Part 6:Mapping The Epigenomic And Transcriptomic Interplay During Memory Formation And Recall in The Hippocampal Engram Ensemble
Mar 22, 2022
ali.ma@wecistanche.com
Click to Cistanche stem can improve memory
References
1. Alberini CM Transcription Factors in Long-Term Memory and Synaptic Plasticity. Physiol. Rev 89, 1–46 (2014).
2. Josselyn SA, Köhler S & Frankland PW Finding the engram. Nat. Rev. Neurosci 16, 521–534(2015). [PubMed: 26289572]
3. Albo Z & GräffJ Engram Excitement. Neuron 101, 198–200 (2019). [PubMed: 30653932]
4. Poo M. ming et al. What is memory? The present state of the engram. BMC Biol (2016). doi:10.1186/s12915-016-0261-6
5. Guenthner CJ, Miyamichi K, Yang HH, Heller HC & Luo L Permanent genetic access to transiently active neurons via TRAP: Targeted recombination inactive populations. Neuron 78, 773–784(2013). [PubMed: 23764283]
6. Denny CA et al. Hippocampal memory traces are differentially modulated by experience, time, and adult neurogenesis. Neuron 83, 189–201 (2014). [PubMed: 24991962]
7. Ramirez S et al. Creating a False Memory in the Hippocampus. Science (80-.). 341, 819–824(2013).
8. Khalaf O et al. Reactivation of recall-induced neurons contributes to remote fear memory attenuation. Science (80-.). 1242, 1239–1242 (2018).
9. Levenson JM & Sweatt JD Epigenetic mechanisms in memory formation. Nat. Rev. Neurosci 6, 108–118 (2005). [PubMed: 15654323]
10. Gräff J et al. Epigenetic priming of memory updating during reconsolidation to attenuate remote fear memories. Cell 156, 261–276 (2014). [PubMed: 24439381]
11. Nguyen TA et al. High-throughput functional comparison of promoter and enhancer activities. Genome Res 26, 1023–1033 (2016). [PubMed: 27311442]
12. Malik AN et al. Genome-wide identification and characterization of functional neuronal activity-dependent enhancers. Nat Neurosci 17, 1330–1339 (2015).
13. Su Y et al. Neuronal activity modifies the chromatin accessibility landscape in the adult brain. Nat.Neurosci 20, 476–483 (2017). [PubMed: 28166220]
14. Rajarajan P, Gil SE, Brennand KJ & Akbarian S Spatial genome organization and cognition. Nat. Rev. Neurosci 17, 681–691 (2016). [PubMed: 27708356]
15. Lieberman-Aiden E et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science (80-.). (2009). doi:10.1126/science.1181369
16. Dekker J et al. The 4D nucleome project. Nature (2017). doi:10.1038/nature23884
17. Yu M & Ren B The Three-Dimensional Organization of mammalian Genomes. Annu. Rev. Cell Dev. Biol (2017). doi:10.1146/annurevcellbio-100616-060531
18. Shlyueva D, Stampfel G & Stark A Transcriptional enhancers: From properties to genome-wide predictions. Nat. Rev. Genet 15, 272–286 (2014). [PubMed: 24614317]
19. Nader K, Schafe GE & Le Doux JE Fear memories require protein synthesis in the amygdala for reconsolidation after retrieval. Nature (2000). doi:10.1038/35021052
20. Rao-Ruiz P et al. Engram-specific transcriptome profiling of contextual memory consolidation. Nat. Commun (2019). doi:10.1038/s41467-019-09960-x
21. Halder R et al. DNA methylation changes in plasticity genes accompany the formation and maintenance of memory. Nat. Neurosci. 19, 102–110 (2015). [PubMed: 26656643]
22. Park CS, Rehrauer H & Mansuy IM Genome-wide analysis ofH4K5 acetylation associated with fear memory in mice. BMC Genomics (2013). doi:10.1186/1471-2164-14-539
23. Kim T et al. Widespread transcription at neuronal activity-regulated enhancers. Nature 465, 182– 187 (2010). [PubMed: 20393465]
24. Vierbuchen T et al. AP-1 Transcription Factors and the BAF Complex Mediate Signal-Dependent Enhancer Selection. Mol. Cell 68, 1134–1146.e6 (2017). [PubMed: 29225033]
25. Weintraub AS et al. YY1 Is a Structural Regulator of enhancer-Promoter Loops. Cell 171, 1573– 1588.e28 (2017). [PubMed: 29224777]
26. Bonev B et al. Multiscale 3D Genome Rewiring during Mouse Neural Development. Cell 171, 557–572.e24 (2017). [PubMed: 29053968]
Cistanche can improve memory
27. Fernandez-Albert J et al. Immediate and deferred epigenomic signatures of in vivo neuronal activation in mouse hippocampus. Nat. Neurosci (2019). doi:10.1038/s41593-019-0476-2
28. Yamada T et al. Sensory experience remodels genome architecture in neural circuit to drive motor learning. Nature (2019). doi:10.1038/s41586-019-1190-7
29. Schoenfelder S et al. The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements. Genome Res. 25, 582–597 (2015). [PubMed: 25752748]
30. Sanyal A, Lajoie BR, Jain G & Dekker J The long-range interaction landscape of gene promoters. Nature (2012). doi:10.1038/nature11279
31. Joo JY, Schaukowitch K, Farbiak L, Kilaru G & Kim TK Stimulus-specific combinatorial functionality of neuronal c-fos enhancers. Nat. Neurosci (2015). doi:10.1038/nn.4170
32. Jaeger BN et al. A novel environment-evoked transcriptional signature predicts reactivity in single dentate granule neurons. Nat. Commun (2018). doi:10.1038/s41467-018-05418-8
33. Tyssowski KM et al. Different Neuronal Activity Patterns Induce Different Gene Expression Programs. Neuron (2018). doi:10.1016/j.neuron.2018.04.001
34. Gaidatzis D, Burger L, Florescu M & Stadler MB Analysis of intronic and exonic reads in RNA- seq data characterize transcriptional and post-transcriptional regulation. Nat. Biotechnol (2015). doi:10.1038/nbt.3269
35. Chandra D et al. GABAA receptor 4 subunits mediate extrasynaptic inhibition in thalamus and dentate gyrus and the action of gaboxadol. Proc. Natl. Acad. Sci (2006). doi:10.1073/ pans.0604304103
36. Pignatelli M et al. Engram Cell Excitability State Determines the Efficacy of Memory Retrieval. Neuron 101, 274–284.e5 (2019). [PubMed: 30551997]
37. Sun X et al. Functionally Distinct Neuronal Ensembles within the Memory Engram. Cell (2020). doi:10.1016/j.cell.2020.02.055
38. Yuan A & Nixon RA Specialized roles of neurofilament proteins in synapses: Relevance to neuropsychiatric disorders. Brain Research Bulletin (2016). doi:10.1016/j.brainresbull.2016.09.002
39. Bramham CR & Wells DG Dendritic mRNA: transport, translation and function. Nat. Rev. Neurosci 8, 776–789 (2007). [PubMed: 17848965]
40. Choi JH et al. Interregional synaptic maps among engram cells underlie memory formation. Science (80-.). (2018). doi:10.1126/science.aas9204
41. Shi SH et al. Rapid spine delivery and redistribution of AMPA receptors after synaptic NMDA receptor activation. Science (80-.). (1999). doi:10.1126/science.284.5421.1811
42. Ostroff LE, Fiala JC, Allwardt B & Harris KM Polyribosomes redistribute from dendritic shafts into spines with enlarged synapses during LTP in developing rat hippocampal slices. Neuron (2002). doi:10.1016/S0896-6273(02)00785-7
43. Chung SH et al. Zac1 plays a key role in the development of specific neuronal subsets in the mouse cerebellum. Neural Dev (2011). doi:10.1186/1749-8104-6-25
44. Ravasi T et al. An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man. Cell (2010). doi:10.1016/j.cell.2010.01.044
45. Schlisio S, Halperin T, Vidal M & Nevins JR Interaction ofYY1 with E2Fs, mediated by RYBP, provides a mechanism for specificity ofE2F function. EMBO J 21, 5775–5786 (2002). [PubMed: 12411495]
46. Korb E, Wilkinson CL, Delgado RN, Lovero KL & Finkbeiner S Arc in the nucleus regulates PML-dependent GluA1 transcription and homeostatic plasticity. Nat. Neurosci (2013). doi:10.1038/nn.3429
47. Prickett AR et al. Genome-wide and parental allele-specific analysis of CTCF and cohesin DNA binding in mouse brain reveals a tissue-specific binding pattern and an association with imprinted differentially methylated regions. Genome Res 23, 1624–1635 (2013). [PubMed: 23804403]
48. Kinde B, Wu DY, Greenberg ME & Gabel HW DNA methylation in the gene body influences MeCP2-mediated gene repression. Proc. Natl. Acad. Sci 113, 15114–15119 (2016). [PubMed: 27965390]
49. Buenrostro JD, Giresi PG, Zaba LC, Chang HY & Greenleaf WJ Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013). [PubMed: 24097267]
50. Langmead B & Salzberg SL Fast gapped-read alignment with Bowtie 2. Nat. Methods (2012). doi:10.1038/nmeth.1923
51. Li H et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics (2009). doi:10.1093/bioinformatics/btp352
52. Zhang Y et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol (2008). doi:10.1186/ GB-2008-9-9-r137
53. Stark R & Brown G DiffBind: differential binding analysis of ChIP-Seq peak data. Cancer Res(2011). doi:10.1093/nar/gkv1191
54. Robinson MD & Oshlack A A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol (2010). doi:10.1186/GB-2010-11-3-r25
55. Thorvaldsdóttir H, Robinson JT & Mesirov JP Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration. Brief. Bioinform (2013). doi:10.1093/bib/bbs017
56. Ernst J & Kellis M Chromatin-state discovery and genome annotation with ChromHMM. Nat. Protoc 12, 2478–2492 (2017). [PubMed: 29120462]
57. Benner C, Heinz S & Glass CK HOMER - Software for motif discovery and next-generation sequencing analysis. Http://Homer.Ucsd.Edu/ (2017).
58. Schep AN et al. Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions. Genome Res (2015). doi:10.1101/gr.192294.115
59. Heinz S et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell (2010). doi:10.1016/ j.molcel.2010.05.004
60. Trapnell C et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc (2012). doi:10.1038/nprot.2012.016
61. Robinson MD, McCarthy DJ & Smyth GK edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics (2009). doi:10.1093/ bioinformatics/btp616
62. Nagano T et al. Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat. Protoc 10, 1986–2003 (2015). [PubMed: 26540590]
63. Wingett SW et al. HiCUP: a pipeline for mapping and processing Hi-C data. F1000Research (2015). doi:10.12688/f1000research.7334.1
64. Cairns J et al. Chicago: Robust detection of DNA looping interactions in Capture Hi-C data. Genome Biol 17, 1–17 (2016). [PubMed: 26753840]
65. Kagey MH et al. Mediator and cohesin connect gene expression and chromatin architecture. Nature (2010). doi:10.1038/nature09380
Fig. 1. Temporal and spatial identification of activated and reactivated engram cells.
(a) Schematic representation of the experimental design. TRAP mice were initially exposed to training in which a conditioned (context) and unconditioned (foot shock, FS) stimulus were paired together within a brief window of 4-Hydroxytamoxifen (TAM) exposure. Four different neuronal populations (Basal, Activated –early, Activated –late and Reactivated) were sorted by flow cytometer and subjected to library preparation for nRNA-seq, ATAC- seq, Hi-C and pc-HiC.
(b) Representative images (DG) and whole hippocampus quantifications of eYFP+ neurons after TAM treatment in home cage (naïve, n = 4 mice/6 slices per animal), 1.5 h (Activated – early, n = 4 mice/4 slices per animal) and 5 days (Activated –late, n = 4 mice/8 slices per animal) after exposure after FS. The scale bar represents 200 μm. The dot-plot indicates the mean and error bars (SEM), one-way ANOVA (parametric, unpaired), F (2, 70) = 240.3, P<0.0001. Bonferroni’s multiple comparisons test; HC vs. Early ***P = 0.0007, HC vs. Late****P < 0.0001, Early vs. Late ****P < 0.0001.
(c) Quantifications ofActivated –late and Reactivated neurons in the hippocampus in two experimental groups; CFC with (CFC+TAM, n = 4 mice/8 slices per animal) or without (CFC NO-TAM, n = 4 mice/6 slices per animal) tamoxifen administration. The dot-plot indicates the mean and error bars (SEM), two-sided unpaired Student’s t-test, Activated – late; t=25.65, df=47, Reactivated; t=11.00, df=52, ****P < 0.0001.
(d) Representative images and quantifications from different regions ofthe hippocampus. Number of positive nuclei were analyzed using IMARIS (see methods) from the DG, CA1 and CA3. The scale bar represents 100 μm. n = 5 mice/ Activated –early: 6 slices per animal, n = 6 mice/ Activated –late: 6 Reactivated: 6 slices per animal. The dot-plot indicates the mean and error bars (SEM), one-way ANOVA (parametric, unpaired), Early; F (2, 42) = 13.46. Late; F (2, 95) = 556.3. Reactivated; F (2, 89) = 83.53. All Ps < 0.0001. Bonferroni’s multiple comparisons test, *P= 0.029, **P= 0.0075, ***P= 0.0009, ****P< 0.0001. (e) Valuation of context effects on the number and the distribution of hippocampal memory traces.
(f) Quantifications ofActivated -late and Reactivated neurons in the A-B (n = 6 mice/ 6 slices per animal), compared to the A-A (n = 5 mice/ 6 slices per animal) group. The dot- plot indicates the mean and error bars (SEM), two-sided unpaired Student’s t-test, Activated –late; t=0.08292, df=59, P = 0.9342. Reactivated; t=8.385, df=57, ****P< 0.0001.n.s = non-significant.
Fig. 2. Chromatin accessibility landscape during memory formation and recall.
(a) Differential accessible regions (DAR)s are shown in a volcano plot (Pairwise comparisons, FDR < 0.01; fold changes > 1.5), n = 3 biologically independent samples. (b) Analysis of common gained- accessible region (stable), acquired 90 min after the FS (Activated -early), that remain accessible in the late state of consolidation (Activated -late) and recall (Reactivated). Overlapping stable DARs were only considered if they appeared in at least 2 biological replicates in a given condition.
(c) Representative IGV genome browser tracks of the Arc locus.
(d) Annotation analysis (HOMER tools) for all the DARs. Numbers on the y-axis indicate the Log2 ratio values of observed loci (number of DARs) over expected (total size (bp)) of the TTS, exons, introns, intergenic and promoters. P-value were calculated by HOMER
tools; Basal vs. Activated -early (Intergenic *P = 1.73 × 10−103), Activated -early vs. Activated -late (TTS *P = 0.0339, Promoter *P = 1.48 × 10−39), Activated -late vs. Reactivated (Promoter *P = 2.48 × 10−6), stable (Intergenic *P = 0.0358).
(e) Histone modification emission states (left) and enrichment analysis offers over emissions using ChromHMM. Fold enrichment scale (FE) 0–30. The and p-value was determined by the z-score value (as calculated in Extended Data Fig. 2c), Full analysis is reported in supplement table 3. *P < 0.00001, reported only for enriched values (not depleted)
(f) Chip-qPCR. Samples were immunoprecipitated with antibodies against H3K27ac or H3K4me1 and subjected to real-time PCR with two sets of primers for identified putative enhancers. H3K27ac: n = 4, H3K4me1 n = 3, The dot-plot indicates the mean and error bars (SEM), one-way ANOVA (parametric, unpaired) with Dunnett’s multiple comparisons test, Enhancer 1; H3K27ac - F (3, 12) = 4.664, P = 0.02, (Basal versus Activated-late *P = 0.0246, Basal versus Reactivated *P = 0.0232), H3K4me1 - F (3, 8) = 0.845, P = 0.506. Enhancer 2; H3K27ac - F (3, 12) = 5.221, P = 0.0155, (Basal vs. Activated -late **P = 0.0059), H3K4me1 - F (3, 8) = 1.719, P = 0.24. Enhancer 3; H3K27ac - F (3, 12) = 29.91, P < 0.0001, H3K4me1 - F (3, 8) = 10.39, P = 0.0039, (Basal vs. Activated -early *P = 0.0298, Basal vs. Activated -late **P = 0.0021, Basal vs. Reactivated **P = 0.0063). Enhancer 4; H3K27ac - F (3, 12) = 6.273, P = 0.0083, (Basal vs. Activated -late **P = 0.007, Basal vs. Reactivated *P = 0.0152), H3K4me1 - F (3, 8) = 9.901, P = 0.0045, (Basal vs. Reactivated **P = 0.0016).
Fig. 3. Sub-compartment switching across the different phases of memory formation and recall.
(a) Hi-C correlation heatmap represents compartmentalization of chr6 for Basal, Activated - early and Activated -late cells. Compartments calculated by Juicer at 500 kb resolution (observed–expected normalized).
Activated -late). Compartment switch was only considered if a negative value was transformed to a positive value and vice versa.
(c) Representative images from IGV browser that shows stable compartment switch from A to B (upper panel) and B to A (lower panel) across different memory phases.
(d) Overlap analysis between DARs in the early phase (Basal vs. Activated –early) and stable switched compartment. Left Venn diagram shows gained DARs and stable compartment switched from B to A. Right, Venn diagram shows lost DARs and stable compartment switched from A to B.
(e) Illustration of activity-induced compartment switch and specific promoter-enhancers interactions.
Fig. 4. Promoters interact more frequently with a distinct subset of enhancers during each
(a) Heatmap represent the interaction score as defined by Chicago, for each population. Basal: n = 3, Activated -early, Activated -late and Reactivated: n = 4 biologically independent samples.
(b) Violin plots show the distribution and the intensity (log10 scale) of interaction that were either unique or shared by at least 3 populations (common). One-way ANOVA (parametric, unpaired), F (4, 57922) = 2364, P < 0.0001. Bonferroni’s multiple comparisons test, n.s = non-significant (P = 0.7659), *P < 0.0001.
(c) Frequency of promoter-enhancer interaction as measured by 3C-qPCR for selected loci. Four sets of primers encompassing interaction between promoter-enhancer (P/E), and three random loci inside the loop (P/Ein), outside the loop (P/Eout), and on the promoter (P/Pin).
Reads were normalized to promoter-enhancer interaction values of the housekeeping gene βActin. n = 3 biologically independent samples. Data are presented as mean values with error bars indicate the SEM, two-way ANOVA. Eif3d; population × interaction location (F
(9, 32) = 1.917, P=0.0851), effect of interaction location only (F (3, 32) = 22.64, P < 0.0001). Grik3; population × interaction location F (9, 32) = 0.1716. Bonferroni’s multiple comparisons test, P/E Basal vs. Reactivated **P = 0.0019.
(d) Representative priming events of the Eif5a locus. IGV Genome browser tracks presenting chromatin accessibility dynamics (colored tracks) on the promoter (gray rectangle) and enhancers (blue rectangle). WashU browser tracks present significant promoter-enhancer interaction via arcs (purple - Basal, light green - Activated -early, dark green - Activated - late, and orange - Reactivated).
Fig. 5. Transcriptional signature of reactivated engram neurons
(a) Pairwise analysis of DEGs from the hippocampus trap mice that were exposed to CFC are shown in a volcano plot (FDR<0.01; log2FC > 2). n = 3 biologically independent samples.
unpaired), followed by Bonferroni’s multiple-comparison test. Dw–Late; F (3, 1032) = 42.19, Up-Late; F (3, 280) = 752.5, Stable; F (3, 764) = 92.28, Reactivation; F (3, 1744) =597.4. All Ps < 0.0001. multiple comparisons test, n.s = non-significant, ***P < 0.0001. (d) Gene ontology (GO) analysis (ToppFun) for DEGs that were identified in each cluster. (e) Representative DEGs from each cluster. log2FC from the basal-state is presented in three biological replicates. Line represent calculated average. n = 3 biologically independent samples, one-way ANOVA (parametric, unpaired) with Dunnett’s multiple comparisons test, n.s = non-significant. Gad1; F (3, 8) = 4.387, P= 0.04. *P = 0.0268. Gabrb3; F (3, 8) = 6.184, P=0.0177. *P = 0.0441. Rest; F (3, 8) = 23.23, P=0.0003. ***P = 0.0268. Map4k4; F (3, 8) = 6.244, P=0.0172. *P = 0.0151. Nefl;F (3, 8) = 12.06, P=0.0024. **P = 0.0059, **P = 0.0014. Nefh; F (3, 8) = 21.50, P=0.0003. *P = 0.016, ***P = 0.0008, ***P = 0.0002. Eif5a; F (3, 8) = 8.482, P=0.0072. **P = 0.004. Eif4h; F (3, 8) = 26.58, P=0.0002. ***P =0.0001.
Cistanche can improve memory
(f) Representative images and quantification of Eif4e protein levels from Activated -early, Activated -late, and Reactivated. For quantification, two separate surfaces were built on IMARIS; cell soma and dendritic shaft. The number ofEif4e puncta were measured only in those surfaces boundaries. Data for dendritic shaft is presented as a ratio between number and the length (μM). The scale bar represents 10 μm. n = 4 mice /5 section per animal, boxplot indicates the mean, interquartile range and the minimum and maximum, one-way ANOVA (parametric, unpaired), Shaft; F (2, 28) = 5.113, P=0.0128. Soma; F (2, 43) = 3.026, P=0.0589. Bonferroni’s multiple comparisons test; Shaft; Activated -early vs. Reactivated *P = 0.0324, Activated -late vs. Reactivated *P = 0.0324
(g) Representative images (left panel) and quantification (right panel) of the Gria1 protein levels, during different phases of memory. Three populations of neurons (Activated -early, Activated -late, and Reactivated) were analyzed, based on the immunoreactivity of eYFP and endogenous Arc. For quantification, MARIS surfaces were built on the dendritic shaft and the number ofGRIA1 puncta were measured only in the boundaries of the surface (<1μm threshold). Data is presented as a ratio between several puncta and the shaft length. The scale bar represents 10 μm. n = 4 mice /5 section per animal, boxplot indicates the mean, interquartile range, and the minimum and maximum, one-way ANOVA, F (2, 16) = 15.22, P = 0.0002. Bonferroni’s multiple comparisons test, **P = 0.002, ***P = 0.0005.
Fig. 6. Sequential reprogramming of chromatin accessibility, promoter-enhancer interactions and
Line graphs for each K-means cluster show the average (log2 scale) of (a) transcriptional changes (p-values as reported in Fig. 5c, mean of n = 3 biologically independent samples) (b) chromatin accessibility (normalized RPKM values, mean of n = 3 biologically independent samples) and (c) promoter-enhancer interaction scores (as calculated by Chicago, mean basal: n = 3, Activated -early, Activated -late and Reactivated: n = 4 biologically independent samples) Repeated measures one-way ANOVA (between groups).
638.7. Stable; F (2.437, 4504) = 717.9. Reactivation; F (2.434, 8481) = 1425. All Ps < 0.0001. Interaction scores; Dw-Late; F (2.833, 7054) = 46.86. Up-Late; F (2.94, 5442) = 14.87. Stable; F (2.91, 5702) = 21.5. Reactivation; F (2.86, 11188) = 44.94. All Ps < 0.0001. Bonferroni’s multiple comparisons test, ***P < 0.0001.
(d-e) Characterization of motif enrichment on interacting enhancers, in each cluster. ChromHMM was used to build the emission states using the HOEMR motif database (Egr1, Ap1, Slug, Plagl1, Hic2, Rest, CTCF, Yy1, Mecp2) and previously published ChIP-seq data (Ap1-Junb12, CTCF47, Mecp248). Four states were identified as i) control – random genome sampling, ii) strong repressors iii) bivalent iv) strong activator. Fold enrichment scale (FE)
0.15–20.
