Cohesin-dependence of neuronal gene expression relates to chromatin loop length

  1. Lesly Calderon
  2. Felix D Weiss
  3. Jonathan A Beagan
  4. Marta S Oliveira
  5. Radina Georgieva
  6. Yi-Fang Wang
  7. Thomas S Carroll
  8. Gopuraja Dharmalingam
  9. Wanfeng Gong
  10. Kyoko Tossell
  11. Vincenzo de Paola
  12. Chad Whilding
  13. Mark A Ungless
  14. Amanda G Fisher
  15. Jennifer E Phillips-Cremins  Is a corresponding author
  16. Matthias Merkenschlager  Is a corresponding author
  1. MRC London Institute of Medical Sciences, Imperial College London, United Kingdom
  2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, United Kingdom
  3. Department of Bioengineering, University of Pennsylvania, United States
  4. Epigenetics Program, Perelman School of Medicine, University of Pennsylvania, United States
  5. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, United States

Abstract

Cohesin and CTCF are major drivers of 3D genome organization, but their role in neurons is still emerging. Here, we show a prominent role for cohesin in the expression of genes that facilitate neuronal maturation and homeostasis. Unexpectedly, we observed two major classes of activity-regulated genes with distinct reliance on cohesin in mouse primary cortical neurons. Immediate early genes (IEGs) remained fully inducible by KCl and BDNF, and short-range enhancer-promoter contacts at the IEGs Fos formed robustly in the absence of cohesin. In contrast, cohesin was required for full expression of a subset of secondary response genes characterized by long-range chromatin contacts. Cohesin-dependence of constitutive neuronal genes with key functions in synaptic transmission and neurotransmitter signaling also scaled with chromatin loop length. Our data demonstrate that key genes required for the maturation and activation of primary cortical neurons depend on cohesin for their full expression, and that the degree to which these genes rely on cohesin scales with the genomic distance traversed by their chromatin contacts.

Editor's evaluation

Neurons use activity-responsive gene programs to shape cell-specific identity and respond appropriately to environmental stimuli. By combining elegant protein degradation and cell-specific knockout approaches with transcriptional profiling and chromatin structure analysis, this manuscript delineates the contributions of cohesin (a key protein responsible for genome structure and organization), in developmental and activity-dependent gene expression programs as well as chromatin reorganization. These results demonstrate that cohesin is required for the full expression of key genes required for the maturation and activation of cortical excitatory neurons, and reveal a tight correlation between cohesin effects and the genomic distance of higher-order chromatin loops.

https://doi.org/10.7554/eLife.76539.sa0

Introduction

Mutations in cohesin and CTCF cause intellectual disability in humans (Deardorff et al., 2018; Gregor et al., 2013; Rajarajan et al., 2016), and defects in neuronal gene expression (Kawauchi et al., 2009; Fujita et al., 2017; van den Berg et al., 2017; van den Berg et al., 2017; McGill et al., 2018; Yamada et al., 2019; Weiss et al., 2021), neuronal morphology (Fujita et al., 2017; McGill et al., 2018; Sams et al., 2016), long-term potentiation (Sams et al., 2016; Kim et al., 2018a), learning, and memory (McGill et al., 2018; Yamada et al., 2019; Sams et al., 2016; Kim et al., 2018b) in animal models. In addition to mediating canonical functions in the cell cycle (Nasmyth and Haering, 2009), cohesin cooperates with CTCF to facilitate the spatial organization of the genome in the nucleus. Cohesin traverses chromosomal DNA in an ATP-dependent manner through a mechanism known as loop extrusion. This process generates self-interacting domains that are delimited by chromatin boundaries marked by CTCF and defined by an increased probability of chromatin contacts (Fudenberg et al., 2016; Rao et al., 2014; Rao et al., 2017; Nora et al., 2017; Schwarzer et al., 2017). The resulting organization of the genome into domains and loops is thought to contribute to the regulation of gene expression by facilitating appropriate enhancer-promoter interactions (Dekker and Mirny, 2016; Merkenschlager and Nora, 2016; Beagan and Phillips-Cremins, 2020; McCord et al., 2020) and its disruption can cause human disease (Lupiáñez et al., 2015; Spielmann et al., 2018; Sun et al., 2018), including neurodevelopmental disorders (Won et al., 2016). Recent studies which induced global cohesin loss on acute timescales resulted in the altered expression of only a small number of genes in a human cell line in vitro (Rao et al., 2017), while later time points after cohesin depletion in vivo revealed more pervasive disruption in expression (Schwarzer et al., 2017). We recently reported that cohesin loss has modest effects on constitutive gene expression in uninduced macrophages, but severely disrupted the establishment of new gene expression programs upon the induction of a new macrophage state (Cuartero et al., 2018). These data support a model in which cohesin-mediated loop extrusion is more important for the establishment of new gene expression rather than maintenance of existing programs (Cuartero et al., 2018). However, the applicability of this model across other cell types remains unclear, and the extent to which deficits in cohesin function alter neuronal gene expression remains a critical underexplored question.

Activity-regulated neuronal genes (ARGs) are defined by transcriptional induction in response to neuronal activity, and are important for cellular morphology, the formation of synapses and circuits, and ultimately for learning and memory (Gallo et al., 2018; Kim et al., 2010; Malik et al., 2014; Greer and Greenberg, 2008; Tyssowski et al., 2018; Yap and Greenberg, 2018). ARG induction is accompanied by acetylation of H3K27, as well as the recruitment of RNA polymerase 2, cohesin, and other chromatin binding proteins at ARGs and their enhancers (Greer and Greenberg, 2008; Malik et al., 2014; Yamada et al., 2019; Yap and Greenberg, 2018; Tyssowski et al., 2018; Schaukowitch et al., 2014; Beagan et al., 2020). For a subset of ARGs, long-range contacts between enhancers and promoters increase upon stimulation of post-mitotic neurons (Schaukowitch et al., 2014; Sams et al., 2016; Beagan et al., 2020). Among neuronal ARGs, IEGs and, late response genes (LRGs) are known to be activated on different time scales according to distinct mechanisms (Tyssowski et al., 2018). Moreover, IEGs and LRGs differ significantly in their looping landscape, as IEGs form fewer, shorter enhancer-promoter contacts compared to the complex, long-range interactions formed by many LRGs (Beagan et al., 2020). Given the importance of cohesin and regulatory looping interactions for brain function, there is a strong imperative to understand the functional role of cohesin-dependent loops in the establishment and maintenance of developmentally regulated and activity-stimulated neuronal gene expression programs.

Here, we establish an experimental system to address the role of cohesin in 3D genome organization and gene expression in non-dividing, post-mitotic neurons, independently of essential cohesin functions in the cell cycle. We employ developmentally regulated expression of Cre recombinase under the control of the endogenous Neurod6 locus (referred to here as NexCre according to Goebbels et al., 2006; Hirayama et al., 2012) to inducibly deplete the cohesin subunit RAD21 specifically in immature post-mitotic mouse neurons in vivo. Cohesin depletion in immature post-mitotic mouse neurons disrupted CTCF-based chromatin loops, and reduced the expression of neuronal genes related to synaptic transmission, neuronal development, adhesion, connectivity, and signaling, resulting in impaired neuronal maturation. Neuronal ARGs were pervasively deregulated in cohesin-deficient neurons, consistent with a model in which the establishment of new gene expression programs in both macrophages and neurons is driven in part by cohesin-mediated enhancer-promoter contacts (Rajarajan et al., 2016; Yamada et al., 2019; Sams et al., 2016; Schaukowitch et al., 2014; Cuartero et al., 2018). The cohesin-dependence of ARGs was confirmed in an inducible system that allows for proteolytic cohesin cleavage in primary neurons (Weiss et al., 2021), providing temporal control over cohesin levels on a time scale similar to the establishment of neuronal gene expression programs upon neural stimulation, thus allowing us to disentangle the role for cohesin-mediated chromatin contacts in the maintenance of existing transcriptional programs versus the establishment of new transcriptional programs in neural circuits. Surprisingly, despite pervasive deregulation at baseline, most IEGs and a subset of LRGs remained fully inducible by KCl and BDNF stimulation after genetic or proteolytic depletion of cohesin.

Cohesin-dependent and -independent ARGs were distinguished not by the binding of cohesin or CTCF to their promoters (Schaukowitch et al., 2014; Sams et al., 2016), but instead by their 3D connectivity. LRGs that depended on cohesin for full inducibility engaged in longer chromatin loops than IEGs, or LRGs that remained fully inducible in the absence of cohesin. Unlike ARGs, the majority of neuronal genes that mediate synaptic transmission and neurotransmitter signaling are constitutively expressed. Nevertheless, as with ARGs, the reliance of these key neuronal genes on cohesin scaled with chromatin loop length. Consistent with a model where short-range enhancer-promoter loops can form in the absence of cohesin, we find that the enhancer activity and short-range enhancer-promoter contacts at the IEG Fos remained robustly inducible in cohesin-depleted neurons. Finally, re-expression of RAD21 protein in cohesin-depleted neurons re-established lost chromatin loops and restored wild-type expression levels of disrupted LRGs, and of constitutive neuronal genes engaged in long-range loops. Together, our data support a model where key neuronal genes required for the maturation and activation of primary neurons require cohesin for their full expression. The degree to which neuronal genes rely on cohesin scales with the genomic distance traversed by their chromatin loops, including loops connecting promoters with activity-induced enhancers.

Results

Conditional deletion of cohesin in immature post-mitotic neurons

To explore the role of cohesin in post-mitotic neurons, we deleted the essential cohesin subunit RAD21 (Rad21lox, Seitan et al., 2011) in immature cortical and hippocampal neurons using NexCre (Goebbels et al., 2006; Hirayama et al., 2012). Explant cultures of wild type and Rad21lox/lox NexCre E17.5/18.5 cortex contained >95% MAP2+ neurons, with <1% GFAP+ astrocytes or IBA1+ microglia (Figure 1a). Immunofluorescence staining showed the loss of RAD21 protein specifically in GAD67- neurons (Figure 1b and c). This was expected, as NexCre is expressed by excitatory but not by inhibitory neurons (Goebbels et al., 2006; Hirayama et al., 2012). Consistent with the presence of ~80% of GAD67- and ~20% GAD67+ neurons in the explant cultures, Rad21 mRNA expression was reduced by 75–80% overall (Figure 1d, left). There was a corresponding reduction in RAD21 protein (Figure 1d, middle). To focus our analysis on cohesin-deficient neurons we combined NexCre-mediated deletion of Rad21 with NexCre-dependent expression of an epitope-tagged ribosomal subunit (Rpl22-HA RiboTag; Sanz et al., 2009). We verified that NexCre-induced RPL22-HA expression was restricted to RAD21-depleted neurons (Figure 1—figure supplement 1a) and performed high throughput sequencing of Rpl22-HA RiboTag-associated mRNA (RiboTag RNA-seq, Supplementary file 1). Comparison with total RNA-seq (Supplementary file 2) showed that NexCre RiboTag RNA-seq captured excitatory neuron-specific transcripts, such as Slc17a7 and Camk2a. Transcripts selectively expressed in astrocytes (Gfap, Aqp4, Mlc1), microglia (Aif1), and inhibitory neurons (Gad1, Gad2, Slc32a1) were depleted from NexCre RiboTag RNA-seq (Figure 1—figure supplement 1b). RiboTag RNA-seq enabled an accurate estimate of residual Rad21 mRNA, which was <5% in Rad21lox/lox NexCre cortical neurons (Figure 1d, right). These data show near-complete loss of Rad21 mRNA and undetectable levels of RAD21 protein in NexCre-expressing Rad21lox/lox neurons. Analysis of chromatin conformation by Chromosome-Conformation-Capture-Carbon-Copy (5 C) showed a substantial reduction in the strength of CTCF-based chromatin loops after genetic (Rad21lox/lox NexCre, Figure 1e) and after proteolytic cohesin depletion (RAD21-TEV, Figure 1—figure supplement 2; Weiss et al., 2021). Of note, restoration of RAD21 expression rescued CTCF-based chromatin loop formation (Figure 1—figure supplement 2). These data show that cohesin is directly linked to the strength of CTCF-based chromatin loops in primary in post-mitotic neurons.

Figure 1 with 2 supplements see all
Conditional cohesin deletion in post-mitotic neurons.

(a) E17.5–E18.5 cortices were dissociated and plated on poly-D-lysine. After 10 days, cultures were stained for pan neuronal (MAP2), astrocyte (GFAP), and microglia (IBA1) markers, and cell type composition was determined by quantitative analysis of immunofluorescence images. Based on 6 Rad21+/+ NexCre and 8 Rad21lox/lox NexCre different samples analyzed in four independent experiments. (b) Immunofluorescence staining of Rad21+/+ NexCre and Rad21lox/lox NexCre neuronal explant cultures for RAD21 and MAP2 (left) and distribution of RAD21 expression by MAP+ neurons (right). Note the discontinuous distribution of RAD21 expression in Rad21lox/lox NexCre neurons. Three independent experiments per genotype. DAPI marks nuclei. Scale bar = 60 μm. (c) Immunofluorescence staining for RAD21, MAP2, and the marker of GABAergic inhibitory neurons, GAD67 (left). Distribution of RAD21 expression in GAD67+ and GAD67- neurons (right). Note that the discontinuous distribution of RAD21 expression in Rad21lox/lox NexCre neuronal explant cultures is due to GAD67+ GABAergic inhibitory neurons. Three independent experiments for Rad21+/+ NexCre and six independent experiments for Rad21lox/lox NexCre. DAPI marks nuclei. Scale bar = 20 μm. (d) Quantitative RT-PCR analysis of Rad21 mRNA expression in Rad21+/+ NexCre and Rad21lox/lox NexCre cortical explant cultures (mean ± SEM, n=18). Hprt and Ubc were used for normalization (left). RAD21 protein expression in Rad21+/+ NexCre and Rad21lox/lox NexCre cortical explant cultures was quantified by fluorescent immunoblots (mean ± SEM, n=6, a representative blot is shown in Figure 1—figure supplement 1) and normalized to LaminB (center). NexCre RiboTag RNA-seq of analysis of Rad21 mRNA expression in Rad21+/+ NexCre and Rad21lox/lox NexCre cortical explant cultures (right, three independent biological replicates). (e) 5C heat maps of Rad21+/+ NexCre and Rad21lox/lox NexCre cortical explant cultures. Shown is a 1.72 Mb region covered by 5C analysis of chr2 107601077–110913077 (Beagan et al., 2020). One of two independent biological replicates with similar results. CTCF ChIP-seq in cortical neurons (Bonev et al., 2017) and mm9 coordinates are shown for reference. Arrowheads mark the position of CTCF-based loops. Results were consistent across two replicates and three chromosomal regions. Histograms below show the quantification of representative CTCF-based loops (arrowheads) in two independent biological replicates for control and Rad21lox/lox NexCre neurons.

Figure 1—source data 1

Figure 1: Conditional cohesin deletion in post-mitotic neurons.

https://cdn.elifesciences.org/articles/76539/elife-76539-fig1-data1-v2.xlsx

Loss of cohesin from immature post-mitotic neurons perturbs neuronal gene expression

Using RiboTag RNA-seq to profile gene expression specifically in cohesin-depleted neurons we identified 1028 downregulated and 572 upregulated transcripts in Rad21lox/lox NexCre cortical neurons (Figure 2a, Figure 2—figure supplement 1a), with preferential deregulation of neuron-specific genes (p<2.2e-16, see methods). Gene ontology (Figure 2b) and gene set enrichment analysis (GSEA, Figure 2—figure supplement 1b) showed that downregulated genes in Rad21lox/lox NexCre neurons were enriched for synaptic transmission, neuronal development, adhesion, connectivity, and signaling (Supplementary file 3) and showed significant overlap with genes linked to human ASD (p=5.10E-15, Figure 2—figure supplement 1c; Banerjee-Basu and Packer, 2010). Upregulated genes showed no comparable functional enrichment (Figure 2b). Inducible ARGs such as Fos, Arc, and Bdnf are activity-regulated by definition, and hence lowly expressed in basal conditions due to spontaneous synaptic activity. In Rad21lox/loxNexCre neurons, previously defined inducible ARGs (Kim et al., 2010) were more frequently downregulated than constitutively expressed genes (Figure 2c, p=1.03e-16, odds ratio = 3.27). These data show that immature post-mitotic neurons require cohesin to establish and/or maintain the correct level of expression of genes that support neuronal maturation, including the growth and guidance of axons, the development of dendrites and spines, and the assembly, function, and plasticity of synapses.

Figure 2 with 1 supplement see all
Loss of cohesin from immature post-mitotic neurons perturbs neuronal gene expression.

(a) Volcano plot representing log2 fold-change (FC) versus significance (-log10 of adjusted p values) of downregulated genes (1028) and upregulated genes (572) in RiboTag RNA-seq of Rad21lox/lox NexCre versus Rad21+/+ NexCre neurons (Supplementary file 1). Red marks Rad21. (b) Analysis of gene ontology of biological functions of deregulated genes in Rad21lox/lox NexCre neurons. Enrichment is calculated relative to expressed genes (Supplementary file 3). (c) The percentage of constitutive (adj. p>0.05 in KCl 1 hr versus TTX and KCl 6 hr versus TTX, see methods) and activity-regulated genes Kim et al., 2010 found deregulated in Rad21lox/lox NexCre neurons in explant culture at baseline as determined by RiboTag RNA-seq. The p-value (Fisher Exact Test) and Odds ratio indicate that ARGs are more frequently deregulated than constitutive genes.

Figure 2—source data 1

Figure 2: Loss of cohesin from immature post-mitotic neurons perturbs neuronal gene expression.

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A role for cohesin in the maturation of post-mitotic neurons

We next set out to examine the impact of cohesin deletion on the maturation of immature post-mitotic neurons. Rad21lox/loxNexCre embryos were found at the expected Mendelian ratios throughout gestation, however postnatal lethality was evident (Figure 3—figure supplement 1a). Rad21lox/lox NexCre cortical neurons did not show increased proliferation (Figure 3—figure supplement 1b), no upregulation of apoptosis markers or signs of DNA damage (Figure 3—figure supplement 1b) and no stress-related gene expression (Figure 3—figure supplement 1c). Brain weight (Figure 3—figure supplement 1d) and cellularity (Figure 3—figure supplement 1e) were comparable between Rad21+/+ and Rad21lox/lox NexCre neocortex. The neuronal transcription factors TBR1, CTIP2, and CUX1 were expressed beyond the boundaries of their expected layers in Rad21lox/lox NexCre cortices, and deeper cortical layers appeared disorganized (Figure 3a). While the total numbers of TBR1+ and CTIP2+ neurons were comparable in wild type and Rad21lox/lox cortices, TBR1+ and CTIP2+ neurons were reduced in the subplate and increased in layers 6 and 7 Rad21lox/lox NexCre cortices (Figure 3a). These findings are consistent with the reported cohesin-dependence of neuronal guidance molecule expression (Kawauchi et al., 2009; Remeseiro et al., 2012; Guo et al., 2015) and migration (van den Berg et al., 2017). To assess the impact of cohesin on morphological maturation, we cultured cortical neurons in the presence of wild type glia (Kaech and Banker, 2006). Compared to freshly explanted E18.5 neurons (Figure 3b), neurons acquired considerable morphological complexity after 14 days in explant culture (Figure 3c). We sparsely labeled neurons with GFP to visualize processes of individual neurons (Figure 3d) and used Sholl analysis (Sholl, 1953) to quantitate the number of axonal crossings, the length of dendrites, the number of terminal points, the number of branch points and the number of spines in GAD67-negative Rad21+/+ NexCre and GAD67-negative Rad21lox/lox NexCre neurons. Cohesin-deficient neurons displayed reduced morphological complexity across scales, with reduced numbers of axonal branch and terminal points (Figure 3c and d). In addition, Rad21lox/lox NexCre neurons showed reduced numbers of dendritic spines, the location of neuronal synapses (Figure 3e). Taken together, these data show that the changes in neuronal gene expression that accompany cohesin deficiency have a tangible impact on neuronal morphology, and that cohesin is required for neuronal maturation.

Figure 3 with 1 supplement see all
Cohesin contributes to the maturation of post-mitotic neurons.

(a) Top: Schema of cortical layers (Greig et al., 2013) showing subplate (SP), layer 6 (VI), layer 5 (V), the cortical plate (CP), and the marginal zone (MZ). Middle: Immunofluorescence analysis of the neuronal transcription factors CUX1, TBR1, and CTIP2 at E16.5. Scale bar = 100 μm. Representative of three biological replicates. Bottom: Quantification of TBR1+ and CTIP2+ neurons in the subplate (SP) and in layers 5 and 6 (LV and VI). Neuron counts per 150 × 300 μm field are shown for five comparable sections from two embryos per genotype. Mean ± SE, *** adj. p<0.0001, two-way ANOVA with Sidak’s multiple comparisons test. (b) Morphology of E18.5 neurons after 1d in explant culture. Immunofluorescence staining for the pan-neuronal marker MAP2, tubulin beta 3 (TUBB3), and DAPI. Scale bar = 20 μm. (c) Morphology of Rad21+/+ NexCre and Rad21lox/lox NexCre cortical neurons in explant culture on rat glia (Kaech and Banker, 2006). Cultures were sparsely labeled with GFP to visualize individual cells and their processes, and stained for GAD67 to exclude GABAergic neurons. Dendritic traces of GFP+ neurons. Scale bar = 50 μm. (d) Sholl analysis of Rad21+/+ NexCre and Rad21lox/lox NexCre cortical neurons in explant cultures shown in (c). Shown is the number of crossings, dendritic length, terminal points, and branch points per 10 μm. Three independent experiments, 32 Rad21lox/lox NexCre and 28 Rad21+/+ NexCre neurons. * adj. p<0.05, ** adj. p<0.01, *** adj. p<0.001, **** adj. p<0.0001. Scale bar = 10 μm. (e) Quantification of spines per 10 μm for Rad21+/+ NexCre and Rad21lox/lox NexCre cortical neurons. Two independent experiments, 10 Rad21lox/lox NexCre and 10 Rad21+/+ NexCre neuron. **** adj. p<0.0001. Scale bar = 10 μm.

Figure 3—source data 1

Figure 3: Cohesin contributes to the maturation of post-mitotic neurons.

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Activity-regulated gene expression is sensitive to acute depletion of cohesin

Neuronal maturation and ARG expression are closely connected. The expression of inducible ARGs promotes neuronal maturation, morphological complexity, synapse formation, and connectivity. In turn, neuronal maturation, morphological complexity, synapse formation, and connectivity facilitate ARG expression (Gallo et al., 2018; Kim et al., 2010; Malik et al., 2014; Greer and Greenberg, 2008; Tyssowski et al., 2018; Yap and Greenberg, 2018). To address whether the downregulation of ARGs observed in Rad21lox/lox NexCre neurons was cause or consequence of impaired maturation we examined gene expression changes 24 hr after acute proteolytic degradation of RAD21-TEV depletion in post-mitotic neurons (Weiss et al., 2021). RNA-seq showed highly significant overlap in gene expression between acute degradation of RAD21-TEV and genetic cohesin depletion in Rad21lox/lox NexCre neurons (p<2.22e-16, odds ratio = 8.24 for all deregulated genes, odds ratio = 23.81 for downregulated genes; Figure 4a). Differentially expressed genes were enriched for ontologies related to synapse function, cell adhesion, and neuronal/nervous system development, and showed expression changes that were correlated across cohesin depletion paradigms (Synapse: p<2.22e−16, odds ratio = 11.26, RS = 0.62; Adhesion: p<2.22e−16, odds ratio = 15.21, RS = 0.6; Neuronal and nervous system development: p<2.22e−16, odds ratio = 9.92, RS = 0.58; Figure 4b). Notably, ARGs were enriched among deregulated genes in response to acute RAD21-TEV cleavage (p<2.22e-16, Odds Ratio = 6.48), and were preferentially downregulated (Figure 4c). Thus, we conclude that activity-regulated genes and genes that facilitate neuronal maturation and homeostasis are directly affected by the acute depletion of cohesin, and not just as a result of impaired neuronal maturation.

Acute cohesin depletion deregulates ARG expression.

(a) Western blot documenting acute RAD21 depletion by 4-OHT-inducible RAD-TEV cleavage (left). GSEA of the gene set downregulated (DEseq2, adj. p<0.05) in RAD21-TEV neurons in Rad21lox/lox NexCre neurons (center). GSEA of genes downregulated in Rad21lox/lox NexCre neurons (DEseq2, adj. p<0.05) in RAD21-TEV neurons (right). NES: normalized enrichment score. FDR: false discovery rate. (b) Scatter plots of gene expression within aggregate GO terms, comparing RAD21-TEV with Rad21lox/lox NexCre neurons. Genes that were found deregulated in at least one of the genotypes are shown. p-values and odds ratios refer to the probability of finding the observed patterns of co-regulation by chance. RS: Spearman’s rank coefficient. (c) Deregulation of constitutive and activity-regulated genes 24 hr after acute cohesin depletion by inducible proteolytic cleavage of RAD21-TEV; adj. p<0.05 based on DEseq2 analysis of three RNA-seq replicates per experiment. Blue indicates downregulation and yellow indicates upregulation in RAD21-TEV versus wild type. Two independent experiments are shown (Weiss et al., 2021 and Supplementary file 4).

Activity-regulated gene classes differ with respect to their reliance on cohesin

The disruption of baseline ARG expression suggested that cohesin-deficient neurons may be unable to induce the same activity-dependent gene expression program as wild-type neurons. To address this possibility, we performed RNA-seq of neuronal explant cultures treated either with tetrodotoxin +D-AP5 (TTX) alone to block neuronal signaling, or treated with TTX followed by 1 or 6 hr of sustained KCl exposure to induce neuronal depolarization. The fraction of constitutive genes deregulated in Rad21lox/lox NexCre versus control neurons remained similar across conditions (baseline, TTX, 1 hr and 6 hr KCl, Figure 5a, top). By contrast, approximately 50% (154/305) of ARGs (Kim et al., 2010) were downregulated in Rad21lox/lox NexCre versus control neurons under baseline conditions (Figure 5—figure supplement 1a). Of these, 76 were induced to control expression levels by KCl in Rad21lox/lox NexCre neurons, while 29% failed to reach control levels, and 16% were expressed at increased levels (Figure 5—figure supplement 1a). Multifactor analysis and hierarchical clustering (Figure 5—figure supplement 1b) showed that baseline ARG expression was more similar to TTX in Rad21lox/lox NexCre than in control neurons. This was confirmed by dendrogram distances (Figure 5—figure supplement 1c) and principal component analysis (Figure 5—figure supplement 1d), and statistically validated by the fraction of ARGs that changed expression between baseline and TTX conditions (49.5% in control neurons versus 28.5% in Rad21lox/lox NexCre neurons; p=5.89e-11, Figure 5—figure supplement 1e). Taken together, these data show that ARG expression is reduced in Rad21lox/lox NexCre neurons under baseline conditions, but remains responsive to activation. Among neuronal ARG classes, IEGs and LRGs are known to differ in their 3D connectivity in that LRGs engage in longer-range chromatin contacts than IEGs in primary cortical neurons (Beagan et al., 2020; see methods for the definition of IEGs and LRGs). However, the functional consequences of this difference in 3D connectivity remain to be explored. We therefore asked whether IEGs and LRGs differ with respect to their reliance on cohesin. While IEGs were induced to at least wild-type levels in cohesin-deficient neurons by stimulation with KCl (Figure 5a) or BDNF (Figure 5b), a substantial fraction of LRGs remained downregulated in cohesin-deficient neurons across conditions (Figure 5c). To test whether the expression of LRGs that remained downregulated in cohesin-deficient neurons across conditions was indeed cohesin-dependent, we restored RAD21 levels following transient proteolytic cleavage of RAD21-TEV. We found that the expression of LRGs was rescued by restoration of RAD21 (Figure 5d). These data show that there are two classes of ARGs: (i) IEGs/LRGs that exhibit altered baseline expression but can fully regain expression in response to activation, and (ii) a subset of LRGs that remain deregulated in response to activation.

Figure 5 with 1 supplement see all
Activity-regulated neuronal gene (ARG) classes differ in their reliance on cohesin.

(a) Pie charts show the expression of constitutive genes (top), immediate early genes (IEGs) (center), and late response genes (LRGs) (bottom) in Rad21 NexCre neurons under four different conditions: baseline, TTX and D-AP5 (TTX), and in response to KCl stimulation for 1 hr or 6 hr. Numbers of expressed constitutive genes, IEGs, and LRGs are given on the left. Note that KCl stimulation normalizes the expression of most (10 out of 11) IEGs downregulated in TTX, but a fraction of LRGs remain downregulated. p-values test the prevalence of deregulated genes in each class under each condition, two-sided Fisher exact test. (b) Pie charts show the expression of IEGs (top) and LRGs (bottom) in RAD21-TEV neurons under baseline conditions 24 hr after ERt2-TEV induction and in response to BDNF (120 min). RAD21-TEV cleavage led to the downregulation (adj. p<0.05) of 13 out of 18 expressed IEGs and of 23 out of 101 expressed LRGs. p-values test the prevalence of downregulated IEGs and LRGs with and without BDNF stimulation, two-tailed Fisher exact test. Note that BDNF stimulation reversed the downregulation of IEGs but not LRGs in cohesin-depleted neurons. (c) Strip plots depict the expression of IEGs, LRGs that are downregulated in Rad21 NexCre neurons compared to control across conditions (TTX and 6 hr KCl), and LRGs that are not downregulated in Rad21 NexCre neurons compared to control across conditions. (d) Transient cohesin depletion and re-expression as in Figure 1—figure supplement 2. Pie charts show the expression of LRGs in RAD21-TEV relative to control neurons. 24 out of 97 LRGs expressed in RAD21-TEV neurons were downregulated 24 hr after Dox-dependent TEV induction (adj. p<0.05). The downregulation of LRGs was reversible upon Dox washout and restoration of RAD21 expression.

Figure 5—source data 1

Figure 5: Activity-regulated neuronal gene (ARG) classes differ in their reliance on cohesin.

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Cohesin-dependent neuronal genes have longer chromatin loops

To explore features that may explain why a subset of ARG requires cohesin for full expression we analyzed ChIP-seq data for cohesin and CTCF binding to ARG promoters in wild-type neurons. ARG promoters are enriched for binding of CTCF (OR = 1.621, p=0.022, two-tailed Fisher Exact Test), the cohesin subunit RAD21 (OR = 1.866, p=0.005), and cohesin in the absence of CTCF (cohesin-non-CTCF, OR = 1.621, p=0.022) compared to non-ARGs expressed in cortical neurons. Within the ARG gene set, however, there were no significant differences in CTCF, RAD21, or RAD21-non-CTCF ChIP-seq binding at the promoters of IEGs (which as a group remained fully inducible in cohesin-deficient neurons), the subset of LRGs that were downregulated across conditions in Rad21 NexCre neurons (both TTX and 6 hr KCl, adj p<0.05), and LRGs that remained fully inducible across conditions in Rad21 NexCre neurons (both TTX and 6 hr KCl, adj p>0.05, Figure 6—figure supplement 1a). Therefore, while ARG promoters are enriched for CTCF, RAD21, and cohesin-non-CTCF binding, this binding is not predictive of which ARGs remain fully inducible, and which are downregulated in cohesin-deficient neurons.

To test whether cohesin-dependence of ARG regulation might instead be linked to the genomic range of chromatin loops formed by these genes we analyzed high-resolution cortical neuron Hi-C data (Bonev et al., 2017). We found that the subset of LRGs that required cohesin for full expression in TTX and full induction by 6 hr KCl (adj p<0.05) formed significantly longer Hi-C loops than IEGs and LRGs that were not deregulated across conditions (TTX and 6 hr KCl, adj p>0.05) in Rad21 NexCre neurons (Figure 6a). Chromatin loops with CTCF binding at one or both loop anchors were also significantly longer for cohesin-dependent LRGs than for IEGs and cohesin-independent LRGs (Figure 6a, red). Chromatin loops that connect promoters with inducible enhancers also tended to span larger genomic distances at downregulated LRGs compared to IEGs or non-deregulated LRGs, even though due to the limited numbers of enhancer-promoter loops associated with each LRG class, these trends do not reach statistical significance (Figure 6a, blue). Overall, the degree to which ARGs depend on cohesin for their correct expression correlates with the length of chromatin loops they form.

Figure 6 with 1 supplement see all
The genomic distance traversed by chromatin contacts formed by neuronal genes predicts whether or not cohesin is required for their full expression.

(a) The span of Hi-C loops (left), Hi-C loops with CTCF bound to at least one of the loop anchors (middle) and Hi-C loops between promoters and inducible enhancers (right) for Immediate early genes (IEGs) (n=18) and late response genes (LRGs) downregulated in Rad21 NexCre versus control neurons in both resting (TTX) and activation conditions (6 hr KCl, adj p<0.05 in both TTX and 6 hr KCl conditions, n=22, ‘Downreg. LRG’), and LRGs not deregulated in either resting (TTX) or activation conditions (6 hr KCl, adj p>0.05 in both TTX and 6 hr KCl conditions) in Rad21 NexCre relative to control neurons (adj. p>0.05, n=43, 'Non-dereg. LRG’). Box plots show the longest loop for each gene rather than average loop length, as Hi-C loop calling at 10 kb resolution precludes detection of loops <40 kb (Beagan et al., 2020). However, analysis of average loop length confirmed that downregulated genes form longer loops than non-deregulated genes among both ARGs and neuronal GO term genes (p=0.0056 and p<0.0001, respectively). Genes without loops are included except for analysis of enhancer loops. Box plots show the longest loop recorded for each gene. Boxes show upper and lower quartiles and whiskers show 1.5 of the interquartile range. p-values were determined by non-parametric Kolmogorov-Smirnov test. Strip plots depict the expression of IEGs, downregulated LRGs, and non-deregulated LRGs in wild-type and Rad21 NexCre neurons. (b) Transient cohesin depletion and re-expression as in Figure 1—figure supplement 2. Pie charts show the expression of neuronal genes related to synaptic transmission (GO:0007268) and glutamate receptor signaling pathway (GO:0007215). 77 out of 583 expressed genes in these GO terms (Neur. GO term genes) were downregulated 24 hr after Dox-dependent TEV induction (adj. p<0.05). The downregulation of 76 of 77 neuronal GO term genes was reversible upon Dox washout and restoration of RAD21 expression, an additional 6 genes were downregulated after Dox washout but not at 24 hr of TEV induction. (c) The span of Hi-C loops (left), Hi-C loops with CTCF bound to at least one of the loop anchors (middle) and Hi-C loops between promoters and constitutive or inducible enhancers (right) for genes in the neuronal GO terms synaptic transmission and glutamate receptor signaling. Gene expression in Rad21 NexCre versus control neurons was assessed in both resting and activation conditions: not deregulated in TTX or 6 hr KCl, n=401, Downregulated in both TTX and 6 hr KCl, n=85, Upregulated in both TTX and 6 hr KCl, n=41. Genes do not form loops are included except for analysis of enhancer loops. Boxes show upper and lower quartiles and whiskers show 1.5 of the interquartile range. p-values were determined by non-parametric Kolmogorov-Smirnov test. Strip plots show the expression of the depicted GO term genes in control and Rad21 NexCre neurons.

Figure 6—source data 1

Figure 6: The genomic distance traversed by chromatin contacts formed by neuronal genes predicts whether or not cohesin is required for their full expression.

https://cdn.elifesciences.org/articles/76539/elife-76539-fig6-data1-v2.xlsx

We next addressed whether cohesin/CTCF binding or chromatin loop length were important factors for the impact of cohesin on the expression of additional neuronal genes. We focused on the neuronal GO terms ‘synaptic transmission’ (GO:0007268) and ‘glutamate receptor signaling pathway’ (GO:0007215) because these gene ontologies were highly enriched among downregulated genes both in acute RAD21-TEV cleavage and genetic cohesin depletion, and remained enriched across conditions in Rad21 NexCre neurons (GO:0007268: 87 of 519 genes downregulated at 6 hr KCl, adj. p<0.05, p-value for enrichment = 2.99E-07; GO:0007215: 21 of 68 genes downregulated at 6 hr KCl, adj p<0.05, p-value for enrichment = 6.46E-06). The majority of synaptic transmission and glutamate receptor signaling genes are classified as constitutive (62.7% of expressed and 59.8% of downregulated GO:0007268 and GO:0007215 genes across conditions), rather than activity-regulated (1.8% of expressed and 1.2% of downregulated GO:0007268 and GO:0007215 genes cross conditions), thus complementing the analysis of ARGs. Of note, restoration of RAD21 after transient depletion in the RAD21-TEV system rescued the expression of 90% (70 of 77) downregulated synaptic transmission and glutamate receptor signaling genes (Figure 6b), confirming that their expression was indeed dependent on cohesin.

As described above for ARGs, the TSSs of neuronal GO term genes related to synaptic transmission and glutamate receptor signaling were enriched for binding of CTCF (OR = 1.409, p<0.0001, two-tailed Fisher Exact Test), RAD21 (OR = 1.745, p<0.0001), and cohesin-non-CTCF (OR = 1.585, p<0.0001) cCompared to the remaining expressed genes in cortical neurons. However, and again as described above for ARGs, the binding of CTCF, RAD21, or cohesin-non-CTCF to the promoters of neuronal GO term genes did not predict which neuronal GO term genes remained expressed at wild-type levels, and which were downregulated in Rad21 NexCre neurons (Figure 6—figure supplement 1b).

Notably, however, synaptic transmission and glutamate receptor signaling genes that were downregulated in Rad21lox/lox NexCre neurons engaged in significantly longer-range chromatin loops than genes that were either not deregulated or upregulated (Figure 6c, black, strip plots show the expression of the depicted GO term genes in control and Rad21lox/lox NexCre neurons). As with ARGs, this pattern extended to Hi-C loops with CTCF binding at one or both loop anchors (Figure 6c, red). The subset of synaptic transmission and glutamate receptor signaling genes that were downregulated in Rad21lox/lox NexCre neurons formed significantly longer Hi-C loops connecting promoters and enhancers than genes in the same gene ontologies that were not deregulated (p<0.0001) or upregulated (p<0.0001) in Rad21 NexCre neurons (Figure 6c, blue). These data extend the relationship between chromatin loop length and cohesin-dependent expression from ARGs to constitutively expressed neuronal genes.

Cohesin is not essential for short-range loops between inducible enhancers and promoters at the activity-regulated Fos and Arc loci

Typical IEG enhancers such as Fos and Arc enhancers are fully accessible prior to stimulation (Carullo et al., 2020), and show increase H3K27ac and eRNA transcription in response to neuronal activation (Malik et al., 2014; Kim et al., 2010; Beagan et al., 2020; Joo et al., 2016; Carullo et al., 2020). To examine the contribution of enhancer activation and enhancer-promoter contacts to inducible ARG expression in Rad21lox/lox NexCre neurons we focused on the immediate early response gene Fos. Fos expression in Rad21lox/lox NexCre neurons was reduced at baseline and in the presence of TTX, but Fos expression remained fully inducible when Rad21lox/lox NexCre neurons were stimulated with KCl (Figure 7a) or with BDNF (Figure 7—figure supplement 1). The transcription of neuronal genes is controlled by neuronal enhancers (Rajarajan et al., 2016; Yamada et al., 2019; Sams et al., 2016; Kim et al., 2010; Malik et al., 2014; Schaukowitch et al., 2014; Beagan et al., 2020). Neuronal Fos enhancers in particular have been extensively characterized (Joo et al., 2016; Beagan et al., 2020), and interference with Fos enhancers precludes full induction of Fos gene expression (Joo et al., 2016). Fos enhancers 1, 2, and 5 are known to undergo activation-induced acetylation of H3K27 (H3K27ac) and active eRNA transcription in response to KCl stimulation (Joo et al., 2016). We found that activation-induced transcription of Fos enhancers did remain intact in Rad21lox/lox NexCre neurons (Figure 7b) and activation-induced H3K27ac of Fos enhancers was also preserved (Figure 7c).

Figure 7 with 3 supplements see all
Fos enhancer-promoter contacts are robustly induced in cohesin-deficient neurons.

(a) Expression of the immediate early genes (IEG) Fos at baseline, after TTX/D-AP5 (TTX), and KCl-stimulation (left, mean log2-transformed counts from 3 biological replicates, * adj. p<0.05). (b) Enhancer transcripts in control and Rad21lox/lox NexCre neurons were quantified based on normalized RNA-seq reads within 1 kb of the enhancer. An intergenic region on chr11 was used as a negative control (71.177.622–71.177.792) . (c) H3K27ac ChIP normalized to H3 in control and Rad21lox/lox NexCre neurons at a control site, Fos enhancer 1 and Fos enhancer 2 after TTX/D-AP5 (TTX) or 1 hr KCl (KCl). (d) Interaction frequency (top) and interaction score (bottom) heatmaps of the region immediately surrounding Fos obtained by 5C analysis of chr12 86201802–87697802 (Beagan et al., 2020). Black frames highlight interactions between the Fos gene and upstream enhancers 1 and 2. CTCF ChIP-seq in cortical neurons (Bonev et al., 2017) is shown for orientation and H3K27ac ChIP-seq in inactive (TTX-treated) and activated neurons is shown to annotate enhancer regions (Beagan and Phillips-Cremins, 2020; Beagan et al., 2020). RNA-seq in TTX-treated and 1 hr KCl-activated control and Rad21lox/lox NexCre neurons shows KCl-inducible transcription of Fos enhancers in wild -type and cohesin-deficient neurons. Two independent biological replicates are shown in Figure 7—figure supplement 3a. (e) Quantification of the interaction frequencies between the Fos promoter and Fos enhancer 1 (top), the Fos promoter and Fos enhancer 2 (middle), and CTCF-marked boundaries of the sub-TAD containing Fos (bottom, grey arrowhead). Two replicates per genotype and condition.

Figure 7—source data 1

Figure 7: Fos enhancer-promoter contacts are robustly induced in cohesin-deficient neurons.

https://cdn.elifesciences.org/articles/76539/elife-76539-fig7-data1-v2.xlsx

Given that Fos can be fully induced, and Fos enhancers are activated in the absence of cohesin, we next set out to understand if Fos can form previously reported looping interactions with its activity-stimulated enhancers (Beagan et al., 2020). We conducted 5 C to generate 10 kilobase resolution maps of chromatin loops around key IEGs and LRGs. Consistent with previous data (Beagan et al., 2020), Fos enhancers 1 and 2, which are located ~18 and ~ 38.5 kb upstream of the Fos TSS, showed inducible chromatin contacts with the Fos promoter that formed rapidly in response to activation of wild-type neurons (Figure 7d). Unexpectedly, Rad21lox/lox NexCre neurons retained the ability to robustly and dynamically induce loops between the Fos promoter and Fos enhancers 1 and 2 (Figure 7d). Quantification showed that inducible enhancer-promoter contacts at the Fos locus were of comparable strength in control and Rad21lox/lox NexCre neurons (Figure 7e, top and center), while a structural CTCF-based loop surrounding the Fos locus was substantially weakened (Figure 7e, bottom). Together these results reveal the surprising finding that the critical activity-stimulated IEG Fos can fully activate expression levels and form robust enhancer-promoter loops in the absence of cohesin.

We also examined the long-range regulatory landscape of the IEG Arc. The expression of the IEG Arc was reduced in Rad21lox/lox NexCre neurons at baseline, but, like Fos, Arc remained inducible by stimulation with KCl (Figure 7—figure supplement 2a, top) and BDNF (Figure 7—figure supplement 2a, bottom). Stimulation of wild-type neurons is known to trigger the formation of chromatin contacts between the Arc promoter and an Arc-associated activity-induced enhancer located ~15 kb downstream of the Arc TSS (Beagan et al., 2020; Figure 7—figure supplement 2b). As observed for Fos, Arc promoter-enhancer contacts were retained in Rad21lox/lox NexCre neurons (Figure 7—figure supplement 2b, c), while CTCF-based loops surrounding the Arc locus were weakened (Figure 7—figure supplement 2b, c). In contrast to control neurons, Arc promoter-enhancer contacts became at least partially independent of activation in Rad21lox/lox NexCre neurons (Figure 7—figure supplement 2b, c). Hence, cohesin is required for the correct baseline expression of ARGs, but largely dispensable for inducible transcription and for specific enhancer-promoter contacts at the IEGs Fos and Arc.

Cohesin-dependent long-range looping at the Bdnf locus

In contrast to IEGs Fos and Arc, the LRG Bdnf has at least eight promoters that initiate transcription of distinct mRNA transcripts, all of which contain the entire open reading frame for the BDNF protein (Aid et al., 2007). Bdnf promoter IV is specifically required for the neuronal activity-dependent component of Bdnf transcription in mouse cortical neurons (Hong et al., 2008). While overall Bdnf transcript levels were significantly reduced in Rad21lox/lox NexCre neurons only at baseline (log 2 FC = –1.16 adj p=0.003, Figure 8), Bdnf transcripts from the activity-dependent Bdnf promoter IV were specifically reduced in cohesin-deficient cortical neurons after 6 hr activation with KCl (log 2 FC = –1.26 adj p=9.24e-05) as well as baseline conditions (log 2 FC = –1.39 adj p=1.19e-05, Figure 8b). Bdnf promoter IV is located in the immediate vicinity of a strong CTCF peak in cortical neurons (Bonev et al., 2017; Figure 8c, bottom track). This CTCF peak forms the base of a constitutive loop between Bdnf promoter IV and an activity-induced enhancer located ~2 Mb upstream of the Bdnf gene (Figure 8c). The strength of this loop was substantially reduced in cohesin-deficient neurons (Figure 8c, quantification in Figure 8d). Hence, while the CTCF-based looping of Bdnf promoter IV to a distant inducible enhancer is cohesin-dependent, the activity-regulated expression of the IEGs Fos and Arc is linked to enhancer-promoter loops that span limited genomic distances ( <40 kb) and can form independently of cohesin.

Figure 8 with 1 supplement see all
Bdnf enhancer-promoter contacts are weakened in the absence of cohesin.

(a) Total Bdnf transcripts at baseline, after TTX/D-AP5 (TTX), and KCl-stimulation (left, mean log2-transformed counts from three biological replicates, * adj. p<0.05). (b) Bdnf promoter IV transcripts at baseline, after TTX/D-AP5 (TTX), and KCl-stimulation (left, mean log2-transformed counts from three biological replicates, * adj. p<0.05). (c) Interaction frequency (top) and interaction score (bottom) heatmaps of the Bdnf region obtained by 5C analysis of chr2 107601077-110913077 (Beagan et al., 2020). CTCF ChIP-seq in cortical neurons (Bonev et al., 2017) and the position of Bdnf are displayed (top). Below: Zoom-in of constitutive Bdnf enhancer-promoter loop (gray frame). Shown on the side is H3K27ac ChIP-seq in resting and activated neurons, marking an activity-dependent enhancer, and CTCF ChIP-seq. RNA-seq in resting and activated wild-type and cohesin deficient neurons and CTCF ChIP-seq are shown underneath. A circle marks an inducible 1.68Mb 5C loop between Bdnf and an activity-induced enhancer (Bdnf enhancer 1 in Beagan et al., 2020). (d) Quantification of 5C interaction frequencies between Bdnf promoter IV and the activity-dependent enhancer. (e) Quantification of inducible 5C loop between Bdnf and Bdnf enhancer 1 (Beagan et al., 2020).

The formation of full-strength inducible loops at the Bdnf locus requires extended stimulation (6 hr, Beagan et al., 2020), which was not performed here. Formation of an inducible Bdnf loop was nevertheless discernible 1 hr after KCl stimulation (Figure 8c and e). Quantification shows that the inducible Bdnf loop increases in strength in response to KCl in wild-type but not in cohesin-deficient neurons (Figure 8c and e), indicating that formation of this loop requires cohesin. With ~1.68 Mb, the inducible Bdnf loop spans a substantially larger genomic distance than the enhancer-promoter loops at the IEGs Fos and Arc (Beagan et al., 2020). Because longer loops can be more difficult to detect due to distance-dependent background signal, we also analyzed loop strength corrected for the distance-dependent background signal. This analysis confirmed the cohesin-dependence of longer loops (Figure 8—figure supplement 1). The finding that long-range inducible loop formation at the Bdnf locus is cohesin-dependent supports the model that cohesin is required for the formation of longer chromatin loops.

Discussion

Given that mutations in cohesin and CTCF cause intellectual disability in humans (Deardorff et al., 2018; Gregor et al., 2013; Rajarajan et al., 2016), the extent to which deficits in cohesin function alter neuronal gene expression remains a critical underexplored question. To define the role of cohesin in immature post-mitotic neurons we use experimental deletion of the cohesin subunit Rad21 during a precise developmental window of terminal neuronal differentiation in vivo. We find impaired neuronal maturation and extensive downregulation of genes related to synaptic transmission, connectivity, neuronal development, and signaling in Rad21lox/lox NexCre neurons. Such gene classes are central to neuronal identity, and their wide-spread downregulation is likely to contribute to the observed maturation defects of Rad21lox/lox NexCre neurons. Acute proteolysis of RAD21-TEV corroborated a prominent role for cohesin in the expression of genes that facilitate neuronal maturation, homeostasis, and activation.

We have recently shown that cohesin loss in macrophages results in severe disruption of anti-microbial gene expression programs in response to macrophage activation (Cuartero et al., 2018). By contrast, cohesin loss only moderately affected genes constitutively expressed in uninduced and induced macrophages, supporting a model where cohesin-mediated loop extrusion is more important for the establishment of new gene expression than the maintenance of existing programs (Cuartero et al., 2018). Here, we extend this model to post-mitotic neurons in the murine brain. The two major ARG classes, IEGs, and LRGs, were broadly downregulated in cohesin-deficient neurons at baseline. However, IEGs and a subset of LRGs remained fully inducible by KCl and BDNF stimulation in the absence of cohesin. IEG-encoded transcription factors such as the AP1 members encoded by Fos, FosB, and JunB facilitate the induction of LRGs (Malik et al., 2014). Our data show that IEG-encoded transcription factors remain fully inducible in cohesin-deficient neurons. The failure to induce a subset of LRGs to wild-type levels is therefore not explained by a lack of IEG-encoded factors. This is in marked contrast to inducible gene expression in cohesin-deficient macrophages. A substantial number of LRGs in macrophages rely on the expression of early-induced interferons (IFN), which act in an autocrine and paracrine manner to support LRG induction (Glass and Natoli, 2016). Expression of IFN-dependent LRGs can be partially rescued by provision of exogenous IFN to cohesin-deficient macrophages (Cuartero et al., 2018).

The reliance of ARGs on cohesin for full activity-induced expression was linked to the scale of chromatin interactions as quantified by the analysis of Hi-C loops. The subset of LRGs that exhibit defects in inducibility in the absence of cohesin is characterized by longer-range chromatin loops than either IEGs or LRGs that remain fully inducible in the absence of cohesin. In addition to defective chromatin architecture, deregulated expression of a particular gene may also arise from disturbances in upstream regulatory mechanisms, such as the activity of specific signaling pathways, or the expression of particular transcription factors. We are not aware of signaling pathways that would unambiguously distinguish the subset of neuronal LRGs that are fully induced in the absence of cohesin from the subset of neuronal LRGs that remain deregulated in the absence of cohesin. As discussed above, TFs encoded by IEGs are required for the induction of LRGs. Of note, these IEGs are fully induced in cohesin-deficient neurons.

The relationship between loop length and cohesin-dependence of neuronal gene expression extends to constitutively expressed cell type-specific neuronal genes. Neuronal genes related to synaptic transmission and glutamate receptor signaling that were downregulated in cohesin-deficient neurons also engaged in significantly longer chromatin loops than genes in the same GO terms that were not deregulated.

A subset of looping interactions made by ARGs and neuron-specific genes involve distal enhancers, suggesting that one role for cohesin in the expression of these genes may be to facilitate enhancer-promoter contacts. Our data indicate that specific enhancer-promoter loops at the key neuronal IEGs Fos and Arc can occur independently of cohesin in primary neurons. Fos enhancer-promoter loops remained responsive to environmental signals. Of note, Fos and Arc enhancer-promoter loops that were robust to cohesin depletion are relatively short-range ( <40 kb). By contrast, an inducible 1.68 Mb enhancer-promoter loop at the Bdnf locus, a constitutive chromatin loop between Bdnf promoter IV and an activity-induced enhancer, and all CTCF-based loops examined were substantial weakened in the absence of cohesin. While longer loops are inherently weaker than shorter loops, the observed differences in loop strength were robust to correction for the distance-dependent background signal. This indicates that our 5 C approach reliably quantifies the strength of both long and short loops.

In earlier studies, we found that contacts between the Lefty1 promoter and the +8 kb enhancer, and between Klf4 and enhancers at +53 kb remained intact in acutely cohesin-depleted ES cells (Lavagnolli et al., 2015). Synthetic activation of a Shh enhancer ~100 kb upstream of the TSS supported transcriptional activation of cohesin, while activation of a + 850 kb enhancer did not (Kane et al., 2021). Analysis of engineered enhancer landscapes in K562 cells indicates graded distance effects: Enhancers at ≥100 kb and 47 kb were highly and moderately dependent on cohesin, respectively, while loss of cohesin actually increased target gene transcription for enhancer distances ≤11 kb (Rinzema et al., 2021). Finally, promoter capture Hi-C in cohesin-depleted HeLa cells indicates ranges of 104–105 bp for retained and 105–106 bp for lost interactions (Thiecke et al., 2020). While these studies suggest that cohesin-dependence of chromatin contacts relates to genomic distance, they fail to link this observation to physiologically relevant gene expression. The new data described here demonstrate scaling of cohesin-dependence with genomic distance in primary neurons, and, importantly, link this finding to critical genome functions, specifically the implementation of cell type-specific gene expression programs during neuronal maturation and activation.

An open question concerns the mechanisms of enhancer-promoter contacts in the absence of cohesin. Current models of 3D genome folding posit competition between two forces, cohesin-mediated loop extrusion and condensate-driven compartmentalization (Rao et al., 2017; Nora et al., 2017; Schwarzer et al., 2017; Beagan and Phillips-Cremins, 2020). RNAP2, Mediator, the transactivation domains of sequence-specific transcription factors, and the C-terminal domain of the chromatin reader BRD4 are thought to support the formation of molecular condensates enriched for components of the transcriptional machinery (Sabari et al., 2018; Boija et al., 2018; Rowley and Corces, 2018; Hsieh et al., 2020). The activation of inducible ARG enhancers involves dynamic H3K27ac (Beagan et al., 2020), recruitment of RNA polymerases, and active transcription (Joo et al., 2016). Our data show that H3K27ac and transcription remain inducible at Fos enhancers in Rad21lox/lox NexCre neurons, which could potentially contribute to loop formation. At the Arc locus, the persistence of an enhancer-promoter loop in unstimulated cohesin-deficient neurons would be consistent with a role for cohesin-mediated loop extrusion in chromatin state mixing, and the separation of enhancer contacts (Rao et al., 2017). Notably, enhancer-promoter contacts resist the inhibition of BET proteins (Crump et al., 2021), selective degradation of BRD4 (Crump et al., 2021), Mediator (El Khattabi et al., 2019; Crump et al., 2021), RNA Polymerase II (Thiecke et al., 2020), or inhibition of transcription (El Khattabi et al., 2019). These observations suggest that numerous components of the transcriptional machinery may redundantly support associations between active genes and regulatory elements (Sabari et al., 2018; Boija et al., 2018). Nevertheless, phase separation-like forces provide an attractive hypothesis for the manner by which cohesin-independent enhancer-promoter loops may form.

In summary, our data demonstrate that the extent to which the establishment of activity-dependent neuronal gene expression programs relies on cohesin-mediated loop extrusion depends at least in part on the genomic distances traversed by their long-range chromatin contacts.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Genetic reagent (Mus musculus)Rad21lox
Rad21tm1.1Mmk
DOI: 10.1038/nature10312MGI:5293824
Genetic reagent (Mus musculus)NexCre
Neurod6tm1(cre)Kan
DOI: 10.1002/dvg.20256MGI:2668659
Genetic reagent (Mus musculus)Rpl22(HA)lox (RiboTag)
Rpl22tm1.1Psam
DOI: 10.1073/pnas.0907143106MGI:4355967
Genetic reagent (Mus musculus)Rad21tev
Rad21tm1.1Kktk
DOI: 10.1101/gad.605910MGI:4840469
antibodyanti-RAD21 (rabbit polyclonal)AbcamCat #. ab154769WB: (dilution 1:1000)
IF: (dilution 1:500)
antibodyanti-LAMIN B (goat polyclonal)Santa Cruz BiotechnologyCat #. sc-6216WB: (dilution 1:10,000)
antibodyanti-rabbit IgG (H + L) Alexa Fluor 680 (goat polyclonal)ThermoFisher ScientificCat #. A-21109WB: (dilution 1:10,000)
antibodyanti-goat IgG (H + L) Alexa Fluor 680 (donkey polyclonal)ThermoFisher ScientificCat #. A-21084WB: (dilution 1:10,000)
antibodyanti-HA (rabbit polyclonal)SigmaCat #. H6908polysome immunoprecipitation
antibodyanti-GFAP (rabbit polyclonal)WakoCat #. Z0334IF: (dilution 1:500)
antibodyanti-MAP2 (chicken polyclonal)AbcamCat #. ab611203IF: (dilution 1:5000)
antibodyanti-GAD67 (mouse monoclonal)MilliporeCat #. MAB5406IF: (dilution 1:500)
antibodyanti-HA (mouse monoclonal)CovanceCat #. MMS-101RIF: (dilution 1:1000)
antibodyIBA1 (rabbit polyclonal)WakoCat #. 019–19741IF: (dilution 1:250)
antibodyanti-TUBB3 (Tuj1, mouse monoclonal)BiolegendCat #. 801,202IF: (dilution 1:500)
antibodyanti-gamma-H2AX (rabbit polyclonal)Bethyl LaboratoriesCat #. A300-081AIF: (dilution 1:3000)
antibodyanti-Cleaved Caspase-3 (Asp175) (rabbit polyclonal)Cell signallingCat #. 9,661IF: (dilution 1:400)
antibodyanti-TBR1 (rabbit polyclonal)AbcamCat #. ab31940IF: (dilution 1:1000)
antibodyanti-CTIP2 (25B6, rat monoclonal)AbcamCat #. ab18465IF: (dilution 1:500)
antibodyanti-CUX-1 (rabbit polyclonal)Santa Cruz BiotechnologyCat #. sc-13024IF: (dilution 1:400)
antibodyanti–Phospho-Histone H3 S10 Alexa Fluor 647 conjugate (rabbit polyclonal)Cell signallingCat #. 9,716IF: (dilution 1:50)
antibodyanti-rabbit IgG (H + L) Alexa Fluor 647 (goat polyclonal)ThermoFisher ScientificCat #. A-21244IF: (dilution 1:500)
antibodyanti-Rabbit IgG (H + L) Alexa Fluor 568 (goat polyclonal)ThermoFisher ScientificCat #. A-11011IF: (dilution 1:500)
antibodygoat anti-mouse IgG (H + L) Alexa Fluor 488 (goat polyclonal)ThermoFisher ScientificA-11001IF: (dilution 1:500)
antibodyanti-chicken IgY (H + L) Alexa Fluor 568 (goat polyclonal)Abcamab175711IF: (dilution 1:500)
Software, algorithmImageJ software(http://imagej.nih.gov/ij/)
Software, algorithmGraphPad Prism software(https://graphpad.com)
Software, algorithmFilamentTracer,
Imaris software, Bitplane AG
https://imaris.oxinst.com
Software, algorithmGSEA Desktop v3.0https://www.gsea-msigdb.org/gsea/index.jsp
Software, algorithmLeica Application Suite X (LAS X, v2.7) softwarehttps://www.leica-microsystems.com/products/microscope-software/p/leica-las-x-ls/
Software, algorithmCellProfiler v2.2https://cellprofiler.org
Software, algorithmImage Studio Software (v5.2)Li-cor Image Studio https://www.licor.com/bio/image-studio/

Mice

Mouse work was done under a UK Home Office project license and according to the Animals (Scientific Procedures) Act. Mice carrying the floxed Rad21 allele (Rad21lox, Seitan et al., 2011), in combination with the Cre recombinase in the Nex locus (Goebbels et al., 2006) and where indicated Rpl22(HA)lox/lox RiboTag (Sanz et al., 2009) were on a mixed C57BL/129 background. For timed pregnancies the day of the vaginal plug was counted as day 0.5. Genotypes were determined by PCR as previously reported (Seitan et al., 2011; Sanz et al., 2009). Rad21tev/tev mice have been described (Tachibana-Konwalski et al., 2010; Weiss et al., 2021).

Neuronal cultures

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For NexCre experiments, mouse cortices were dissected and dissociated from individual E17.5–E18.5 mouse embryos as described (Beaudoin et al., 2012) with minor modifications. Dissociated neurons were maintained in Neurobasal medium with B27 supplement (Invitrogen), 1 mM L-glutamine, and 100 U/mL penicillin/streptomycin for 10 days in vitro. Cells were plated at a density of 0.8 × 106 cells per well on six-well plates pre-coated overnight with 0.1 mg/ml poly-D-lysine (Millipore) and one third of the media in each well was replaced every 3 days. Cultures were treated with 5 μM Cytosine β-D-arabinofuranoside (Ara-C, Sigma) from day 2–4. For immunofluorescence staining neurons were plated on 12 mm coverslips (VWR) coated with poly-D-lysine at a density of 0.1 × 106 cells per coverslip. For cell-type-specific isolation of ribosome-associated mRNA, neurons from both cortices from each individual mouse embryo were seeded in a 10 cm dish.

For RAD21-TEV experiments, mouse cortices were dissected and dissociated on E14.5–15.5 as described (Weiss et al., 2021). Neurons were maintained in Neurobasal medium with B27 supplement (Invitrogen), 1 mM L-glutamine, and 100 U/ml penicillin/streptomycin. Cells were plated at a density of 1.25 × 105 /cm2 on 0.1 mg/ml poly-D-lysine (Millipore) coated plates, and half the media was replaced every 3 days. Cultures were treated with 5 μM Ara-C at day 5. For cleavage of RAD21-TEV, neurons were plated as described above and transduced at day 3 with lentivirus containing ERt2-TEV at a multiplicity of infection of one. For ERt2-TEV dependent RAD21-TEV degradation, neurons were treated on culture day 10 with 500 nM 4-hydroxytamoxifen (4-OHT) or vehicle (ethanol) for 24 hr.

For KCl depolarization experiments, neuronal cultures were pre-treated with 1 μM tetrodotoxin (TTX, Tocris) and 100 μM D-(-)–2-Amino-5-phosphonopentanoic acid (D-AP5, Tocris) overnight to reduce endogenous neuronal activity prior to stimulation. Neurons were membrane depolarized with 55 mM extracellular KCl by addition of prewarmed depolarization buffer (170 mM KCl, 2 mM CaCl2, 1 mM MgCl2, 10 mM HEPES pH 7.5) to a proportion of 0.43 volumes per 1 ml volume of neuronal culture medium in the well. For BDNF induction experiments, neuronal cultures were treated with BDNF (50 ng/ml) for the indicated period of time at 10 days in vitro.

For Sholl analysis, dissociated cortical neurons were cultured as described (Greig et al., 2013). Astroglial monolayers were adhered to culture dishes and cortical neurons to coverslips, which were then suspended above the glia. Primary cultures of glial cells were prepared from newborn rat cortices. Four days before neuronal culture preparation, glial cells were seeded in 12-well plates at a density of 1 × 104 cells per well and one day before, the medium from the glial feeder cultures was removed and changed to neuronal maintenance medium for preconditioning. Mouse cortices were dissected from E17.5/E18.5 mouse embryos and kept up to 24 hr in 2 mL of Hibernate-E Medium (ThermoFisher) containing B27 supplement (Invitrogen) and 1 mM L-glutamine in the dark at 4 °C. Embryos were genotyped and the cortices from the desired genotypes were used to prepare neuronal cultures as described before. Neurons were plated on 24-well plates containing poly-D-lysine precoated 12 mm coverslips (VWR) at a density of 0.1 × 106 cells per well. Wax dots were applied to the coverslips, which served as ‘feet’ to suspend the coverslips above the glial feeder layer. 4 hr after neuronal seeding, each coverslip containing the attached neurons was transferred upside down into a well of the 12-well dishes with the glial feeder. Cultures were treated with 5 μM Ara-C from day 2–4 and subsequently one third of the media was replaced every 3 days. For sparse neuronal GFP labeling, cortical neurons were transfected using 1 μg of peGFP-N1 plasmid along with 2 μl per well of Lipofectamine 2000 (Invitrogen) after 12 days in culture. Cultures were maintained for 14 days in vitro before fixation.

RNA extraction and RT-qPCR

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RNA was extracted with QIAshredder and RNeasy minikit (Qiagen). Residual DNA was eliminated using DNA-free kit (Ambion) and reverse-transcribed using the SuperScript first-strand synthesis system (Invitrogen). RT-PCR was performed on a CFX96 Real-Time System (Bio-Rad) with SYBR Green Master Mix (Bio-Rad) as per the manufacturer’s protocol and normalized to Ubc and Hprt mRNA levels. Relative level of the target sequence against the reference sequences was calculated using the ΔΔ cycle threshold method. RT–PCR primer sequences:

GeneForward (5’- 3’)Reverse (5’- 3’)
UbcAGGAGGCTGATGAAGGAGCTTGATGGTTTGAATGGATACTCTGCTGGA
HprtCCTGCTAATTTTACTGGCAACATCAACATTGAAATTCCAGACAAGTTTGTTGTTGG
Rad21AGCACCAGCAACCTGAATGAGATCGTCAAAGATGCCACCA
ArcTACCGTTAGCCCCTATGCCATCTGATATTGCTGAGCCTCAACTG
FosAATGGTGAAGACCGTGTCAGGATTGATCTGTCTCCGCTTGGAGTGT
Cdkn1aGCAGACCAGCCTGACAGATTGAGGGCTAAGGCCGAAGA
Mdm2TGTGTGAGCTGAGGGAGATGCACTTACGCCATCGTCAAGA
Cdkn2aAATCTCCGCGAGGAAAGCGTCTGCAGCGGACTCCAT
Cdkn2bAGACTGCAAGCACGAAGAGGTTGTCTTACTGGGTAGGGTTCAA

Protein analysis

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Whole cell extracts were prepared by resuspending cells in PBS with complete proteinase inhibitor (Roche, Cat#18970600), centrifugation, and resuspension in protein sample buffer (50 mM Tris-HCl pH 6.8, 1% SDS, 10% glycerol) followed by quantification using Qubit. Following quantification 0.001% Bromophenol blue and 5% beta-mercaptoethanol were added. Sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) was carried out with the Bio-Rad minigel system. 20 μg of protein sample and the benchmark pre-stained protein ladder (Biorad, #161–0374) were loaded on to a precast 10% polyacrylamide gel (Biorad, #456–1036). Resolved gels were blotted to a polyninylidene fluoride transfer membrane (Millipore, #IPVH00010) in transfer buffer (48 mM Trizma base, 39 mM glycine, 0.037% SDS, and 20% methanol) using the trans-blot semi-dry electrophoretic transfer apparatus (BioRad). Membranes were incubated for 1 hr with fluorescent blocker (Millipore, HC-08) followed by primary antibody incubation diluted in blocker at an appropriate dilution for 2 hr or at room temperature or overnight at 4 °C. Primary antibodies were rabbit polyclonal to RAD21 (1:1000; ab154769, Abcam), goat polyclonal to LAMIN B (1:10,000; sc-6216; Santa Cruz Biotechnology), mouse monoclonal anti-myc tag (1:500, SC-40, Santa Cruz Biotechnology). Secondary antibodies were goat anti-rabbit IgG (H + L) Alexa Fluor 680 (1:10,000; A-21109, ThermoFisher), goat anti-mouse IgG, Alexa Fluor 680 1:10,000, and donkey anti-goat IgG (H + L) Alexa Fluor 680 (1:10,000; A-21084, ThermoFisher). Immobilon-FL PVDF membranes (Millipore) were imaged on an Odyssey instrument (LICOR).

Cell-type-specific isolation of ribosome-associated mRNA

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For polysome immunoprecipitation experiments, homogenates from 10 day cortical explant cultures were prepared as described (Sanz et al., 2009) with minor modifications. Cells were first washed two times on ice with 10 ml of PBS containing 100 μg/mL cycloheximide (Sigma). Cells were lysed in 50 mM Tris pH 7.5, 100 mM KCl, 12 mM MgCl2 (ThermoFisher), 1% IGEPAL CA-630 (Sigma), 1 mM DTT (Sigma), 200 U/mL RNasin (ThermoFisher), 1 mg/mL heparin (Sigma), 100 μg/mL cycloheximide, 1 x Protease inhibitor (Sigma) and homogenization with a motor-driven grinder and pestle for about 2 min. Samples were then centrifuged at 10,000 g for 10 min to create a postmitochondrial supernatant. For immunoprecipitations, 100 μL of Dynabeads Protein G (Invitrogen) were coupled directly to 10 μL of rabbit anti-HA antibody (Sigma, H6908). After polysome immunoprecipitation, total RNA was prepared using a RNeasy Plus Mini kit (Qiagen).

RNAseq analysis

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Total RNA was obtained in parallel from 10 day explant cultures of dissociated cortical neurons without stimulation (baseline); after overnight treatment with TTX and D-AP5 (TTX); and after overnight treatment with TTX and D-AP5 and depolarization with KCl for 1 hr (KCl1h) or 6 hr (KCl6h). RNA was extracted with QIAshredder and RNeasy mini kit (Qiagen). RNA-seq libraries were prepared from 600 ng of total RNA (RNA integrity number (RIN) >8.0) with TruSeq Stranded Total RNA Human/Mouse/Rat kit (Illumina). For polysome immunoprecipitation experiments, 300 ng of total RNA was used for library preparation (RIN >9.0). RNA from Rad21-TEV neurons was purified with a PicoPure RNA Isolation kit (Applied Biosystems KIT0204), and 200 ng of total RNA was used to prepare libraries using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (polyA enrichment), following the manufacturer recommendations. Library quality and quantity were assessed on a Bioanalayser and Qubit respectively. Libraries were sequenced on an Illumina Hiseq2500 (v4 chemistry) and at least 40 million paired end 100 bp reads per sample were generated per library and mapped against the mouse (mm9) genome. The quality of RNA-seq reads was checked by Fastqc (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and aligned to mouse genome mm9 using Tophat version 2.0.11 (Kim et al., 2013) with parameters ‘library-type = fr-first-strand’. Gene coordinates from Ensembl version 67 were used as gene model for alignment. Quality metrics for the RNA-Seq alignment were computed using Picard tools verion 1.90 (https://broadinstitute.github.io/picard/) (Picard Toolkit, 2018). Genome wide coverage for each sample was generated using bedtools genomeCoverageBed and converted to bigwig files using bedGraphToBigWig application from UCSC Genome Browser. Bigwig files were visualised using IGV. After alignment, number of reads on the genes were summarized using HTSeq-count (version 0.5.4; Anders et al., 2015). All downstream analysis was carried out in R (version 3.4.0). Differentially expressed genes between condition were determined using DESEq2 (Love et al., 2014). p-values calculated by DESeq2 were subjected to multiple testing correction using Benjamini-Hochberg method. Adjusted p-value of 0.05 was used to select the differentially expressed genes. Principal Component Analysis (PCA) and hierarchical clustering of samples were done on the normalized read counts (rlog) computed using DESeq2. KCl-inducible genes were defined as genes in Rad21+/+ neurons with adjusted p<0.05 and log2 fold change ≥1 in KCl1h versus TTX or KCl6h versus TTX. As reference we used previously defined activity dependent genes (Kim et al., 2010). Constitutive genes were defined as expressed genes in wild-type neurons with adj. p≥0.05 in KCl1h versus TTX and KCl6h versus TTX.

Definition of ARGs and ARG classes

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Our initial analysis used inducible ARGs described (Kim et al., 2010). The number of ARGs with assigned p-values across RNA-seq conditions was n=298 in the RiboTag RNA-seq of Rad21 NexCre neurons and n=305 in the RNA-seq analysis of Rad21 NexCre neurons. For the definition of ARG classes we used previously curated gene sets (Tyssowski et al., 2018). We refer to rapidly induced, translation-independent ARGs as early-induced IEGs (called rIEGs in Tyssowski et al., 2018) and late-induced LRGs (LRGs are called translation-independent delayed PRGs and translation-dependent SRGs by Tyssowski et al., 2018; Beagan and Phillips-Cremins, 2020; Beagan et al., 2020). The number of IEGs is n=19 (Tyssowski et al., 2018, their Supplementary Table 5). Of these, n=18 had assigned p-values in all conditions of our RNA-seq analysis, as well as informative Hi-C data, and were included in our analysis. The number of fully annotated LRGs as defined by Tyssowski et al., 2018 (their Supplementary Table 5) is n=149 (comprised of 113 delayed translation-independent PRGs and n=36 translation-dependent SRGs). Of these, the number of LRGs with assigned p-values across RNA-seq conditions were n=107 in the RNA-seq analysis of Rad21 NexCre neurons, n=101 in the RNA-seq analysis of RAD21-TEV neurons, and n=99 in the RNA-seq analysis of transiently RAD21-depleted and subsequently reconstituted neurons. For inclusion in the comparison of chromatin loop length versus gene expression in cohesin-deficient neurons, ARGs had to meet the following criteria: (i) assigned p-values across RNA-seq conditions, (ii) downregulation or no deregulation across TTX and KCl conditions (ARGs that were downregulated in either TTX or KCl but not in both were excluded), and (iii) informative Hi-C data had to be available for the genomic region of each gene. These conditions were met by n=18 IEGs, n=22 downregulated LRGs, and n=43 non-deregulated LRGs.

GO term analysis

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GO terms enriched among differentially expressed genes were identified using goseq R package (Young et al., 2010) using all expressed genes in each comparison as background. GSEA was performed as described (Subramanian et al., 2005) using GSEA Desktop v3.0 (http://www.broadinstitute.org/gsea). ‘Wald statistics’ from DESeq2 differential expression analysis were used to rank the genes for GSEA. The gene set collections C2 (curated; KEGG 186 gene sets) and C5 (GO ontologies; 5,917 gene sets) were obtained from Molecular Signature Database (MSigDB version 6.1; Broad Institute, http://www.broadinstitute.org/gsea/msigdb). Neuron-specific genes were identified using Neutools (Gao et al., 2018).

Chromatin immunoprecipitation

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ChIP was performed as described with minor modifications. Briefly, cells were cross-linked for 10 min at room temperature with rotation using 1% formaldehyde in cross-linking buffer (0.1 M NaCl, 1 mM EDTA, 0.5 mM EGTA and 25 mM HEPES-KOH, pH 8.0). The reaction was quenched using 125 mM glycine for 5 min with rotation and the cells were washed three times using ice-cold PBS containing complete protease inhibitor cocktail tablets (Roche). Cells were resuspended in lysis buffer (1% SDS, 50 mM Tris-HCl pH 8.1, 10 mM EDTA pH 8) with EDTA-free protease inhibitor cocktail and incubated for 30 min on ice. Cell lysates were then sonicated 20 times at 4 °C (Bioruptor Plus, Diagenode, 30/30 cycles) and centrifuged at 14,000 rpm for 1 min at 4 °C to remove cellular debris. 10% of total volume was taken as input. Input samples were reverse crosslinked overnight at 65 °C, then incubated for 1 hr with 9 mM EDTA pH 8, 3.6 mM Tris-HCl pH 6.8 and 36 μg/mL proteinase K at 45 °C. Input chromatin purification was performed using phenol-chloroform at 4 °C. Total chromatin was pre-cleared for 1 hr at 4 °C with rotation using protein A sepharose beads (P9424, Merck). 3 µg of anti-histone H3 (Abcam, ab1791) and 5 µg of anti-H3K27Ac (Active Motif, 39133) were added overnight at 4 °C with rotation. 100 μL of protein A sepharose beads were added to each IP for at least 4 hr before being washed with low salt buffer (150 mM NaCl, 2 mM Tris pH 8.1, 0.1% SDS, 1% Triton X-100 and 2 mM EDTA pH 8), high salt buffer (500 mM NaCl, 20 mM Tris pH 8.1, 0.1% SDS, 1% Triton X-100 and 2 mM EDTA pH 8), LiCl salt buffer (0.25 mM LiCl, 10 mM Tris pH 8.1, 1% NP-40, 1% sodium deoxycholate and 1 mM EDTA pH 8) and Tris-EDTA buffer (10 mM Tris pH 8.1 and 1 mM EDTA pH 8). Chelex-100 (Bio-Rad, catalog number #1421253) was added to the samples, which were then boiled and incubated for 1 hr at 55 °C with 36 μg/mL proteinase K and boiled once again. Samples were centrifuged at 12,000 rpm for 1 min and the beads washed once with nuclease-free water. The following PCR primers were used: Control region chr11: 71.177.622–71.177.792: forward, 5’-CATTCCAGGGCAACTCCACT-3′, reverse, 5’-CAGGGGCTCCTGTACTACCT-3′; Fos enhancer 1 forward, 5′-TCCGGTAAGGGCATTGTAAG-3′, reverse, 5′-CAAAGCCAGACCCTCATGTT-3′; Fos enhancer forward, 5′-TGCAGCTCTGCTCCTACTGA-3′, reverse, 5′-GAGGAGCAAGACTCCCACAG-3′.

3C template generation

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Neuronal cultures were fixed in 1% formaldehyde for 10 min (room temp) via the addition (1:10 vol/vol) of the following fixation solution: 50 mM Hepes-KOH (pH 7.5), 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 11% Formaldehyde. Fixation was quenched via the addition of 2.5 M glycine (1:20 vol/vol) and scraped into pellets. Each pellet was washed once with cold PBS, flash frozen, and stored at –80 °C. For each condition, in situ 3 C was performed on two replicates of 4–5 million cells as described (Beagan et al., 2020). Briefly, cells were thawed on ice and resuspended (gently) in 250 μL of lysis buffer (10 mM Tris-HCl pH 8.0, 10 mM NaCl, 0.2% Igepal CA630) with 50 μL protease inhibitors (Sigma P8340). Cell suspension was incubated on ice for 15 min and pelleted. Pelleted nuclei were washed once in lysis buffer (resuspension and spin), then resuspended and incubated in 50 μL of 0.5% SDS at 62 °C for 10 min. SDS was inactivated via the addition of 145 μL H2O, 25 uL 10% Triton X-100, and incubation at 37 °C for 15 min. Subsequently, chromatin was digested overnight at 37 °C with the addition of 25 μL 10 X NEBuffer2 and 100 U (5 μL) of HindIII (NEB, R0104S), followed by 20 min incubation at 62 °C to inactivate the HindIII. Chromatin was re-ligated via the addition of 100 μL 10% Triton X-100, 120 μL NEB T4 DNA Ligation buffer (NEB B0202S), 12 μL 10 mg/mL BSA, 718 μL H2O, and 2000 U (5 μL) of T4 DNA Ligase (NEB M0202S) and incubation at 16 °C for 2 hr. Following ligation nuclei were pelleted, resuspended in 300 μL of 10 mM Tris-HCl (pH 8.0), 0.5 M NaCl, 1% SDS, plus 25 μL of 20 mg/mL proteinase K (NEB P8107), and incubated at 65 °C for 4 hr at which point an additional 25 μL of proteinase K was added and incubated overnight. 3 C templates were isolated next day via RNaseA treatment, phenol-chloroform extraction, ethanol precipitation, and Amicon filtration (Millipore MFC5030BKS). Template size distribution and quantity were assessed with a 0.8% agarose gel.

5C library preparation

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5 C primers which allow for the query of genome folding at ultra-high resolution but on a reduced subset of the genome were designed according to the double-alternating design scheme (Kim et al., 2018b; Beagan et al., 2020) using My5C primer design (Lajoie et al., 2009; http://my5c.umassmed.edu/my5Cprimers/5C.php) with universal ‘Emulsion’ primer tails. Regions were designed to capture TAD structures immediately surrounding the genes of interest in published mouse cortex Hi-C data (Bonev et al., 2017). The following regions were analyzed in this paper: chr2 107601077–110913077, chr10 107002896–109474896, chr12 86201802–87697802, chr15 73376037–74580037, chr17 89772409–92148409, chrX 97543400–98835400. 5 C reactions were carried out as previously described (Beagan et al., 2020). 600 ng (~200,000 genome copies) of 3 C template for each replicate was mixed with 1 fmole of each 5 C primer and 0.9 ug of salmon sperm DNA in 1 x NEB4 buffer, denatured at 95 °C for 5 min, then incubated at 55 °C for 16 hr. Primers which had then annealed in adjacent positions were ligated through the addition of 10 U (20 μL) Taq ligase (NEB M0208L) and incubation at 55 °C for 1 hr then 75 °C for 10 min. Successfully ligated primer-primer pairs were amplified using primers designed to the universal tails (FOR = CCTCTC TATGGGCAGTCGGTGAT, REV = CTGCCCCGGGTTCCTCATTCTCT) across 30 PCR cycles using Phusion High-Fidelity Polymerase. Presence of a single PCR product at 100 bp was confirmed via agarose gel, then residual DNA <100 bp was removed through AmpureXP bead cleanup at a ratio of 2:1 beads: DNA (vol/vol). 100 ng of the resulting 5 C product was prepared for sequencing on the Illumina NextSeq 500 using the NEBNext Ultra DNA Library Prep Kit (NEB E7370) following the manufacturer’s instructions with the following parameter selections: during size selection, 70 μL of AMPure beads was added at the first step and 25 at the second step; linkered fragments were amplified using eight PCR cycles. A single band at 220 bp in each final library was confirmed using an Agilent DNA 1000 Bioanalyzer chip, and library concentration was determined using the KAPA Illumina Library Quantification Kit (#KK4835). Finally, libraries were evenly pooled and sequenced on the Illumina NextSeq 500 using 37 bp paired-end reads to read depths of between 11 and 30 million reads per replicate.

5C interaction analysis

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5 C analysis steps were performed as described (Gilgenast and Phillips-Cremins, 2019; Beagan et al., 2020; Fernandez et al., 2020). Briefly, paired-end reads were aligned to the 5 C primer pseudo-genome using Bowtie, allowing only reads with one unique alignment to pass filtering. Only reads for which one paired end mapped to a forward/left-forward primer and the other end mapped to a reverse/left-reverse primer were tallied as true counts. Primer-primer pairs with outlier count totals, resulting primarily from PCR bias, were identified as those with a count at least 8-fold higher (100-fold for the lower-quality Arc region) than the median count of the 5 × 5 subset of the counts matrix centered at the primer-primer pair in question; outlier counts were removed.

Primer-primer pair counts were then converted to fragment-fragment interaction counts by averaging the primer-primer counts that mapped to each fragment-fragment pair (max of 2 if both a forward/left-forward and a reverse/left-reverse primer were able to be designed to both fragments and were not trimmed during outlier removal). We then divided our 5 C regions into adjacent 4 kb bins and computed the relative interaction frequency of two bins (i, j) by summing the counts of all fragment-fragment interactions for which the coordinates of one of the constituent fragments overlapped (at least partially) a 12 kb window surrounding the center of the 4 kb ith bin and the other constituent fragment overlapped the 12 kb window surrounding the center of the jth bin. Binned count matrices were then matrix balanced using the ICE algorithm69,71 and quantile normalized across all eight replicates (two per condition) within each experimental set (neuronal activation and cohesin-rescue experimental datasets were quantile normalized separately) as previously described (Beagan et al., 2020), at which point we considered each entry (i, j) to represent the Observed Interaction Frequency of the 4 kb bins i and j. Finally, the background contact domain ‘expected’ signal was calculated using the donut background mode (Su et al., 2017) and used to normalize the relative interaction frequency data for the background interaction frequency present at each bin-bin pair. The resulting background-normalized interaction frequency (‘observed over expected’) counts were fit with a logistic distribution from which p-values were computed for each bin-bin pair and converted into ‘Background-corrected Interaction Scores’ (interaction score = –10*log2(p-value)), which have previously shown to be informatively comparable across replicates and conditions (Beagan et al., 2020).

Identification of neuronal enhancers

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H3K27Ac ChIP-Seq and corresponding input datasets (Malik et al., 2014) were from NCBI GEO using accession GSE60192. The sra files were converted to fastq using sratoolkit and aligned to mouse genome mm9 using bowtie version 0.12.8 with default parameters (Langmead et al., 2009). Sequencing reads aligned to multiple positions in the genome were discarded. Duplicate reads were identified using Picard tools v 1.90 and removed from the downstream analysis. H3K27Ac peaks were identified using macs2 with ‘broad’ parameters (Zhang et al., 2008). Gene coordinates were obtained from Ensembl using ‘biomaRt’ R package (Durinck et al., 2009) and enhancers were assigned to nearest genes using ‘nearest’ function from GenomicRanges R package (Lawrence et al., 2013). Enhancers at Hi-C loop anchors were defined as reported previously (Beagan et al., 2020) and are provided there in Table S11.

Hi-C loop span analysis

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Loops were called on mouse cortical neuron Hi-C data (Bonev et al., 2017) as described (Beagan et al., 2020, loop calls are provided as Table S16 in Beagan et al., 2020). Cortical neuron CTCF ChIP-seq (Bonev et al., 2017) reads were downloaded from GEO accessions GSM2533876 and GSM2533877, merged, and aligned to the mm9 genome using Bowtie v0.12.7. Reads with more than two possible alignments were removed (-m2 flag utilized). Peaks were identified using MACS2 version 2.1.1.20160309 with a p value cutoff parameter of 10−4. Loops were classified as ‘CTCF loops’ if a CTCF peak fell within at least one anchor of the loop. Loops were classified as enhancer-promoter loops if the TSS of a gene of interest fell within one loop anchor and the other anchor of the same loop contained an activity-induced enhancer (for ARGs) or a constitutive or activity-induced enhancer (for neuronal genes related to synaptic transmission and glutamate receptor signaling; Table S11 in Beagan et al., 2020). The span of loops with an anchor that contained the TSS of a gene of interest was quantified by calculating the genomic distance between the midpoint of the loop’s two anchors.

Immunocytochemistry

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Neurons plated on coverslips were fixed with warmed to 37 °C PBS containing 4% paraformaldehyde and 4% sucrose for 10 min at room temperature. Neurons were then permeabilized with 0.3% Triton X-100 for 10 min and treated with blocking solution (10% normal goat serum, 0.1% Triton X-100 in PBS) for 1 hr. Primary and secondary antibodies were diluted in 0.1% Triton X-100, 2% normal goat serum in PBS. Appropriate primary antibodies were incubated with samples for 2 hr at room temperature or overnight at 4 °C. Primary antibodies used were specific to RAD21 (1:500; rabbit polyclonal ab154769, Abcam), MAP2 (1:5000; chicken polyclonal ab611203, Abcam), GAD67 (1:500; mouse monoclonal MAB5406, Millipore), HA (1:1000; mouse monoclonal MMS-101R, Covance), GFAP (1:500; rabbit polyclonal Z0334, Dako), or IBA1 (1:250; rabbit polyclonal 019–19741, Wako). Secondary antibodies were incubated for 1 hr at room temperature. Goat anti-rabbit IgG (H + L) Alexa Fluor 647 (A-21244, ThermoFisher), Goat anti-Rabbit IgG (H + L) Alexa Fluor 568 (A-11011, ThermoFisher), goat anti-mouse IgG (H + L) Alexa Fluor 488 (A-11001, ThermoFisher), goat anti-chicken IgY (H + L) Alexa Fluor 568 (ab175711, Abcam) conjugates were used at a 1:500 dilution. Cells were mounted in Vectashield medium containing DAPI (Vector Labs).

Embryonic brains were fixed for 4 hr in 4% paraformaldehyde in PBS, washed in PBS, transferred to 15% sucrose in PBS for cryopreservation, embedded in OCT and stored at −80 °C until use. Coronal sections of 10 μm were cut with a Leica cryostat and mounted on glass slides. The sections were washed two times for 10 min in PBS, blocked with 0.3% TritonX-100, 5% normal goat serum in PBS, at room temperature and incubated overnight with the primary antibody solution. Sections were then washed three times for 10 min each in PBS and were incubated for 1 hr in the dark with the secondary antibody solution. The primary antibodies were specific to RAD21 (1:500; rabbit polyclonal ab154769, Abcam), anti-gamma-H2AX (1:3000; rabbit polyclonal A300-081A, Bethyl Laboratories), Cleaved Caspase-3 (Asp175) (1:400; rabbit polyclonal 9661, Cell signalling), TBR1 (1:1000; rabbit polyclonal ab31940, Abcam), CTIP2 (1:500; rat monoclonal [25B6] ab18465, Abcam), CUX-1 (1:400; rabbit polyclonal sc-13024, Santa Cruz), and anti phospho-histone H3 (Ser10) Alexa Fluor 647 conjugate (1:50; rabbit polyclonal 9716, Cell signalling). The secondary antibodies used are described in the previous section. Sections were mounted with Vectashield medium containing DAPI.

Confocal image analysis and quantification

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For quantification analysis of RAD21 negative neurons and inhibitory neurons (GAD67+) images (1024 × 1024 pixels) were acquired using TCS SP5 confocal microscope (Leica Microsystems), using a HCX PL APO CS 40 x/1.25 lens at zoom factor 1 (373 nm/pixel). Images were acquired with identical settings for laser power, detector gain, and amplifier offset, with pinhole diameters set for one airy unit. DAPI-identified nuclei that colocalized with the GAD67 signal, or without RAD21 signal were counted as inhibitory or RAD21 negative neurons, respectively; and were quantified using a processing pipeline developed in CellProfiler (version 2.2, Broad Institute, Harvard, Cambridge, MA, USA, https://cellprofiler.org/). For astrocytes (GFAP+) and microglia (IBA1+) quantification in dissociated cortical neuronal cultures, the entire coverslips were imaged using a IX70 Olympus microscope equipped with a 4 × 0.1 NA Plan-Neofluar lens (1.60 μm/pixel) and the number of astrocytes and microglia were counted. The analysis and quantification of different cell types were done for three different experiments, each experiment containing at least two different samples of each genotype.

For Sholl analysis of dissociated cortical neurons, samples were imaged using a TCS SP8 confocal microscope, a HC PL APO CS2 40 x/1.30 lens at zoom factor 0.75 with a resolution of 2048 × 2048 pixel (189 nm/pixel, 0.5 μm/stack). Images were acquired with identical settings for laser power, detector gain, and amplifier offset, with pinhole diameters set for one airy unit. Approximately 10 neurons were imaged per sample and at least two different samples per genotype in each experiment; each experiment was performed three times. GFP +neurons were traced using the FilamentTracer package in Imaris software (Bitplane AG). Statistical significance was assessed using a repeat measures ANOVA with a Bonferroni Post Test (Prism-GraphPad Software).

Data availability

RNAseq and 5C data generated in this study have been deposited at Gene Expression Omnibus under accession number GSE172429.

The following data sets were generated
    1. Merkenschlager M
    (2022) NCBI Gene Expression Omnibus
    ID GSE172429. Cohesin-dependence of neuronal gene expression relates to chromatin loop length.
The following previously published data sets were used
    1. Malik AN
    (2014) NCBI Gene Expression Omnibus
    ID GSE60192. Genome-wide identification and characterization of functional neuronal activity-dependent enhancers.
    1. Bonev B
    (2017) NCBI Gene Expression Omnibus
    ID GSM2533876. In-vitro differentiated cortical neurons.
    1. Bonev B
    (2017) NCBI Gene Expression Omnibus
    ID GSM2533877. In-vitro differentiated cortical neurons.

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    Cornelia de Lange Syndrome
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    1. Paxinos G
    2. Halliday G
    3. Watson C
    4. Kassem M.
    (2020)
    Atlas of the Developing Mouse Brain (2nd Edition)
    Academic Press.
    1. Sholl DA
    (1953)
    Dendritic organization in the neurons of the visual and motor cortices of the cat
    Journal of Anatomy 87:387–406.

Decision letter

  1. Jeremy J Day
    Reviewing Editor; University of Alabama at Birmingham, United States
  2. Kevin Struhl
    Senior Editor; Harvard Medical School, United States

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

Decision letter after peer review:

Thank you for submitting your article "Reliance of neuronal gene expression on cohesin scales with chromatin loop length" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Kevin Struhl as the Senior Editor. The reviewers have opted to remain anonymous.

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

Essential revisions:

1) All reviewers agreed that there is somewhat of a disconnect between effects of Rad21 depletion on activity-dependent gene expression and activity-dependent loop formation. While the manuscript presents some solid evidence that Rad21 contributes to SRG expression, it was not clear that this was due to prevention of new loops formed after stimulation, as the authors only report effects at constitutive loops. The manuscript would benefit from a more systematic comparison of the effects of RAD21 on inducible vs. constitutive loops, similar to the analysis that was performed with inducible vs. constitutive genes.

2) The conceptual framing of the manuscript could be improved by focusing more on developmental aspects of Rad21 depletion in neurons, and less on activity-dependent aspects. Given that many of the effects reported here at SRGs alter both basal and inducible gene expression, and the strong constitutive role for Rad21 in maintenance of chromatin architecture in neurons, this reframing would be a better fit for the results presented here.

3) The manuscript should include more thorough discussion to place the present work in context with prior work, as outlined in the individual reviews provided below.

4) A revised manuscript should address the individual concerns raised by reviewers to specify which gene sets are used for different analyses, clarify the terms IEG and SRG as used in the manuscript, and provide further analysis on the contribution of loop length to Rad21 effects.

Reviewer #1 (Recommendations for the authors):

1. Figure 5 convincingly shows that genes that are downregulated in RAD21 depleted neurons have longer HiC loop length, for both activity regulated genes and constitutive neuronal genes. However, in order to conclude that RAD21 effects on chromatin looping "scales with the genomic distance traversed by their chromatin contacts", it would seem to be necessary to conduct a more detailed analysis showing that the effects of RAD21 depletion directly correlate with the genomic distance of HiC contacts. By binning HiC lengths and showing average effects of RAD21 depletion on mRNA by bin classes (e.g,. 0-50, 50-100, 100-500, 500+kb loop lengths), it may be possible to address this question. As it stands, we only know that RAD21 downregulated genes tend to have lengthier loops, not that RAD21 effects scale with chromatin loop lengths.

2. The incorporation of 5C to show the lack of effect for RAD21 depletion on Fos enhancer-promoter looping (Figure 6) is encouraging and confirms the overall model of cohesin-dependent gene expression. However, the observations at Bdnf (a defined SRG, Supp. Figure 11) resemble those at Fos – only constitutive loops are lost following RAD21 depletion, with variable effects on gene basal levels and inducibility. Were the authors able to specifically examine activity-dependent loops that are formed involving SRGs as a result of stimulation? It does not appear that 5C at a more delayed timepoint (e.g. after 6hr of KCl stimulation) was conducted. This would have provided an opportunity to examine the stimulus-dependent loops formed at the Bdnf locus, previously identified by Beagan, et al., (PMID: 32451484). Either way, the manuscript could be improved by addition of the Bdnf figure (Supp. Figure 11) as a main figure in the text.

3. Prior work demonstrates that AP1 transcription factors serve as a key regulator of stimulus-activated enhancers in cortical neurons (PMID: 25195102). Similarly, evidence from ATAC-seq experiments suggests that IEG enhancers are already fully accessible prior to stimulation (PMID: 32810208). In contrast, AP1 members that comprise part of the IEG program are thought to serve as pioneer transcription factors (in combination with cell-selective TFs) to open chromatin at SRG enhancers. This manuscript would benefit from a more systematic comparison of the effects of RAD21 on inducible vs. constitutive loops, similar to the analysis that was performed with inducible vs. constitutive genes.

4. Many constitutive neuronal genes are also long genes, and these may be more likely to be disrupted after RAD21 depletion and resulting changes in genome structure and organization. This manuscript should include a supplementary analysis to determine whether RAD21 effects scale with gene length itself, in addition to chromatin loop length.

Reviewer #2 (Recommendations for the authors):

I feel that a revised version of the paper should address my comments listed here:

There should be a discussion on whether cohesin dependence is scaling with loop length in other cell types as well. For example, the Cuartero et al., 2018 study by some of the same authors, using approaches similar to the ones presented here in immune cells, and may be good dataset to compare with their findings in neurons.

Based on Figure 4a, only a tiny fraction of transcribed genes are immediate early genes IEGs (N=18) and only N=107 genes are secondary response genes. To me, it is not entirely clear how the Authors arrived at these numbers. The number of activity regulated genes should be in the several hundreds, based on the literature (including the Kim et al., 2010 paper that the Authors cite). In any case, one wonders what type of conclusion can be drawn from such a small set of N-18 IEG genes.

The Authors , if I understood correctly, probed multiple genomic loci with 5C, but specific information on the sequences and loci probed is hard to find in the paper. From reading the paper, it is clear that c-Fos and BDNF and Arc gene loci (Figures S9-11) were probed with 5C. Please be more specific on the area of genome interrogated with 5C.

The sensitivity of chromosome conformation capture techniques to detect loops and contacts declines with increasing linear genome distance between the anchors. There should be a discussion and if necessary, a control experiment (I believe in silico may suffice) whether or not the reported cohesion- sensitivity of longer range chromatin loopings could be reflective of the overall lower baseline signal of longer range chromosomal loopings. If so, this would be a significant confound for the Authors conclusion of cohesion dependence scaling with loop length.

Reviewer #3 (Recommendations for the authors):

My questions about the study fall in two parts.

First, I am left wondering about the significance of the observations and the way they are presented. The relationship between Rad21 dependence and longer loop length is supported by the data, but I struggled to understand what that might mean. Is there anything else the authors can add about why this might be the case? Is this special in neurons and other postmitotic cells and if so what might that mean? More importantly, the authors include a paragraph in the discussion about how promoter-enhancer interactions might work independent of cohesin, which is interesting, and perhaps it is a major finding of the study that those enhancers do not require Rad21. If this is the most important finding then the fact that this study reaches a different conclusion that Yamada 2019 needs to be directly addressed. That study also knocked out Rad21 in postmitotic neurons (CGNs) but did report disruption of promoter-enhancer loops and activity-induced gene expression. These data may well be stronger than those, but the authors should directly address the differences in the findings.

Second, given that the authors find the expression of many ion channels and synaptic proteins impacted by the loss of Rad21, they need to consider that any changes they see in impaired induction of activity-regulated genes – such as the reduced activation of Bdnf IV in Suppl Figure 11 – may arise from impaired intracellular signaling rather than a specific effect of Rad21 on the architecture of the Bdnf gene. Controls for the activation of signaling cascades and transcription factors could be added to address this concern. The demonstration that much of the effect of Rad21 knockout on activity-regulated genes is their reduction at baseline is certainly consistent with this idea of reduced synaptic drive to these genes, which is ongoing in cultures. KCl addition may largely overcome those deficits by providing such a strong drive through LVGCCs.

A few other questions:

1) When the authors refer to "activity-regulated genes" for example in Figure 2C, do they include only genes induced by neuronal activity or also those repressed by neuronal activity? This distinction is important because the meaning of those genes begin "Up" or "Down" in the Rad21 cKO is very different depending on the sign of their response to neuronal stimulation. This should be explicitly stated.

2) The data in Figure 3a on the distribution of different cell types in the cortex of Rad21 cKO mice is entirely anecdotal as presented. If the data are to be included they should be quantified and analyzed with appropriate statistics.

3) The terms IEG and SRG for the ARG classes are used here in a way that is not consistent with the literature. The division between the terms as the authors use them here seems to be time (IEGs early and SRGs late) whereas in the past the terms had more to do with mechanism – IEGs did not require stimulus-induced protein synthesis before they could be turned on whereas SRGs did. (Basically cycloheximide does not block IEG induction but it does block SRG induction). The Tyssowski paper referenced by the authors uses the language PRG for primary response genes that do not require protein synthesis for their induction, and then divide them into early and delayed PRGs for the reflection of time. BDNF is a delayed PRG and following the stimuli used here it does not require protein synthesis for its induction. The authors may want to consider clarifying their use of their terms depending on whether time or mechanism of transcription is their main focus to match other literature.

4) There is a paper from Kim Nasmyth's lab that knocked out Rad21 in postmitotic neurons of the fly with relatively severe phenotypes. By contrast at least the morphological phenotypes in the images in Suppl Figure 4 seem quite mild. This might be an important point of discussion if it is the case that the consequences of Rad21 function are different by species?

5) It is a little surprising that all the data from the final paragraphs on activity-regulated genes are in the supplementary figures. This points to evidence that the authors do not consider these to be the most important of their findings, which is slightly out of balance with the attention paid to activity-dependent genes in the text. Perhaps a rewrite of the paper would make the significance of the findings more obvious.

https://doi.org/10.7554/eLife.76539.sa1

Author response

Essential revisions:

1) All reviewers agreed that there is somewhat of a disconnect between effects of Rad21 depletion on activity-dependent gene expression and activity-dependent loop formation. While the manuscript presents some solid evidence that Rad21 contributes to SRG expression, it was not clear that this was due to prevention of new loops formed after stimulation, as the authors only report effects at constitutive loops.

To address this concern, the revised manuscript includes a new analysis of inducible Bdnf loop formation. While formation of the full strength inducible Bdnf loop requires 6h stimulation (Beagan et al., 2020, not performed in the present manuscript), an inducible Bdnf loop can nevertheless be seen to form 1h after KCl stimulation in wild-type neurons. The revised manuscript presents an analysis of this inducible Bdnf loop (revised Figure 8,). Quantification shows that the inducible Bdnf loop forms in wild-type but not in cohesin-deficient neurons, indicating that the formation of this loop requires cohesin. With ~1.68Mb, the inducible Bdnf loop spans a substantially larger genomic distance than the enhancer-promoter loops at the IEGs Fos and Arc (Beagan et al., 2020).

We have also followed the advice to re-balance data presentation, and have moved data on inducible gene expression from Supplementary to the main figures. In particular, data on the rescue of inducible gene expression, and the 5C analysis of the Bdnf locus are now presented in the main figures of the revised manuscript.

The manuscript would benefit from a more systematic comparison of the effects of RAD21 on inducible vs. constitutive loops, similar to the analysis that was performed with inducible vs. constitutive genes.

The most compelling inducible loops known in primary neurons are at Fos and Arc, and our data show that these loops form independently of cohesin. As described above, the revised manuscript now includes an analysis of inducible Bdnf loops (Revised Figure 8).

As suggested by Referee 2, "the sensitivity of chromosome conformation capture techniques to detect loops and contacts declines with increasing linear genome distance". We have therefore performed a comparison of loop strength at different genomic distances (New Supplementary Figure 10). New Figure 8 —figure supplement 1 shows the observed loop strength after correction for the distance-dependent background signal (as described by Beagan et al., 2020). This approach allows an assessment of loop strength across distances. The y-axes are now on the same scale, allowing an appreciation of cohesin effects on loop strength, despite the differences in distance. This analysis indicates that the claimed differences in cohesin-dependence exist, independent of differences in loop length (Referee 2 Figure 2b and new Figure 8 —figure supplement 1).

2) The conceptual framing of the manuscript could be improved by focusing more on developmental aspects of Rad21 depletion in neurons, and less on activity-dependent aspects. Given that many of the effects reported here at SRGs alter both basal and inducible gene expression, and the strong constitutive role for Rad21 in maintenance of chromatin architecture in neurons, this reframing would be a better fit for the results presented here.

We have addressed this concern in two ways.

First, to strengthen the developmental aspects of the manuscript, we have added a quantitative analysis or cortical layer organisation: "While the total numbers of TBR1+ and CTIP2+ neurons were comparable in wild-type and Rad21lox/lox cortices, TBR1+ and CTIP2+ neurons were reduced in the subplate and increased in layers 6 and 7 Rad21lox/lox NexCre cortices" (Revised Figure 3a).

Second, as discussed in detail above, the revised manuscript includes a new analysis of inducible Bdnf loop formation.

Third, we have followed the advice to re-balance data presentation, and have moved data on inducible gene expression from Supplementary to the main figures. In particular, data on the rescue of inducible gene expression, and the 5C analysis of the Bdnf locus are now presented in the main figures of the revised manuscript.

3) The manuscript should include more thorough discussion to place the present work in context with prior work, as outlined in the individual reviews provided below.

We have addressed this point by discussing our work in the context of experiments that depleted neuronal cohesin in flies and mice, as prompted by referee 3. We also emphasise that our data show an essential role of neuronal cohesin in mice, as indicated by lethality during the first 2 weeks of postnatal life. We have also followed the advice by referee 1 to discuss the role of AP1 transcription factors encoded by IEGs in the inducible expression of LRGs.

4) A revised manuscript should address the individual concerns raised by reviewers to specify which gene sets are used for different analyses, clarify the terms IEG and SRG as used in the manuscript, and provide further analysis on the contribution of loop length to Rad21 effects.

We agree that a clear definition of gene sets will benefit the manuscript. We used inducible ARGs described by Kim et al., 2010 for our initial analysis. The number of ARGs with assigned P-values across RNA-seq conditions was n = 298 in the RiboTag RNA-seq of Rad21 NexCre neurons and n = 305 in the RNA-seq analysis of Rad21 NexCre neurons.

For the definition of ARG classes we used gene sets curated by Tyssowski et al., 2018. We refer to rapidly induced, translation-independent ARGs as IEGs (these genes are called rIEGs in Tyssowski et al., 2018, and IEGs in Beagan et al., 2020). To describe late-induced ARGs we had initially adopted the term 'SRG' from Beagan et al., 2020. However, in light of the referees' comments we refer to late-induced ARGs as 'LRGs' in the revised manuscript. LRGs are called translation-independent delayed PRGs and translation-dependent SRGs by Tyssowski et al., 2018.

The number of IEGs as defined by Tyssowski et al., 2018, Supplementary Table 5 is n = 19. Of these, n = 18 had assigned P-values in all conditions of our RNA-seq analysis and informative Hi-C data, and were included in our analysis.

The number of fully annotated LRGs as defined by Tyssowski et al., 2018, Supplementary Table 5 is n = 149 (comprised of 113 delayed translation-independent PRGs and n = 36 translation-dependent SRGs). Of these, the number of LRGs with assigned P-values across RNA-seq conditions were n = 107 in the RNA-seq analysis of Rad21 NexCre neurons, n = 101 in the RNA-seq analysis of RAD21-TEV neurons, and n = 99 in the RNA-seq analysis of transiently RAD21-depleted and subsequently reconstituted neurons.

For inclusion in the comparison of chromatin loop length versus gene expression in cohesin-deficient neurons, ARGs had to meet the following criteria: (i) assigned P-values across RNA-seq conditions, (ii) downregulation or no deregulation across TTX and KCl conditions (ARGs that were downregulated in either TTX or KCl but not in both were excluded), and (iii) informative Hi-C data had to be available for the genomic region of each gene. These conditions were met by n = 18 IEGs, n = 22 downregulated LRGs, and n = 43 non-deregulated LRGs.

We have added this information to the methods section of the revised manuscript.

Reviewer #1 (Recommendations for the authors):

1. Figure 5 convincingly shows that genes that are downregulated in RAD21 depleted neurons have longer HiC loop length, for both activity regulated genes and constitutive neuronal genes. However, in order to conclude that RAD21 effects on chromatin looping "scales with the genomic distance traversed by their chromatin contacts", it would seem to be necessary to conduct a more detailed analysis showing that the effects of RAD21 depletion directly correlate with the genomic distance of HiC contacts. By binning HiC lengths and showing average effects of RAD21 depletion on mRNA by bin classes (e.g,. 0-50, 50-100, 100-500, 500+kb loop lengths), it may be possible to address this question. As it stands, we only know that RAD21 downregulated genes tend to have lengthier loops, not that RAD21 effects scale with chromatin loop lengths.

We agree with the referee. We have changed the title to "Cohesin-dependence of neuronal gene expression relates to chromatin loop length" and have revised other relevant passages of the manuscript accordingly.

2. The incorporation of 5C to show the lack of effect for RAD21 depletion on Fos enhancer-promoter looping (Figure 6) is encouraging and confirms the overall model of cohesin-dependent gene expression. However, the observations at Bdnf (a defined SRG, Supp. Figure 11) resemble those at Fos – only constitutive loops are lost following RAD21 depletion, with variable effects on gene basal levels and inducibility. Were the authors able to specifically examine activity-dependent loops that are formed involving SRGs as a result of stimulation? It does not appear that 5C at a more delayed timepoint (e.g. after 6hr of KCl stimulation) was conducted. This would have provided an opportunity to examine the stimulus-dependent loops formed at the Bdnf locus, previously identified by Beagan, et al., (PMID: 32451484). Either way, the manuscript could be improved by addition of the Bdnf figure (Supp. Figure 11) as a main figure in the text.

The revised manuscript includes a new analysis of inducible Bdnf loop formation. While formation of the full strength inducible Bdnf loop requires 6h stimulation (Beagan et al., 2020, not performed in the present manuscript), an inducible Bdnf loop can nevertheless be seen to form 1h after KCl stimulation in wild-type neurons. The revised manuscript presents an analysis of this inducible Bdnf loop. Quantification shows that the inducible Bdnf loop forms in wild-type but not in cohesin-deficient neurons, indicating that the formation of this loop requires cohesin. With ~1.68Mb, the inducible Bdnf loop spans a substantially larger genomic distance than the enhancer-promoter loops at the IEGs Fos and Arc (Beagan et al., 2020).

The finding that inducible looping at the Bdnf locus is cohesin-dependent adds substantially to the strength of our manuscript by supporting the correlative evidence that cohesin-dependent genes are characterised by longer loops. Based on this – and also the referee's advice – we have moved the revised Bdnf figure (formerly Supp. Figure 11) to a main of the revised manuscript (now Figure 8).

3. Prior work demonstrates that AP1 transcription factors serve as a key regulator of stimulus-activated enhancers in cortical neurons (PMID: 25195102). Similarly, evidence from ATAC-seq experiments suggests that IEG enhancers are already fully accessible prior to stimulation (PMID: 32810208). In contrast, AP1 members that comprise part of the IEG program are thought to serve as pioneer transcription factors (in combination with cell-selective TFs) to open chromatin at SRG enhancers. This manuscript would benefit from a more systematic comparison of the effects of RAD21 on inducible vs. constitutive loops, similar to the analysis that was performed with inducible vs. constitutive genes.

We thank the referee for these comments. The revised manuscript discusses the role of IEG-encoded TFs that facilitate LRG induction, citing the role of AP1 (PMID: 25195102; Malik et al., 2014) and the accessibility of IEG enhancers prior to stimulation (Carullo et al., 2020).

"Typical IEG enhancers such as Fos and Arc enhancers are fully accessible prior to stimulation (Carullo et al., 2020), and show increase H3K27ac and eRNA transcription in response to neuronal activation (Malik et al., 2014; Kim et al., 2010; Beagan et al., 2020; Joo et al., 2016; Carullo et al., 2020)"

and "IEG-encoded transcription factors such as the AP1 members encoded by Fos, FosB and JunB facilitate the induction of LRGs (Malik et al., 2014). Our data show that IEG-encoded transcription factors remain fully inducible in cohesin-deficient neurons. The failure to induce a subset of LRGs to wild-type levels is therefore not explained by a lack of IEG-encoded factors. This is in marked contrast to inducible gene expression in cohesin-deficient macrophages. A substantial number of LRGs in macrophages rely on the expression of early-induced interferons (IFN), which act in an autocrine and paracrine manner to support LRG induction (Glass and Natoli, 2016). Expression of IFN-dependent LRGs can be partially rescued by provision of exogenous IFN to cohesin-deficient macrophages (Cuartero et al., 2018)".

The most compelling inducible loops known in primary neurons are at Fos and Arc, and our data show that these loops form independently of cohesin. As described in response to the referee's previous point, the revised manuscript now includes an analysis of an inducible loop at the Bdnf locus (Revised Figure 8).

4. Many constitutive neuronal genes are also long genes, and these may be more likely to be disrupted after RAD21 depletion and resulting changes in genome structure and organization. This manuscript should include a supplementary analysis to determine whether RAD21 effects scale with gene length itself, in addition to chromatin loop length.

It is an interesting suggestion that long neuronal genes might be more likely to be disrupted by loss of RAD21, but our analysis does not support this idea. (Author response image 1).

Author response image 1
No scaling between gene length and cohesin-dependence in Rad21 NexCre neurons.

Reviewer #2 (Recommendations for the authors):

I feel that a revised version of the paper should address my comments listed here:

There should be a discussion on whether cohesin dependence is scaling with loop length in other cell types as well. For example, the Cuartero et al., 2018 study by some of the same authors, using approaches similar to the ones presented here in immune cells, and may be good dataset to compare with their findings in neurons.

The comparison between neurons and macrophages suggested by the referee is very interesting. However, Hi-C data that is currently available for macrophages (Mumbach et al., 2019) has lower resolution than the Hi-C data we used for loop calling in cortical neurons (Bonev et al., 2017). As a result, fewer loops are found in macrophages than in cortical neurons (n = 3,029 in macrophages vs n = 24,937 loops in cortical neurons). Consequently, only a fraction of genes expressed in macrophages can be assigned associated loops. This hampers the quantitative comparison of loop lengths between macrophages and neurons, and between cohesin-dependent and -independent genes in macrophages. The findings described below should therefore be regarded as preliminary until this analysis can be repeated once higher-resolution macrophage Hi-C data becomes available.

– Based on the available data we find no difference in the length of loops associated with early- and late-inducible genes in macrophages. If confirmed by higher-resolution data, this would be consistent with the deregulation of both early- and late-inducible genes observed in resting and LPS-activated macrophages (Cuartero et al., 2018)

– Based on the available data we find chromatin loops associated with a significantly higher fraction of cohesin-dependent than with cohesin-independent genes in macrophages (P < 0.0001, odds ratio = 2.70). If confirmed by higher-resolution data, this would be consistent with a requirement for cohesin in the expression of genes with a high degree of 3D connectivity.

– Due to the resolution-related limitations of the macrophage Hi-C data, we believe that it would be premature to include these results in the revised manuscript.

Based on Figure 4a, only a tiny fraction of transcribed genes are immediate early genes IEGs (N=18) and only N=107 genes are secondary response genes. To me, it is not entirely clear how the Authors arrived at these numbers. The number of activity regulated genes should be in the several hundreds, based on the literature (including the Kim et al., 2010 paper that the Authors cite). In any case, one wonders what type of conclusion can be drawn from such a small set of N-18 IEG genes.

We agree that a clear definition of gene sets is required.

We used inducible ARGs described by Kim et al., 2010 for our initial analysis. The number of ARGs with assigned P-values across RNA-seq conditions was n = 298 in the RiboTag RNA-seq of Rad21 NexCre neurons and n = 305 in the RNA-seq analysis of Rad21 NexCre neurons.

For the definition of ARG classes we used gene sets curated by Tyssowski et al., 2018. We refer to rapidly induced, translation-independent ARGs as IEGs (these genes are called rIEGs in Tyssowski et al., 2018, and IEGs in Beagan et al., 2020). To describe late-induced ARGs we had initially adopted the term 'SRG' from Beagan et al., 2020. However, in light of the referees' comments we refer to late-induced ARGs as 'LRGs' in the revised manuscript. LRGs are called translation-independent delayed PRGs and translation-dependent SRGs by Tyssowski et al., 2018.

The number of IEGs as defined by Tyssowski et al., 2018, Supplementary Table 5 is n = 19. Of these, n = 18 had assigned P-values in all conditions of our RNA-seq analysis and informative Hi-C data, and were included in our analysis.

The number of fully annotated LRGs as defined by Tyssowski et al., 2018, Supplementary Table 5 is n = 149 (comprised of 113 delayed translation-independent PRGs and n = 36 translation-dependent SRGs). Of these, the number of LRGs with assigned P-values across RNA-seq conditions were n = 107 in the RNA-seq analysis of Rad21 NexCre neurons, n = 101 in the RNA-seq analysis of RAD21-TEV neurons, and n = 99 in the RNA-seq analysis of transiently RAD21-depleted and subsequently reconstituted neurons.

For inclusion in the comparison of chromatin loop length versus gene expression in cohesin-deficient neurons, ARGs had to meet the following criteria: (i) assigned P-values across RNA-seq conditions, (ii) downregulation or no deregulation across TTX and KCl conditions (ARGs that were downregulated in either TTX or KCl but not in both were excluded), and (iii) informative Hi-C data had to be available for the genomic region of each gene. These conditions were met by n = 18 IEGs, n = 22 downregulated LRGs, and n = 43 non-deregulated LRGs.

We have added this information to the methods section of the revised manuscript.

The Authors , if I understood correctly, probed multiple genomic loci with 5C, but specific information on the sequences and loci probed is hard to find in the paper. From reading the paper, it is clear that c-Fos and BDNF and Arc gene loci (Figures S9-11) were probed with 5C. Please be more specific on the area of genome interrogated with 5C.

We apologise if the manuscript was unclear about the regions interrogated with 5C. We examined 6 different genomic regions:

chr Start End

2 107601077 110913077

10 107002896 109474896

12 86201802 87697802

15 73376037 74580037

17 89772409 92148409

X 97543400 98835400

These coordinates have been added to the Methods section:

"The following regions were analysed in this paper: chr2 107601077-110913077, chr10 107002896-109474896, chr12 86201802-87697802, chr15 73376037-74580037, chr17 89772409-92148409, chrX 97543400-98835400".

The revised figure legends now state the genomic coordinates of the regions examined.

The sensitivity of chromosome conformation capture techniques to detect loops and contacts declines with increasing linear genome distance between the anchors. There should be a discussion and if necessary, a control experiment (I believe in silico may suffice) whether or not the reported cohesion- sensitivity of longer range chromatin loopings could be reflective of the overall lower baseline signal of longer range chromosomal loopings. If so, this would be a significant confound for the Authors conclusion of cohesion dependence scaling with loop length.

We agree with the referee that "the sensitivity of chromosome conformation capture techniques to detect loops and contacts declines with increasing linear genome distance", and that it is important to ask whether or not the apparent cohesin-sensitivity of longer chromatin loops could arise simply because longer loops are weaker. We find that the observed strength of longer loops is consistently different between wild-type and cohesin-deficient neurons (New Figure 8 —figure supplement 1a, loops from Figure 1e), and between resting and activated wild-type neurons for the 1.68Mb inducible loop at the Bdnf locus (New Figure 8 —figure supplement 1a, loops from Figure 8e). At the Fos locus we observe a 155kb CTCF-based, cohesin-dependent loop, and two enhancer-promoter loops that are shorter (20-35kb) and remain fully inducible in cohesin-deficient cells. Despite differences in loop length, cohesin-dependent and cohesin-independent loops at the Fos locus are comparable in strength: The observed strength of the CTCF-based, cohesin-dependent loop in wild-type cells is ~1400. Both cohesin-independent inducible loops are comparable in strength to the CTCF-based, cohesin-dependent loop (~1650 in KCl-stimulated wild-type cells for the 35 kb loop between Fos and Fos enhancer 1, and ~2900 for the cohesin-independent inducible 20kb loop between Fos and Fos enhancer 2 in KCl-stimulated wild-type cells, New Figure 8 —figure supplement 1a, loops from Figure 6e).

Nevertheless, as stated by the referee, the observed values are highly biased by the distance-dependent background signal, the y-axes are on different scales, and the observed values for loop strength therefore need to be used with caution for claims regarding loop strength. New Figure 8 —figure supplement 1b therefore shows the observed loop strength after correction for the distance-dependent background signal (as described by Beagan et al., 2020). This approach allows an assessment of loop strength across distances. The y-axes are now on the same scale, allowing an appreciation of cohesin effects on loop strength, despite the differences in distance. This analysis indicates that the claimed differences in cohesin-dependence exist, independent of differences in loop length (New Figure 8 —figure supplement 1b). We have revised the discussion to state:

"While longer loops are inherently weaker than shorter loops, the observed differences in loop strength were robust to correction for the distance-dependent background signal. This indicates that our 5C approach reliably quantifies the strength of both long and short loops".

Of note, in addition to the cohesin sensitivity of loop formation itself, our study also links the degree to which gene expression is sensitive to cohesin with the length of chromatin loops that are formed by these genes. For this analysis, we measure deregulated gene expression by RNA-seq of wild-type and cohesin-deficient cells, and relate the results to the length of chromatin loops called in Hi-C data from wild-type cells. In this case, the issue of loop length versus loop strength does not arise, because (i) loops are called in wild type Hi-C and (ii) RNA-seq is not sensitive to loop length.

Reviewer #3 (Recommendations for the authors):

My questions about the study fall in two parts.

First, I am left wondering about the significance of the observations and the way they are presented. The relationship between Rad21 dependence and longer loop length is supported by the data, but I struggled to understand what that might mean. Is there anything else the authors can add about why this might be the case? Is this special in neurons and other postmitotic cells and if so what might that mean? More importantly, the authors include a paragraph in the discussion about how promoter-enhancer interactions might work independent of cohesin, which is interesting, and perhaps it is a major finding of the study that those enhancers do not require Rad21. If this is the most important finding then the fact that this study reaches a different conclusion that Yamada 2019 needs to be directly addressed. That study also knocked out Rad21 in postmitotic neurons (CGNs) but did report disruption of promoter-enhancer loops and activity-induced gene expression. These data may well be stronger than those, but the authors should directly address the differences in the findings.

Our findings indicate that short enhancer-promoter loops at the Fos and Arc loci form in the absence of cohesin, while a much longer 1.68Mb enhancer-promoter loop at the Bdnf locus requires cohesin (new data in revised Figure 8). Constitutive, CTCF-based loops also require cohesin. Correlative evidence shows that genes that are deregulated in cohesin-deficient neurons engage in longer loops than genes that are expressed at wild-type levels. This suggests – but does not prove – that the shorter loops associated with genes expressed at wild-type levels may remain intact in the absence of cohesin.

The ability of short enhancer-promoter loops to form – or at least persist – in the absence of cohesin does not appear to be unique to neurons or post-mitotic cells. Specifically, shorter enhancer-promoter loops are more likely to persist after acute cohesin depletion in HeLa cells than longer loops (Thiecke et al., 2020). Similarly, two recent preprints report short – but not long – enhancer-promoter loops in the absence of cohesin (Kane et al., 2021; Rinzema et al., 2021).

We suggest that our data do not contradict the findings reported by Yamada et al., 2019. The Yamada study lacked sufficient resolution for the analysis of individual loops, and the authors performed aggregate loop analysis instead. This approach would not have uncovered a subset of shorter loops that persist in the absence of cohesin. Similarly, the analysis of gene expression by Yamada et al., 2019 grouped genes into modules, and would not have uncovered the subset of inducible genes that remain inducible in the absence of cohesin.

Second, given that the authors find the expression of many ion channels and synaptic proteins impacted by the loss of Rad21, they need to consider that any changes they see in impaired induction of activity-regulated genes – such as the reduced activation of Bdnf IV in Suppl Figure 11 – may arise from impaired intracellular signaling rather than a specific effect of Rad21 on the architecture of the Bdnf gene. Controls for the activation of signaling cascades and transcription factors could be added to address this concern. The demonstration that much of the effect of Rad21 knockout on activity-regulated genes is their reduction at baseline is certainly consistent with this idea of reduced synaptic drive to these genes, which is ongoing in cultures. KCl addition may largely overcome those deficits by providing such a strong drive through LVGCCs.

We agree with the referee that defects in maturation and connectivity likely contribute to the deregulation of baseline gene expression in Rad21 NexCre neurons, but can be partially are overcome by stimulation with KCl or BDNF. This is one of the reasons we performed parallel experiments with acutely cohesin-depleted neurons.

We are not aware of signaling pathways that would unambiguously distinguish the subset of neuronal LRGs that are fully induced in the absence of cohesin from the subset of neuronal LRGs that remain deregulated in the absence of cohesin. We have added to the discussion that TFs encoded by IEGs are required for the induction of LRGs, and that these IEGs are fully induced in cohesin-deficient neurons.

"IEG-encoded transcription factors such as the AP1 members encoded by Fos, FosB and JunB facilitate the induction of LRGs (Malik et al., 2014). Our data show that IEG-encoded transcription factors remain fully inducible in cohesin-deficient neurons. The failure to induce a subset of LRGs to wild-type levels is therefore not explained by a lack of IEG-encoded factors. This is in marked contrast to inducible gene expression in cohesin-deficient macrophages. A substantial number of LRGs in macrophages rely on the expression of early-induced interferons (IFN), which act in an autocrine and paracrine manner to support LRG induction (Glass and Natoli, 2016). Expression of IFN-dependent LRGs can be partially rescued by provision of exogenous IFN to cohesin-deficient macrophages (Cuartero et al., 2018)".

We agree that the failure of cohesin-deficient neurons to express certain genes at wild-type levels in may not necessarily result from defective chromatin architecture at each deregulated gene, but in some cases may arise from disturbances in upstream regulatory mechanisms, such as the activity of specific signaling pathways or the expression of particular transcription factors. We have added a Discussion section to acknowledge this:

"In addition to defective chromatin architecture, deregulated expression of a particular gene may also arise from disturbances in upstream regulatory mechanisms, such as the activity of specific signaling pathways, or the expression of particular transcription factors. We are not aware of signaling pathways that would unambiguously distinguish the subset of neuronal LRGs that are fully induced in the absence of cohesin from the subset of neuronal LRGs that remain deregulated in the absence of cohesin. As discussed above, TFs encoded by IEGs are required for the induction of LRGs. Of note, these IEGs are fully induced in cohesin-deficient neurons."

A few other questions:

1) When the authors refer to "activity-regulated genes" for example in Figure 2C, do they include only genes induced by neuronal activity or also those repressed by neuronal activity? This distinction is important because the meaning of those genes begin "Up" or "Down" in the Rad21 cKO is very different depending on the sign of their response to neuronal stimulation. This should be explicitly stated.

We thank the referee for this important comment. We indeed focus our analysis on inducible activity-regulated genes. We now explicitly refer to "inducible ARGs" in the revised manuscript.

2) The data in Figure 3a on the distribution of different cell types in the cortex of Rad21 cKO mice is entirely anecdotal as presented. If the data are to be included they should be quantified and analyzed with appropriate statistics.

We agree with the referee. Figure 3a of the revised manuscript now presents a quantitative analysis of TBR1+ and CTIP2+ neurons in the subplate and in layers 5 and 6 (LV and VI) in control and Rad21 NexCre brains at E16.5. We have added the following passage to the Results section:

"While the total numbers of TBR1+ and CTIP2+ neurons were comparable in wild-type and Rad21lox/lox cortices, TBR1+ and CTIP2+ neurons were reduced in the subplate and increased in layers 6 and 7 Rad21lox/lox NexCre cortices."

3) The terms IEG and SRG for the ARG classes are used here in a way that is not consistent with the literature. The division between the terms as the authors use them here seems to be time (IEGs early and SRGs late) whereas in the past the terms had more to do with mechanism – IEGs did not require stimulus-induced protein synthesis before they could be turned on whereas SRGs did. (Basically cycloheximide does not block IEG induction but it does block SRG induction). The Tyssowski paper referenced by the authors uses the language PRG for primary response genes that do not require protein synthesis for their induction, and then divide them into early and delayed PRGs for the reflection of time. BDNF is a delayed PRG and following the stimuli used here it does not require protein synthesis for its induction. The authors may want to consider clarifying their use of their terms depending on whether time or mechanism of transcription is their main focus to match other literature.

We agree that a clear definition of gene sets will benefit the manuscript. We used inducible ARGs described by Kim et al., 2010 for our initial analysis. The number of ARGs with assigned P-values across RNA-seq conditions was n = 298 in the RiboTag RNA-seq of Rad21 NexCre neurons and n = 305 in the RNA-seq analysis of Rad21 NexCre neurons.

For the definition of ARG classes we used gene sets curated by Tyssowski et al., 2018. We refer to rapidly induced, translation-independent ARGs as IEGs (these genes are called rIEGs in Tyssowski et al., 2018, and IEGs in Beagan et al., 2020). To describe late-induced ARGs we had initially adopted the term 'SRG' from Beagan et al., 2020. However, in light of the referees' comments we refer to late-induced ARGs as 'LRGs' in the revised manuscript. LRGs are called translation-independent delayed PRGs and translation-dependent SRGs by Tyssowski et al., 2018.

The number of IEGs as defined by Tyssowski et al., 2018, Supplementary Table 5 is n = 19. Of these, n = 18 had assigned P-values in all conditions of our RNA-seq analysis and informative Hi-C data, and were included in our analysis.

The number of fully annotated LRGs as defined by Tyssowski et al., 2018, Supplementary Table 5 is n = 149 (comprised of 113 delayed translation-independent PRGs and n = 36 translation-dependent SRGs). Of these, the number of LRGs with assigned P-values across RNA-seq conditions were n = 107 in the RNA-seq analysis of Rad21 NexCre neurons, n = 101 in the RNA-seq analysis of RAD21-TEV neurons, and n = 99 in the RNA-seq analysis of transiently RAD21-depleted and subsequently reconstituted neurons.

For inclusion in the comparison of chromatin loop length versus gene expression in cohesin-deficient neurons, ARGs had to meet the following criteria: (i) assigned P-values across RNA-seq conditions, (ii) downregulation or no deregulation across TTX and KCl conditions (ARGs that were downregulated in either TTX or KCl but not in both were excluded), and (iii) informative Hi-C data had to be available for the genomic region of each gene. These conditions were met by n = 18 IEGs, n = 22 downregulated LRGs, and n = 43 non-deregulated LRGs.

We have added this information to the methods section of the revised manuscript.

4) There is a paper from Kim Nasmyth's lab that knocked out Rad21 in postmitotic neurons of the fly with relatively severe phenotypes. By contrast at least the morphological phenotypes in the images in Suppl Figure 4 seem quite mild. This might be an important point of discussion if it is the case that the consequences of Rad21 function are different by species?

The observed changes may appear mild at first sight, but the data in Figure 3 —figure supplement 1a show that neuronal Rad21is required for postnatal viability in mice. We found Rad21lox/lox NexCre mice at the expected Mendelian ratios up to the end of gestation, but none were alive by 2 weeks.

5) It is a little surprising that all the data from the final paragraphs on activity-regulated genes are in the supplementary figures. This points to evidence that the authors do not consider these to be the most important of their findings, which is slightly out of balance with the attention paid to activity-dependent genes in the text. Perhaps a rewrite of the paper would make the significance of the findings more obvious.

We thank the referee for this important comment. To re-balance the manuscript we have (i) strengthened the developmental aspects of the manuscript by adding the quantitative analysis or cortical layer organisation discussed above (ii) added new data on the formation of an inducible Bdnf loop (revised Figure 8) and (iii) moved important data into main figures, specifically Bdnf (revised Figure 8), and the rescue of inducible gene expression by cohesin (revised Figure 5d and 6b).

https://doi.org/10.7554/eLife.76539.sa2

Article and author information

Author details

  1. Lesly Calderon

    1. MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
    2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Investigation, Visualization, Writing - review and editing
    Contributed equally with
    Felix D Weiss and Jonathan A Beagan
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5253-7369
  2. Felix D Weiss

    Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Investigation, Visualization, Writing - review and editing
    Contributed equally with
    Lesly Calderon and Jonathan A Beagan
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0228-5081
  3. Jonathan A Beagan

    Department of Bioengineering, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Visualization, Writing - review and editing
    Contributed equally with
    Lesly Calderon and Felix D Weiss
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7049-3467
  4. Marta S Oliveira

    1. MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
    2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Radina Georgieva

    1. MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
    2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Formal analysis, Visualization, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Yi-Fang Wang

    1. MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
    2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Data curation, Formal analysis, Methodology, Supervision, Visualization, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Thomas S Carroll

    Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Data curation, Formal analysis, Methodology, Supervision, Visualization, Writing - review and editing
    Competing interests
    No competing interests declared
  8. Gopuraja Dharmalingam

    1. MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
    2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Data curation, Formal analysis, Methodology, Visualization
    Competing interests
    No competing interests declared
  9. Wanfeng Gong

    Department of Bioengineering, University of Pennsylvania, Philadelphia, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  10. Kyoko Tossell

    Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Investigation, Methodology, Visualization
    Competing interests
    No competing interests declared
  11. Vincenzo de Paola

    Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Conceptualization, Methodology
    Competing interests
    No competing interests declared
  12. Chad Whilding

    1. MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
    2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  13. Mark A Ungless

    1. MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
    2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Conceptualization, Methodology, Supervision
    Competing interests
    No competing interests declared
  14. Amanda G Fisher

    1. MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
    2. Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Conceptualization, Writing - review and editing
    Competing interests
    No competing interests declared
  15. Jennifer E Phillips-Cremins

    1. Department of Bioengineering, University of Pennsylvania, Philadelphia, United States
    2. Epigenetics Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    3. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Formal analysis, Methodology, Supervision, Visualization, Writing - original draft, Writing - review and editing
    For correspondence
    jcremins@seas.upenn.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4702-0450
  16. Matthias Merkenschlager

    Institute of Clinical Sciences, Faculty of Medicine, Imperial College, London, United Kingdom
    Contribution
    Conceptualization, Supervision, Visualization, Writing - original draft, Writing - review and editing
    For correspondence
    matthias.merkenschlager@lms.mrc.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2889-3288

Funding

Medical Research Council

  • Matthias Merkenschlager

Wellcome Trust (099276/Z/12/Z)

  • Matthias Merkenschlager

European Molecular Biology Organization (ALTF 1047-2012)

  • Lesly Calderon

Human Frontier Science Program (LT00427/2013)

  • Lesly Calderon

National Institutes of Health (1R01-MH120269)

  • Jennifer E Phillips-Cremins

National Institutes of Health (1DP1OD031253)

  • Jennifer E Phillips-Cremins

National Institutes of Health (1R01-NS114226)

  • Jennifer E Phillips-Cremins

4D Nucleome Common Fund (1U01DK127405)

  • Jennifer E Phillips-Cremins

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. For the purpose of Open Access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

Acknowledgements

We thank Drs G Little, M Clements, and S Parrinello (University College London) for advice and practical instruction, S Di Giovanni and J Merkenschlager (Rockefelller University) for comments on the manuscript, members of our labs for helpful discussions, L Game for sequencing, and J Elliott and B Patel for cell sorting. This work was supported by the Medical Research Council UK, The Wellcome Trust (Investigator Award 099276/Z/12/Z to MM), EMBO (ALTF 1047–2012 TO LC), HFSP (LT00427/2013 to LC), the NIH National Institute of Mental Health (1R01-MH120269; 1DP1OD031253; JEPC), the NIH National Institute of Neural Disorders and Stroke (1R01-NS114226; JEPC), and a 4D Nucleome Common Fund grant (1U01DK127405; JEPC).

Ethics

Laboratory bred mice of the appropriate genotype were maintained under SPF conditions and 12h light/dark cycle. Embryos were used to derive cells and tissues. Ethical approval was granted by the Home Office, UK, and the Imperial College London Animal Welfare and Ethical Review Body (AWERB).

Senior Editor

  1. Kevin Struhl, Harvard Medical School, United States

Reviewing Editor

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

Publication history

  1. Preprint posted: February 24, 2021 (view preprint)
  2. Received: December 20, 2021
  3. Accepted: April 26, 2022
  4. Accepted Manuscript published: April 26, 2022 (version 1)
  5. Version of Record published: May 13, 2022 (version 2)

Copyright

© 2022, Calderon 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. Lesly Calderon
  2. Felix D Weiss
  3. Jonathan A Beagan
  4. Marta S Oliveira
  5. Radina Georgieva
  6. Yi-Fang Wang
  7. Thomas S Carroll
  8. Gopuraja Dharmalingam
  9. Wanfeng Gong
  10. Kyoko Tossell
  11. Vincenzo de Paola
  12. Chad Whilding
  13. Mark A Ungless
  14. Amanda G Fisher
  15. Jennifer E Phillips-Cremins
  16. Matthias Merkenschlager
(2022)
Cohesin-dependence of neuronal gene expression relates to chromatin loop length
eLife 11:e76539.
https://doi.org/10.7554/eLife.76539
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