from tissues or cultured aNSCs using an RNeasy Lipid Tissue Mini Kit (Qiagen, Venlo, The Netherlands) and an RNAeasy Mini Kit (Qiagen) respectively, per manufacturer’s instructions. The sequencing of RNA samples was done using Illumina’s next-generation sequencing methodology (Bentley et al., 2008) – the quality check and quantification of the total RNA were completed using the Agilent Bioanalyzer 2100 in combination with the RNA 6000 nano kit (Agilent Technologies). For library preparation 3 µg of tissue total RNA or 800 ng of aNSC total RNA were introduced to the TruSeq Stranded mRNA Kit (Illumina, San Diego, USA), per manufacturer’s description. The quantification and quality check of the libraries were conducted using the Agilent Bioanalyzer 2100 in combination with the DNA 7500 kit. For sequencing of tissues, pools of four libraries were compiled and each pool was loaded on one lane of a HiSeq2500 machine running in 51cycle/single-end/high-output mode. For the sequencing of aNSCs, all libraries were pooled and loaded on three lanes of a HiSeq2500 machine running in 51 cycle/single-end/high-output mode. The sequence information was extracted in FastQ format using Illumina’s bcl2FastQ v1.8.4. The sequencing resulted in around 55mio and 37mio reads per sample for tissues and aNSCs, respectively.

The reads of all samples were mapped to the mouse reference genome (GRCm38) with the Ensembl genome annotation (Release Ensembl 92) using STAR (version 2.5.4b; parameters: --alignIntronMax 100000 --outSJfilterReads Unique --outSAMmultNmax 1 --outFilterMismatchNoverLmax 0.04) (Dobin et al., 2013). Reads mapped uniquely to one genomic position were assigned to the gene annotated at this position with FeatureCounts (version 1.5.0; meta-feature mode, stranded mode ‘2’, Ensembl 92) (Liao et al., 2014). A quality assessment of the raw input data, the read mapping and assignment steps, was performed using MultiQC (version 1.6) (Ewels et al., 2016), with the respective results provided in Supplementary file 6.

Read counts per gene were subjected to the R package DESeq2 (version 1.20.0) (Love et al., 2014), to test for differential expressions in pairwise comparisons as follows: Cultured aNSCs: five mutants contrasted to five controls; Cortex: four mutants contrasted to four controls; Striatum: four mutants contrasted to four controls. For each gene and comparison, the p-value was calculated using the Wald significance test. The resulting p-values were adjusted for multiple testing with Benjamini and Hochberg correction. Genes with an adjusted p<0.05 (false discovery rate, FDR) are considered differentially expressed. The log2 fold changes (LFC) were shrunk with lfcShrink from the DESeq2 package, to control for a variance of LFC estimates for genes with low read counts. The overlaps of all three pairwise DEG lists were calculated and visualized using the R package VennDiagram (version 1.6.20).

GO and KEGG pathway enrichment analyses were performed by supplying the gene lists of DEG overlaps into the database for annotation, visualization, and integrated discovery (DAVID) programs (Huang et al., 2009a; Huang et al., 2009b). TFBS enrichment analysis was performed by supplying the different lists of DEGs into the gene set enrichment analysis (GSEA) database (Subramanian et al., 2005; Mootha et al., 2003). A list of the Sp1 targets was extracted from the Harmonizome database (Rouillard et al., 2016) and compared with the RNA-seq data sets. The lists of Sp1 targets affected by the Trrap deletion was then analyzed using the Ingenuity Pathway Analysis (IPA) program (Qiagen).

Sample preparation for MS proteomics

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First, homogenates of the cortex tissues were prepared using the bead-beating device (24 tissue homogenizer) from Precellys (Montigny-le-Bretonneux, France). Frozen tissue was transferred on ice to bead-beating tubes (Precellys CKMix, 0.5 ml) containing ice-cold PBS with Protease and a Phosphatase Inhibitor cocktail (Roche) and beaten for 2 cycles of 20 s at 6000 rpm, with a 30 s break at 4°C. Homogenates were prepared at an estimated protein concentration of 10 µg/µl; based on 5% protein content of fresh brain tissues by weight. A volume of homogenate corresponding to approximately 500 µg protein was transferred to 1.5 ml Eppendorf tubes and taken for lysis. Lysis was carried out by resuspension of the homogenate in lysis buffer (final concentration 4% SDS, 0.1 M HEPES [pH 8], 50 mM DTT) to a final protein concentration of 1 µg/µl, followed by a sonication in a Bioruptor (Diagenode, Seraing, Belgium) (10 cycles, 1 min ON/30 s OFF, 20°C). The samples were heated (95°C, 10 min), and sonication steps repeated. The lysates were clarified by brief centrifugation, incubated with iodacetamide, (15 mM) at RT, in the dark. Each sample was treated with four volumes ice-cold acetone to precipitate the proteins (overnight, −20°C). The samples were centrifuged at 20,800 g (30 min, 4°C). The supernatant was removed and the pellets washed twice with 400 µl of ice-cold 80% acetone/20% water. The pellets were air-dried before dissolving in a digestion buffer (3 M urea in 0.1 M HEPES, pH 8) at 1 µg/µl. A 1:100 w/w amount of LysC (Wako, Richmond, USA; sequencing grade) was added to each sample before incubation (4 hr, 37°C, 1000 rpm). The samples were diluted 1:1 with milliQ water and incubated with a 1:100 w/w amount of trypsin (Promega, Madison, USA; sequencing grade) (overnight, 37°C, 650 rpm). The digests were acidified with 10% trifluoroacetic acid and desalted with Waters Oasis HLB µElution Plate 30 µm (Waters, Milford, USA) in the presence of a slow vacuum, to manufacturer’s instructions. The eluates were dried down with the speed vacuum centrifuge. Peptide labeling with TMT and subsequent high pH fractionation and LC-MS were conducted as detailed previously (Buczak et al., 2018). Briefly, the peptide samples obtained from the digestion were labeled with TMT-10plex isobaric mass tags (Thermo Fischer Scientific) per manufacturer’s instructions. Equal amounts of the labeled peptides from the 10 samples (five replicates each condition) were mixed, desalted, and pre-fractionated into 16 fractions using high pH reverse phase fractionation on an Agilent Infinity 1260 HPLC, then each fraction was measured individually by nano-LC-MS on an Orbitrap Fusion Lumos employing SPS-MS3 data acquisition (Thermo Fischer Scientific). Subsequently, the fraction data were searched together in Mascot 2.5.1 (Matrix Science, Boston, USA) using Proteome Discoverer 2.0 (Thermo Fischer Scientific) against the Swissprot Mus musculus database (2016; 16,756 entries) and a list of common contaminants. Reporter ion intensity values for the PSMs were exported and processed using in-house written R scripts to remove common contaminants and decoy hits. Only PSMs having reporter ion intensities above 1 × 103 in all the relevant TMT channels were retained for a quantitative analysis, as described in Buczak et al., 2018. Briefly, the reporter ion (TMT) intensities were log2-transformed and normalized. Peptide-level data were summarized into their respective protein groups by taking the median value. For differential protein expression, the five replicates of the two conditions respectively within the TMT10-plex were taken together. Protein ratios were calculated for all protein groups quantified with at least two peptides. To compare DEP in the cortex obtained by RNA-seq to protein DEP (differentially expressed proteins) obtained by mass spectrometry, Ensembl gene IDs were mapped to Uniprot IDs with the R packages AnnotationDbi (1.42.1) and org.Mm.eg.db (3.6.0), while only genes/proteins present in both analyses were considered. When for a single Uniprot ID multiple Ensembl IDs are known, the proteomics measurement is duplicated and all different transcriptomics results assigned to this entry.

aNSC cell culture

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The SVZ of 2–4 months old mice were isolated, minced, and digested with DMEM/F-12 medium supplemented with 20 U/ml papain, 240 µg/ml cysteine, and 400 µg/ml DNAse I type IV. After 1 hr, the digestion was stopped by ovomucoid trypsin inhibitor. The homogenized aNSCs were then cultured in suspension medium (DMEM/F-12 medium supplemented with 1× B-27 Supplements, 1× penicillin–streptomycin, 20 ng/ml EGF, 20 ng/ml bFGF). To induce Trrap deletion, aNSCs were treated with 1 µM 4-hydroxytamoxifen (4-OHT) for 3 days, followed by incubation in fresh medium for another 2 days.

Transfection, Sp1 knockdown, and luciferase assay

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2 × 105 aNSCs were plated in 50 μg/ml PLL and 10 μg/ml laminin pre-coated 24-well plates in Neurobasal Medium (NEM) supplemented with 1× B-27 Supplements, 2 mM L-glutamine, 1× N-2 Supplement, 1× penicillin–streptomycin, 20 ng/ml EGF, and 20 ng/ml bFGF. The transfection was performed after overnight culture using Lipofectamine 2000. For luciferase assay of Sp1 activity, the vector −111 hr TF m3 was used as Sp1 reporter plasmid – gifted by Nigel Mackman (Addgene plasmid # 15450; http://n2t.net/addgene:15450; RRID:Addgene_15450). Guntram Suske gifted the vector pN3-Sp1FL used to overexpress Sp1 (Addgene plasmid # 24543; http://n2t.net/addgene:24543; RRID:Addgene_24543). 24 hr later, transfection cells were collected to measure the luciferase activity using a Dual-Glo Luciferase Assay System (Promega), per manufacturer’s instructions. For Sp1 knockdown, aNSC in adherent conditions was supplemented with 30 nM siRNA against Sp1 mixed with RNAiMAX reagent (Thermo Fischer Scientific). After 48 hr, transfection cells were collected for immunoblot analysis.

Chromatin preparation for ChIP and ChIP-seq

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2 × 106 aNSCs were cross-linked by adding formaldehyde 1% for 10 min at room temperature, quenched with 0.125 M glycine for 5 min at room temperature, then washed three times in phosphate-buffered saline (PBS) before freezing. Pellets were suspended in 0.25 ml SDS lysis buffer (50 mM HEPES-KOH, 140 mM NaCl, 1 mM EDTA, 0.1% Triton X-100, 0.1% sodium deoxycholate, 1% SDS, 10 mM NaB, and protease inhibitors), incubated on a rotator for 30 min at 4°C, sonicated for 20 min at 4°C, then centrifuged at 14,000 rpm for 10 min at 4°C. Supernatants were diluted 10-fold with a ChIP dilution buffer (1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl, 150 mM NaCl, 10 µM NaB, and protease inhibitors) (25 µl retained as input) and incubated overnight in gentle rotation at 4°C with 4 µg of antibody. The following antibodies were used: rabbit anti-SP1 (Millipore), rabbit anti-acetyl-Histone 3 (Millipore), mouse anti-TRRAP (Euromedex), rabbit anti-acetyl-Histone 4 (Millipore), rabbit anti-H3K4me2 (Abcam), rabbit anti-H3 (Abcam), rabbit anti-H4 (Abcam), rabbit anti-Oct4 (Cell Signaling), and rabbit anti-IgG (Sigma). After that, 40 µl of preblocked protein-G-conjugated magnetic beads (DYNAL, Thermo Fischer) were added and incubated for 2 hr in a rotator at 4°C. The immunoprecipitated complexes were washed three times in low-salt wash buffer (0.1% SDS,1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl, 150 mM NaCl), once in high-salt wash buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl, 500 mM NaCl) and once in TE buffer. The complexes were eluted by adding 0.2 ml of Elution buffer (TE 1×, 1% SDS, 150 mM NaCl, 5 mM DTT) for 30 min in rotation at room temperature. The de-cross-linking was performed overnight at 65°C. The de-cross-linked DNA was purified using a QiaQuick PCR Purification Kit (Qiagen) according to the manufacturer’s instruction.

ChIP-seq

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For the library preparation, approximately 10 ng of purified ChIP DNA was end-repaired, dA-tailed, and adaptor-ligated using the TruSeq ChIP Sample Preparation Kit (illumina), to manufacturer’s instructions. The size of the library was checked using Fragment Analyzer (Agilent) and the library sequenced on the NextSeq500 platform (illumina). The Fastq files quality check was performed with FastQC (v0.11.5). Fastq files mapping to mm9 genome was performed by using Bowtie (v1.1.2) with --best --strata -m one parameters. Duplicate reads were removed using a custom script. For peak calling, macs14 (v1.4.2) was used with --nolambda parameter and two different p-value cutoffs (1e-3 for histone modifications and 1e-5 for SP1). Other downstream analyses were done using R (v3.4.4). For a RPM (Read Per Million) calculation, the peaks were merged using the Peakreference function (TCseq_1.2.0 package). The merged peaks were used as the reference for the calculation of RPM for each sample by using a custom script. 10% or 30% of the most depleted regions in mutant versus control samples for histone modifications and Sp1 respectively were used as cutoff for defining differentially regulated regions. Differentially regulated regions were assigned to the nearest gene (ENSEMBL annotation), where the distance of the region was less than ±5 Kb to the TSS (Transcription Start Site).

ChIP qRT-PCR was performed using the Platinum SYBR Green qPCR SuperMix-UDG (Thermo Fischer Scientific) and a LightCycler 480 Real-Time PCR System (Roche). All experiment values were subtracted by those obtained with a rabbit nonimmune serum (IgG) and divided by input, as indicated in the literature (Neri et al., 2012). The following primers were used for amplification: STMN3: Fwd, 5’-CTTGCTACTGCATCAGGCGA-3’; Rev, 5’-AGCCTAGGGGATCATGGGAC-3’; STMN4: Fwd, 5’-TCGCTTTGGAAACCGGACTG-3’; Rev, 5’-TTTGTTTAAAACCCCCGCCC-3’.

siRNASequence (5’→ 3’)
siScrambleUGGUUUACAUGUCGACUAA
siTrrap-1CAAAAGUAGUGAACCGCUA
siTrrap-2CCUACAUUGUGGAGCGGUU
siTrrap-3GCCAACUGUCAGACCGUAA
siTrrap-4CGUACCUGGUCAUGAACGA
siSp1GGAUGGUUCUGGUCAAAUAtt

Data availability

The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through the GEO Series accession numbers GSE131213 (RNA-seq aNSCs), GSE131283 (RNA-seq brain tissues) and GSE131028 (ChIP-seq aNSCs). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository, with the dataset identifier PXD013730.

The following data sets were generated
    1. Kirkpatrick J
    2. Ori A
    3. Wang ZQ
    (2021) PRIDE
    ID PXD013730. The mass spectrometry proteomics of different brain areas of Trrap conditional knockout Mus musculus.
    https://www.ebi.ac.uk/pride/archive/projects/PXD013730
    1. Groth M
    2. Koch P
    3. Pellón DL
    4. Wang ZQ
    (2021) NCBI Gene Expression Omnibus
    ID GSE131213. RNA-seq of murine primary adult stem cells of Trrap inducible knockout Mus musculus with and without 4-OHT treatment.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131213
    1. Groth M
    2. Koch P
    3. Pellón DL
    4. Soler AT
    5. Wang ZQ
    (2021) NCBI Gene Expression Omnibus
    ID GSE131283. RNA-seq of different brain tissue areas of Trrap conditional knockout Mus musculus.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131283
    1. Krepelova A
    2. Rasa SMM
    3. Neri F
    4. Wang ZQ
    (2021) NCBI Gene Expression Omnibus
    ID GSE131028. ChIP-seq of adult neuro-stem cells of Trrap inducible knockout Mus musculus with and without 4-OHT treatment.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131028

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Decision letter

  1. Jeremy J Day
    Reviewing Editor; University of Alabama at Birmingham, United States
  2. Huda Y Zoghbi
    Senior Editor; Texas Children's Hospital, United States

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

Acceptance summary:

This manuscript identifies a novel mechanism linking loss of Transformation/transcription domain-associated protein (TRRAP) to neurodegeneration and motor deficits in a mouse model. Overall, the compelling results highlight a regulatory pathway by which TRRAP, the transcription factor SP1, and histone acetylation interact to control expression of key microtubule associated genes, resulting in destabilized microtubule dynamics when TRRAP is deleted. Given the ties between TRRAP mutation and several human neuropathies, these findings have implications for uncovering novel disease mechanisms.

Decision letter after peer review:

Thank you for submitting your article "TRRAP-HAT modulates microtubule dynamics via SP1 signaling in brain homeostasis and neurodegeneration" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Huda Zoghbi as the Senior Editor. The reviewers have opted to remain anonymous.

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

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

Summary:

This manuscript from Tapias, Larazo, et al., examines effects of neuronal deletion of the transformation/transcription domain associated protein (Trrap) gene. The authors show that Trrap deletion in Purkinje neurons of the cerebellum causes neurite retraction, progressive cell loss, and age-related motor control deficits. Similarly, deletion of Trrap in the cortex and striatum (using the forebrain-selective Camk2a promoter) results in widespread transcriptional dysregulation, specifically at genes containing SP1 transcription factor motifs. Consistent with SP1-mediated deficits in gene expression, Trrap deletion decreased SP1 activity at a luciferase reporter, and decreased SP1 binding and histone acetylation at Trrap target genes Stmn3 and Stmn4. Similar gene expression changes were observed after conditional Trrap deletion in neural stem cells. Finally, the authors report that overexpression of Trrap target genes Stmn3 and Stmn4 rescues effects of Trrap knockdown on neurite branching and length in primary cultured neurons.

Overall, the results identify a novel mechanism by which Trrap regulates neuronal function, and might contribute to neuronal development and potentially also neurodegeneration. Additionally, these results may have relevance to Trrap mutations in human patients.

While the results identify a potentially novel link between Trrap, SP1, and microtubule dynamics that support neruonal growth and function, the reviewers had several concerns with the current manuscript. First, the evidence that SP1 and histone acetylation mediates the effects of Trrap deletion is not sufficient to fully support the claims of the manuscript. Second, while the examination of different cell types is helpful and increases the ability to generalize effects of Trrap deletion, there are significant concerns about the timing of Trrap deletion in these models, and several of the results are not consistent with a neurodegenerative phenotype. We have suggested the following revisions to improve the manuscript.

Essential revisions:

1) The data presented in Figure 3G are intriguing, and are the main data in the paper to support a direct mechanistic link between Trrap and SP1 function. However, these results are based on a relatively underpowered experiment (n=3), with large error. The manuscript would be improved if this could be strengthened or bolstered by additional evidence. For example, the manuscript already contains H3ac and H4ac ChIP-seq data, which is used to infer effects mediated by SP1 at Trrap target genes. The authors might confirm this relationship by looking at the distribution of histone acetylation across SP1 sites in the genome (as well as specifically at Trrap DEGs). Similarly, it would be useful to show that Trrap is normally found at SP1 motifs for these genes in neurons (e.g. with ChIP). Likewise, although the ChIP data suggest that SP1 regulates STMN3 and 4 expression, this is not directly demonstrated. Does SP1 depletion alone influence their expression?

2) The authors argue that neurodegeneration linked with Trrap deletion is the result of altered transcription of SP1 targets through impaired SP1 activity caused by reduced acetylation. However, the overlap between hypoacetylated loci and SP1-regulated genes on a genome-wide scale was pretty low (subsection “Trrap-HAT mediates Sp1 transcriptional control of microtubule dynamic genes”, 11 genes), indicating that other factors may be at play. The authors validate their assertion by showing that Sp1 and AcH4 are reduced by Trrap deletion on target promoters (Figure 4E and F), but this study is lacking proper controls to show that these changes are selective for acetylation. Given that the authors argue that the effect is mediated by HAT activity (which is not measured but inferred based on known Trrap functions), it is important to exclude alternative explanations, such as non-specific effects of Trrap deletion on nucleosome density or other post-translational modifications. As such, studies in Figure 4D should be repeated for total H4 and also for another PTM that isn't directly regulated by Trrap. In addition, to validate the relevance of SP1 as a regulator of genes affected by Trrap function, an additional control would be beneficial – specifically, the authors could conduct ChIP for a TF that was not associated with genes affected by Trrap deletion in Figure 3F to show that changes in Figure 4F are specific for SP1 and acetylation.

3) The authors link SP1 with impaired microtubule function. They show microtubule-related terms in ontology analysis of differentially regulated genes that overlap in the striatum and the cortex, but not specifically for DEGs that contain SP1 binding sites. Although they do identify STMN3 and 4 as targets, it would be useful to see if these categories come out for SP1 regulated genes as a group. As authors point out, SP1 tends to have wide spread functions that influence many pathways, so it is important to exclude the possibility that microtubule-related functions affected by Trrap may be small compared to other pathways that are influenced more dramatically. Even if they are small, this finding is still of interest, but the conclusions need to be dampened.

4) While the manuscript claims that TRRAP-HAT modulates microtubule dynamics via SP1 signaling in brain homeostasis and neurodegeneration, the evidence for these assertions is relatively weak. The signaling pathway the authors found is based on the RNA-seq and ChIP-seq in the forebrain at P10 when postnatal brain development and maturation is still going on. Also, the role of TRRAP-HAT-SP1-STMN3/4 signaling in the neurodegeneration context is not validated since the cortical primary culture at DIV6-12 is also at developmental stage rather than fully matured stage. Therefore, while the manuscript begins with Purkinje neuronal degeneration by Trrap deletion, data in Figure 3 shows that the signaling pathway is more likely to be involved in the neural development. Similarly, the authors did not carefully confirm the effect of Trrap deletion on Purkinje neuronal development. In the PCP2-Cre line the authors used in this study, Cre expression starts at P6 (Zhang et al., 2004). Considering the cerebellum and Purkinje neuronal development is completed around at ~P20, it is difficult to exclude the possibility TRRAP-HAT-SP1-STMN3/4 signaling affect the Purkinje neuronal development rather than neuronal survival. Indeed, previous study showed that STMN3 is crucial for the formation and development of the Purkinje cell dendritic arbor (Poulain et al., 2008). These issues should be addressed in the manuscript, and if the authors cannot rule out neurodvelopmental effects, the specific claims regarding neurodegeneration should be removed.

5) The primary interest appears to be in investigating the role of Trrap in cerebellar neurodegeneration, and as such, they quantify phenotypes that are relevant to cerebellar function. However, mechanistic studies are conducted in the striatum and cortex. The argument for switching mouse models and brain regions was the scarcity of Purkinje cells in the Trrap-PC model, so they switch to CamKIIa Cre and striatum and cortex for mechanism. A search of the literature and Allen Brain atlas shows CamK2a expression in cerebellar cells, including Purkinje cells. Since there was no cell sorting performed to select for CamK2a cells in any of the brain regions examined, the experiments would presumably be able to be conducted in the cerebellum? Similarly, the authors use multiple cell types in cell culture studies, including cultured cortical neurons – why not use cultured cerebellar neurons?

6) Please clarify how the ChIP datasets in Figure 4E-F are compared between groups. Why is there no variability in the control group? For ChIP-qPCR data, it would be preferable to show the results as % of input, rather than binding enrichment. Additionally, while the sequences for ChIP-qPCR primers are provided, the authors should clarify where these primers target within the genome, and whether site contains a known SP1 motif.

7) The data showing siRNA-mediated Trrap knockdown (Figure 5—figure supplement 1A) is not convincing – summary data and statistical comparisons should be shown for manipulations as performed in the in vitro experiments. Similarly, it is not clear how transfection experiments would lead to robust knockdown. Typically, neuronal transfection experiments suffer from a very low efficiency, meaning that manipulations would only occur in a very small fraction of cells. If this is not the case here, data for transfection efficiency should be provided.

8) The Title of the manuscript should be revised to meet eLife guidelines. Specifically, the Title should avoid use of dashes, acronyms, or unfamiliar abbreviations (unless needed for scientific reasons). Please revise your Title with this advice in mind. Additionally, the Title and/or Abstract should provide a clear indication of the biological system under investigation (i.e., species name or broader taxonomic group, if appropriate). Please revise your Title and/or Abstract with this advice in mind.

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

Thank you for submitting your article "HAT cofactor TRRAP modulates microtubule dynamics via SP1 signaling in neurodegeneration" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Huda Zoghbi as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Hongjun Song (Reviewer #3).

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

We would like to draw your attention to changes in our policy on revisions we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.

Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife, either of which would be linked to the original paper.

Summary:

In original manuscript, the authors identified a novel mechanism by which Trrap regulates neuronal function, with implications for neurodegeneration. Specifically, they show that loss of TRRAP results in neurite retraction, neurodegeneration, and motor deficits. They link these changes with activity of the transcription factor SP1 and with two downstream targets, Stmn3 and Stmn4, which are implicated in microtubule dynamics. Overall, these data present a novel regulatory pathway in regulating microtubule dynamics and provide a basis for uncovering the role of specific epigenetic factors in this process. Given the ties between TRRAP and several human conditions affecting neural conditions, these findings have implications for uncovering novel disease mechanisms.

In this revised manuscript, the authors made significant efforts to address all the concerns raised by previous reviews with new experiment, data re-analyses, and clarification in the text. The manuscript is significantly improved, and the new results support the author's interpretation that Trrap deletion alters SP1 binding and expression of STMN3 and STMN4. Notably, overexpression of STNM3 can rescue certain neurite growth deficits caused by Trrap deletion, in agreement with their model. Likewise, many conclusions have been appropriately toned down to match the experimental results or in light of noted caveats. However, some specific revisions are still critical prior to publication of the manuscript (outlined below).

Essential revisions:

1) Replace all figures with higher resolution images – the current versions appear pixelated upon zooming in to see details. Vector images are preferred where possible.

2) In relation to comment 4: Conclusions regarding neurodegeneration are still too strong and the answer provided in response to the reviewers is not fully reflected in the discussion of the revised manuscript.

3) The authors still do not address the lack of variability in control groups in several figures, including:

-Figure 4B – from blot images in Figure 4A, there seems to be sample variability in controls, but the individual data points look exactly the same. The same applies to Figure 5 – figure supplement 1A.

-Individual data points are shown for some graphs, but not for others. Individual data points should be included for all graphs.

https://doi.org/10.7554/eLife.61531.sa1

Author response

Essential revisions:

1) The data presented in Figure 3G are intriguing, and are the main data in the paper to support a direct mechanistic link between Trrap and SP1 function. However, these results are based on a relatively underpowered experiment (n=3), with large error.

We have now newly performed the experiments 5 times. The new data replaced the old Figure 3G by revised Figure 3F.

The manuscript would be improved if this could be strengthened or bolstered by additional evidence. For example, the manuscript already contains H3ac and H4ac ChIP-seq data, which is used to infer effects mediated by SP1 at Trrap target genes. The authors might confirm this relationship by looking at the distribution of histone acetylation across SP1 sites in the genome (as well as specifically at Trrap DEGs). Similarly, it would be useful to show that Trrap is normally found at SP1 motifs for these genes in neurons (e.g. with ChIP).

We appreciate this important suggestion and have reanalyzed the ChIP-seq data. The new analyses are shown as Figure 4—figure supplement 1B-C. Moreover, our ChIP assay also showed Trrap binding at Sp1 motifs (Figure 4F-G), because these primers contain Sp1 motifs, we now show these in Figure 4—figure supplement 1G. These new data are also described in the text.

Likewise, although the ChIP data suggest that SP1 regulates STMN3 and 4 expression, this is not directly demonstrated. Does SP1 depletion alone influence their expression?

To address this point, we performed siRNA mediated SP1 knockdown and found a reduction of STMN3 and 4 expressions. Representative Western blots and quantifications are provided in Figure 4H.

2) The authors argue that neurodegeneration linked with Trrap deletion is the result of altered transcription of SP1 targets through impaired SP1 activity caused by reduced acetylation. However, the overlap between hypoacetylated loci and SP1-regulated genes on a genome-wide scale was pretty low (subsection “Trrap-HAT mediates Sp1 transcriptional control of microtubule dynamic genes”, 11 genes), indicating that other factors may be at play. The authors validate their assertion by showing that Sp1 and AcH4 are reduced by Trrap deletion on target promoters (Figure 4E and F), but this study is lacking proper controls to show that these changes are selective for acetylation. Given that the authors argue that the effect is mediated by HAT activity (which is not measured but inferred based on known Trrap functions), it is important to exclude alternative explanations, such as non-specific effects of Trrap deletion on nucleosome density or other post-translational modifications. As such, studies in Figure 4D should be repeated for total H4 and also for another PTM that isn't directly regulated by Trrap. In addition, to validate the relevance of SP1 as a regulator of genes affected by Trrap function, an additional control would be beneficial – specifically, the authors could conduct ChIP for a TF that was not associated with genes affected by Trrap deletion in Figure 3F to show that changes in Figure 4F are specific for SP1 and acetylation.

We thank the reviewers for their valuable suggestions. To address the comments, we performed all these experiments and presented them as Figure 4F-G. We used H3 and H4 for histone controls and H3K4me2 as a non-relevant PTM control. Oct4 is a transcription factor that is not under the control of SP1 (PMID:10592386) and is used as a control, as requested by the reviewers. Please note there was a large error bar in the Oct4 ChIP data because one pair of the sample gave a very high value (we noted in the figure legend). However, when we compared pairwise (paired t-test), the error bar in both control and mutant is low (data not shown). Nonetheless, both showed no statistical difference. In revised manuscript, we present these new data in the text and described them in the figure legend.

3) The authors link SP1 with impaired microtubule function. They show microtubule-related terms in ontology analysis of differentially regulated genes that overlap in the striatum and the cortex, but not specifically for DEGs that contain SP1 binding sites. Although they do identify STMN3 and 4 as targets, it would be useful to see if these categories come out for SP1 regulated genes as a group. As authors point out, SP1 tends to have wide spread functions that influence many pathways, so it is important to exclude the possibility that microtubule-related functions affected by Trrap may be small compared to other pathways that are influenced more dramatically. Even if they are small, this finding is still of interest, but the conclusions need to be dampened.

Our original Figure 3E and Table 3 showed the overall changes of molecular pathways. Following this comment, we have now provided the Top50 of the SP1-containing DEGs in Figure 3—figure supplement 2G. As one can see, again, the microtubule dynamics is prominent in the list.

4) While the manuscript claims that TRRAP-HAT modulates microtubule dynamics via SP1 signaling in brain homeostasis and neurodegeneration, the evidence for these assertions is relatively weak. The signaling pathway the authors found is based on the RNA-seq and ChIP-seq in the forebrain at P10 when postnatal brain development and maturation is still going on. Also, the role of TRRAP-HAT-SP1-STMN3/4 signaling in the neurodegeneration context is not validated since the cortical primary culture at DIV6-12 is also at developmental stage rather than fully matured stage. Therefore, while the manuscript begins with Purkinje neuronal degeneration by Trrap deletion, data in Figure 3 shows that the signaling pathway is more likely to be involved in the neural development. Similarly, the authors did not carefully confirm the effect of Trrap deletion on Purkinje neuronal development. In the PCP2-Cre line the authors used in this study, Cre expression starts at P6 (Zhang et al., 2004). Considering the cerebellum and Purkinje neuronal development is completed around at ~P20, it is difficult to exclude the possibility TRRAP-HAT-SP1-STMN3/4 signaling affect the Purkinje neuronal development rather than neuronal survival. Indeed, previous study showed that STMN3 is crucial for the formation and development of the Purkinje cell dendritic arbor (Poulain et al., 2008). These issues should be addressed in the manuscript, and if the authors cannot rule out neurodvelopmental effects, the specific claims regarding neurodegeneration should be removed.

We are aware of the limitation of the model that was used. As stated in the text, the problem for Purkinje cell specific knockout is the scarcity of Purkinje cells (PC) in our PCdel models. Therefore, we switched to forebrain deleted model to analyze the pathway controlled by Trrap for the feasibility. To gain insight we performed integrated omics (RNA-seq, proteomics and ChIP-seq) and found a striking commonality of molecular pathways governed by TRRAP-HAT in different neural cell types. We provided detailed analyses on these omic datasets. Then, we confirmed STMNs using primary neurons and PC models (Figure 4C-D). However, given the normal PC development in the PCD model, i.e., a normal PC density and behavior at young age (Figure 1A-B), Trrap-Sp1-STMNs does not likely play a prominent role in early PC development at least not in the Pcp2-Cre model. Moreover, in our animal models the deletion occurs after the neuron differentiation, despite not fully matured, pointing out that at the early stages the Trrap deleted brain looks normal (Figure 1C, Figure 2C (Purkinje-Cre related) and Figure 3A (CamkII-Cre related)). Having said this, we have no direct evidence, which would allow us to rule out the possibility that Trrap-Sp1-STMNs affects the general neuronal development or survival. Indeed, as the reviewers pointed out, STMN3/4 are also important in the maintenance of neuron dendrites, which had been discussed in the original text. Thus, we have carefully rephrased our statement, in the revised manuscript.

It is noteworthy that we have many PCdel models running in the lab, in which many life essential genes were deleted, such as ATR, MRE11 and NBS1 (null mutation of these genes causes cell and embryonic lethality). None of these PCD models show the neurodegeneration phenotype (even after 20 months) in contrast to the Trrap-PCD model. These observations thus highlight the specificity of the HAT-TRRAP-Sp1-STMN axis in preventing PC degeneration.

5) The primary interest appears to be in investigating the role of Trrap in cerebellar neurodegeneration, and as such, they quantify phenotypes that are relevant to cerebellar function. However, mechanistic studies are conducted in the striatum and cortex. The argument for switching mouse models and brain regions was the scarcity of Purkinje cells in the Trrap-PC model, so they switch to CamKIIa Cre and striatum and cortex for mechanism. A search of the literature and Allen Brain atlas shows CamK2a expression in cerebellar cells, including Purkinje cells. Since there was no cell sorting performed to select for CamK2a cells in any of the brain regions examined, the experiments would presumably be able to be conducted in the cerebellum?

Our lab works on CamKII-Cre mice extensively and observed using Confetti reporter that CamKII-Cre can deleted only minor subpopulations of cells in cerebella. Thus, an extensive cerebellar analysis of Trrap in the CamkII-Cre model would be very complicated due to two reasons: we would have to first map exactly the cells deleted and second exclude the possible influence of the forebrain excitatory neurons (extensively expressing CamKII). In our Trrap-FBD model it is not even possible since these mutant mice die around weaning. Thus, CamKII-Cre is not a suitable model to study cerebella. Nevertheless, this model was very useful to gain insight into the mechanistic understanding on how Trrap regulates neurological processes.

Similarly, the authors use multiple cell types in cell culture studies, including cultured cortical neurons – why not use cultured cerebellar neurons?

Indeed, we tried to establish cerebellar PC culture; but it turned out to be very challenging. Meanwhile we have good experience in culturing cortical neurons. Although our in vitro data alone could not differentiate whether Sp1-STMNs is operational in PC development or degeneration, together with in vivo data (e.g., Figure 4C-D) and the discussion of literatures, we can comfortably conclude that proper Sp1-STMNs very likely contribute to neuroprotection. Nevertheless, we have carefully rephrased some sentences to reflect certain limitations (Discussion).

6) Please clarify how the ChIP datasets in Figure 4E-F are compared between groups. Why is there no variability in the control group? For ChIP-qPCR data, it would be preferable to show the results as % of input, rather than binding enrichment. Additionally, while the sequences for ChIP-qPCR primers are provided, the authors should clarify where these primers target within the genome, and whether site contains a known SP1 motif.

We now normalized our ChIP-qPCR as % of Input (Figure 4F-G). Per request, we have now also indicated the position where the primers are located in the promoter proximity of STMN3 and 4 in Figure 4—figure supplement 1G. Indeed, these primers’ sequence contain the Sp1 consensus (see Figure 4—figure supplement 1G).

7) The data showing siRNA-mediated Trrap knockdown (Figure 5—figure supplement 1A) is not convincing – summary data and statistical comparisons should be shown for manipulations as performed in the in vitro experiments. Similarly, it is not clear how transfection experiments would lead to robust knockdown. Typically, neuronal transfection experiments suffer from a very low efficiency, meaning that manipulations would only occur in a very small fraction of cells. If this is not the case here, data for transfection efficiency should be provided.

We repeated the siRNA knockdown and provided quantification with statistical analyses in Figure 5—figure supplement 1A. In the rescue experiment, we transfected the siRNA constructs together with a GFP-expressing vector. Only GFP+ cells were used for analyses. We have clarified this point in the figure legend.

8) The Title of the manuscript should be revised to meet eLife guidelines. Specifically, the Title should avoid use of dashes, acronyms, or unfamiliar abbreviations (unless needed for scientific reasons). Please revise your Title with this advice in mind. Additionally, the Title and/or Abstract should provide a clear indication of the biological system under investigation (i.e., species name or broader taxonomic group, if appropriate). Please revise your Title and/or Abstract with this advice in mind.

We followed the advice/guideline and modified the Title and Abstract.

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

Essential revisions:

1) Replace all figures with higher resolution images – the current versions appear pixelated upon zooming in to see details. Vector images are preferred where possible.

The original quality of images seems to be fine. The quality problem might be due to the problem of compressed images during conversion of uploaded files. We have readjusted (vector images) where possible and uploaded all figures.

2) In relation to comment 4: Conclusions regarding neurodegeneration are still too strong and the answer provided in response to the reviewers is not fully reflected in the Discussion of the revised manuscript.

We showed clearly a progress neurodegeneration in Trrap deleted Purkinje cell mouse model (Trrap-PCD). However, due to technical limitation, we could not fully prove the molecular mechanism (SP1-STMNs) on the Trrap-PCD model, although we verified STMNs downregulation in their cerebellum. We agree some of the statements are too strong. Following Editors’ suggestion, we have deleted some repetitive conclusions and tried to soften the statement in the relevant Discussion parts.

3) The authors still do not address the lack of variability in control groups in several figures, including:

-Figure 4B – from blot images in Figure 4A, there seems to be sample variability in controls, but the individual data points look exactly the same. The same applies to Figure 5—figure supplement 1A.

We have explained the quantification of these figures. We modified presentation of these figures to reflect the data accordingly (Figure 4B, 4H and Figure 5-figure supplement 1A).

-Individual data points are shown for some graphs, but not for others. Individual data points should be included for all graphs.

Following the Editors’ suggestion, we include data points all bar graphs wherever possible in the revised figures.

https://doi.org/10.7554/eLife.61531.sa2

Article and author information

Author details

  1. Alicia Tapias

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Contributed equally with
    David Lázaro and Bo-Kun Yin
    Competing interests
    No competing interests declared

  2. David Lázaro

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Writing - original draft
    Contributed equally with
    Alicia Tapias and Bo-Kun Yin
    Competing interests
    No competing interests declared

  3. Bo-Kun Yin

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Data curation, Formal analysis, Investigation
    Contributed equally with
    Alicia Tapias and David Lázaro
    Competing interests
    No competing interests declared

  4. Seyed Mohammad Mahdi Rasa

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6850-8909

  5. Anna Krepelova

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared

  6. Erika Kelmer Sacramento

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared

  7. Paulius Grigaravicius

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared

  8. Philipp Koch

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Formal analysis
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2825-7943

  9. Joanna Kirkpatrick

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Data curation, Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared

  10. Alessandro Ori

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Conceptualization, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3046-0871

  11. Francesco Neri

    Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis
    Competing interests
    No competing interests declared

  12. Zhao-Qi Wang

    1. Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany
    2. Faculty of Biological Sciences, Friedrich-Schiller-University of Jena, Jena, Germany
    Contribution
    Conceptualization, Data curation, Supervision, Funding acquisition, Investigation, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    Zhao-Qi.Wang@leibniz-fli.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8336-3485

Funding

Leibniz Association (Postdoc Fellowship)

  • Alicia Tapias

Leibniz Association (PhD studentship)

  • David Lázaro

Leibniz Association (Open Access Fund)

  • Zhao-Qi Wang

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

Acknowledgements

We thank D Stefanova, H Ivashenko, S Ortega, S Tsukamoto, M Oi, C Murakami, and C Meisezahl for their technical support and assistance. We also thank P Elsner for his excellent assistance in maintenance of the animal colonies and K Buder for her help using TEM. We are grateful to Dr K-H Gührs for his critical reading of the manuscript. We are grateful to the FLI Core Facilities DNA Sequencing, Life Science Computing, Proteomics, Histology and Imaging for their technical support. We thank members of the Wang Laboratory for their helpful and critical discussions. AT was supported by a Postdoc Fellowship of the Leibniz Institute for Age Research (FLI) and DL was a recipient of a PhD studentship from the Leibniz Graduate School on Aging (LGSA). Z-QW is supported in part by the Deutschen Forschungsgemeinschaft (DFG), Germany.

Ethics

Animal experimentation: Animal experiments were conducted according to German animal welfare legislation, and the protocol is approved by Thüringen Landesamt für Verbraucherschutz (TLV) (03-042/16), Germany.

Senior Editor

  1. Huda Y Zoghbi, Texas Children's Hospital, United States

Reviewing Editor

  1. Jeremy J Day, University of Alabama at Birmingham, United States

Publication history

  1. Received: July 28, 2020
  2. Accepted: February 16, 2021
  3. Accepted Manuscript published: February 17, 2021 (version 1)
  4. Version of Record published: March 8, 2021 (version 2)
  5. Version of Record updated: May 18, 2021 (version 3)
  6. Version of Record updated: July 19, 2021 (version 4)

Copyright

© 2021, Tapias 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. Alicia Tapias
  2. David Lázaro
  3. Bo-Kun Yin
  4. Seyed Mohammad Mahdi Rasa
  5. Anna Krepelova
  6. Erika Kelmer Sacramento
  7. Paulius Grigaravicius
  8. Philipp Koch
  9. Joanna Kirkpatrick
  10. Alessandro Ori
  11. Francesco Neri
  12. Zhao-Qi Wang
(2021)
HAT cofactor TRRAP modulates microtubule dynamics via SP1 signaling to prevent neurodegeneration
eLife 10:e61531.
https://doi.org/10.7554/eLife.61531

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    Lisa Baumgartner, Dominik Handler ... Julius Brennecke
    Research Article

    RNA interference systems depend on the synthesis of small RNA precursors whose sequences define the target spectrum of these silencing pathways. The Drosophila Heterochromatin Protein 1 (HP1) variant Rhino permits transcription of PIWI-interacting RNA (piRNA) precursors within transposon-rich heterochromatic loci in germline cells. Current models propose that Rhino’s specific chromatin occupancy at piRNA source loci is determined by histone marks and maternally inherited piRNAs, but also imply the existence of other, undiscovered specificity cues. Here, we identify a member of the diverse family of zinc finger associated domain (ZAD)-C2H2 zinc finger proteins, Kipferl, as critical Rhino cofactor in ovaries. By binding to guanosine-rich DNA motifs and interacting with the Rhino chromodomain, Kipferl recruits Rhino to specific loci and stabilizes it on chromatin. In kipferl mutant flies, Rhino is lost from most of its target chromatin loci and instead accumulates on pericentromeric Satellite arrays, resulting in decreased levels of transposon targeting piRNAs and impaired fertility. Our findings reveal that DNA sequence, in addition to the H3K9me3 mark, determines the identity of piRNA source loci and provide insight into how Rhino might be caught in the crossfire of genetic conflicts.

    1. Chromosomes and Gene Expression

    The structure-selective endonucleases GEN1 and MUS81 mediate complementary functions in safeguarding the genome of proliferating B lymphocytes

    Keith Conrad Fernandez, Laura Feeney ... Jayanta Chaudhuri
    Research Article

    During the development of humoral immunity, activated B lymphocytes undergo vigorous proliferative, transcriptional, metabolic, and DNA remodeling activities; hence, their genomes are constantly exposed to an onslaught of genotoxic agents and processes. Branched DNA intermediates generated during replication and recombinational repair must be eliminated to preserve the integrity of these DNA transactions for the faithful duplication and propagation of genomic material. To investigate the role of two structure-selective endonucleases, GEN1 and MUS81, in B cell biology, we established B-cell conditional knockout mouse models and found that targeted deletion of GEN1 and MUS81 in early B-cell precursors abrogates the development and maturation of B-lineage cells while selective loss of the enzymes in mature B cells inhibits the generation of robust germinal centers. Upon activation, these double-null mature B lymphocytes fail to proliferate and survive while exhibiting transcriptional signatures of p53 signaling, apoptosis, and type I interferon response. Metaphase spreads of these endonuclease-deficient cells showed severe and diverse chromosomal abnormalities, including a preponderance of chromosome breaks, consistent with a defect in resolving DNA recombination intermediates. These observations underscore the pivotal roles of GEN1 and MUS81 in safeguarding the genome to ensure the proper development and maintenance of B lymphocytes.

    1. Cancer Biology
    2. Chromosomes and Gene Expression

    DNA methylome combined with chromosome cluster-oriented analysis provides an early signature for cutaneous melanoma aggressiveness

    Arnaud Carrier, Cécile Desjobert ... Paola B Arimondo
    Research Article Updated

    Aberrant DNA methylation is a well-known feature of tumours and has been associated with metastatic melanoma. However, since melanoma cells are highly heterogeneous, it has been challenging to use affected genes to predict tumour aggressiveness, metastatic evolution, and patients’ outcomes. We hypothesized that common aggressive hypermethylation signatures should emerge early in tumorigenesis and should be shared in aggressive cells, independent of the physiological context under which this trait arises. We compared paired melanoma cell lines with the following properties: (i) each pair comprises one aggressive counterpart and its parental cell line and (ii) the aggressive cell lines were each obtained from different host and their environment (human, rat, and mouse), though starting from the same parent cell line. Next, we developed a multi-step genomic pipeline that combines the DNA methylome profile with a chromosome cluster-oriented analysis. A total of 229 differentially hypermethylated genes was commonly found in the aggressive cell lines. Genome localization analysis revealed hypermethylation peaks and clusters, identifying eight hypermethylated gene promoters for validation in tissues from melanoma patients. Five Cytosine-phosphate-Guanine (CpGs) identified in primary melanoma tissues were transformed into a DNA methylation score that can predict survival (log-rank test, p=0.0008). This strategy is potentially universally applicable to other diseases involving DNA methylation alterations.