Part 3:Activation Of A Hippocampal CREB-pCREB-miRNAMEF2 Axis Modulates Individual Variation Of Spatial Learning And Memory Capability
Mar 18, 2022
Contact: joanna.jia@wecistanche.com / WhatsApp: 008618081934791
Uniquely, miR-466f-3p appears to be a positive regulator of spatial learning and memory (Figures 1 and 3). It is now well established that biogenesis, activity, and degradation of specific miRNAs are involved in regulating neuronal plasticity responsible for learning and long-term memory formation (McNeill and Van Vactor, 2012), and misexpression of some of them are associated with neurological disorders (Issler and Chen, 2015; Salta and De Strooper, 2017). For example, in the invertebrate Aplysia California, miR-124 regulates serotonin-mediated synaptic plasticity through the regulation of CREB (Rajasethupathy et al., 2009). A role for miRNAs in stress-associated, amygdala-dependent
Figure 6. Stochastic phosphorylation of hippocampal CREB and transcriptional activation of the miR-466-669 cluster
(A) Gene maps of mSfmbt2 and the miR-466-669 cluster. The miRNA-precursor-encoding sequences of the miR-466-669 cluster located in intron 10 of the mSfmbt2 gene are shown as gray boxes. The first nucleotide of the most 50 miRNA precursor (pre-mir-466m) is denoted +1. Locations of different parts of the primary transcript of miR-466-669 cluster (A–H) exhibiting positive (+) RT-qPCR signals are indicated by gray bars. Part I showing no () RT-qPCR signal is indicated by the blank bar. TSS, putative transcriptional start site of the miR-466-669 cluster.
(B) Relative hippocampal expression levels of mSfmbt2 mRNA of GLN mice, which exhibit high miR-466f-3p levels, in comparison to PLN mice (n = 7 per group). The Ct values of mSfmbt2 are ~29–32.
(C) Relative hippocampal expression levels of the primary transcript of miR-466-669 cluster (parts B and G) of GLN mice in comparison to PLN mice (n = 10 per group).
(D) Western blotting analysis of phospho-CREB (pCREB), total CREB (tCREB), and b-actin expression in the hippocampus of GLN, PLN, and HC mice. Representative blots are shown (left), and the right histogram shows the relative pCREB/tCREB ratio after normalization to b-actin (n = 16 per group). (E) Pearson correlation scatterplots show correlations between the hippocampal expression levels of miR-466-669 cluster primary transcript and pCREB/tCREB protein of individual GLN (n = 18, R = 0.52, *p = 0.02, dots) and PLN mice (n = 9, R = 0.71, *p = 0.03, squares, respectively). The average level for HC mice (n = 9) was set as 1.
(F) Western blotting analysis of pCREB, tCREB, and b-actin expression in DIV14 primary hippocampal neurons upon chemically-induced LTP (by forskolin) and chemically-inhibited CREB phosphorylation (by 666-15). Neurons were treated with 1 nM or 2 nM of 666-15 for 1 h and then treated with forskolin for 2 h. The histogram shows relative pCREB/tCREB ratios.
(G and H) Comparison of the expression levels of miR-466f-3p (G) and miR-466-669 cluster primary transcript (H) in DIV14 primary hippocampal neurons under 666-15 and forskolin treatment as described in (F). Nurr1 and homer1a mRNAs are positive controls. The RT-qPCR signals of parts B and G indicated in (A) above were used to represent the miR-466-669 cluster primary transcript.
Data shown in (B)–(D) are presented as mean ± SEM, and data from three independent sets of experiments (n = 6 per group) shown in (F) and (G) are presented as mean ± SD. Statistical significance was assessed by unpaired t-test (B and C), one-ANOVA with Tukey’s post hoc test (D, F, and G), or two-ANOVA with Tukey’s post hoc test (H). Statistical differences: *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.
fear learning and extinction has been clearly demonstrated (Ronovsky et al., 2019; Sillivan et al., 2020). Also, the NOR test increases miR-183/96/182 expression in the hippocampus (Wol- demichael et al., 2016). Like miR-124, brain-specific miR-134 negatively regulates fear memory formation and LTP induction in rodent hippocampal CA1 region through translational repression of LimK1 mRNA (Gao et al., 2010). With respect to spatial and object recognition memory, miR-132 is inducible by neuronal-activity-dependent modulation of CREB (Hansen et al., 2016). Similar to miR-132, we have found that neuronal activity induces miR-466f-3p through transcriptional activation of CREB (Figures 6F–6H). However, unlike miR-466f-3p, miR-132 is induced in mice with either better or poor performance in the MWMtask (Figures 1BandS1A), likely due to miR-132 also being inducible by stress (Shaltiel et al., 2013), as would be the case during the long-lastingandstressfulMWMtask.Another possible reason why the ratios detected are indistinguishable in the GLN and PLN groups is a limitation of the detection method we used, with high basal levels of miR-132, as well as ERK, impeding our ability to detect fold changes in the sparse engrams of hippocampal lysates. Although miRNAs in miR-466-669 cluster bear high degrees of sequence similarity, only some members are induced during MWM training (Figure 1B), possibly due to differential transcriptional regulation and/or post-transcriptional regulation during miRNA biogenesis (Michlewski and Ca´ ceres, 2019; Siomi and Siomi, 2010).
In contrast to these other miRNAs, miR-466f-3p emerged in our study as a positive regulator of neuronal plasticity via a CREB-pCREB-miR-466f-3p-MEF2A axis (Figures 5 and 6). The MWM task is known to stimulate CREB phosphorylation (Porte et al., 2008). pCREB positively regulates neuronal plasticity, as well as memory allocation and consolidation, mainly by transcriptional activation of various genomic loci/genes (Lisman et al., 2018). Our in vitro data show that activation of CREB via phosphorylation is required for miR-466f-3p expression (Figures 6F and 6G). Significantly, parallel to increased hippocampal levels of miR-466f-3p (Figure 1B), we found that CREB was activated by phosphorylation of GLN, but not PLN, mice (Figure 6D; see below). Moreover, our combinatorial approach of using lentiviral-mediated overexpression and sponge inhibition demonstrates that miR466-3p induction is a primary cause of better spatial learning and memory capability (Figure 3C). Correlated with the results of the MWM task, mice overexpressing miR-466f- 3p in their hippocampus exhibited stronger LTP, as evidenced by the increase in fEPSPs relative to control or miR-sponge-vi- rus-infected mice (Figure 4B).
Mechanistically, miR-466f-3p represses translation of Mef2a mRNA, thereby reducing levels of MEF2A protein, a negative regulator of learning-induced dendritic spine growth and spatial memory formation (Cole et al., 2012; Flavell et al., 2006), in the hippocampus of GLN mice (Figures 5D, 5F, and 5G). MEF2A/ 2D has been reported to inhibit the induction of excitatory dendritic synapses (Flavell et al., 2006). Although both of these previous studies, mirroring our study herein using miR-466f-3p overexpression or miR-sponge-based inhibition approaches (Figure 2A), examined the dendritic arborization and reported no differences between wild-type and MEF2 overexpression or knockdown subjects, neither study analyzed the effect on dendritic length. It should be mentioned here that although the 30 UTR of Mef2d mRNA also harbors a predictive binding site for miR-466f-3p, miR-466-f-3p overexpression did not affect the re- porter activity of a Mef2d 30 UTR-carried plasmid (data not shown). Notably, Cole et al. (2012) showed that the levels of MEF2A/D proteins are downregulated in the hippocampus of mice trained in the water maze. Further, since their trained mice with later MEF2 overexpression presented normal spatial memory, they concluded that MEF2 overexpression specifically disrupted the formation of, but not existing, spatial memory. However, their manipulation of memory was temporally limited, because the herpes simplex virus (HSV) vector used for transgene expression typically peaked at 2–4 days after microinjection and dissipated 8–12 days after microinjection (Cole et al., 2012). On the other hand, we have used lentivirus, the DNA of which would be integrated into host chromosomes, conferring permanent infection enabling a long-lasting manipulation of learning/memory (Figure 3). Finally, a small proportion (25%) of GLN mice did not exhibit miR-466f-3p induction during the MWM task (Figure 1C), suggesting that other factors and/or pathways may contribute to the spatial learning and memory capability of these GLN mice. We have assessed miR-335-5p and Sgk mRNA, both of which are differentially expressed during spatial learning and memory formation (Capitano et al., 2017; Tsai et al., 2002). However, in contrast to rats or CD1 outbred mice, we did not observe any differences in the hippocampal levels of miR-335-5p or Sgk mRNA of GLN mice relative to PLN mice (Figures S1A and S5). Thus, stochastic activation of the CREB- pCREB-miR-466f-3p-MEF2A axis appears to be the major cause underlying individual variation of the spatial learning and memory capability of our inbred C57BL/6J mice.
Stochastic gene expression among genetically identical cells, operating at the level of transcription, translation, or post-translational modification, has been studied intensively (Eling et al., 2019; Reinius and Sandberg, 2015). This stochasticity underlies cell-to-cell variability in cellular functions and the consequent diversity of phenotypic characteristics manifested in the same microenvironment in response to environmental stimuli during differentiation/development (Eling et al., 2019). Two well-studied examples of such stochasticity in gene expression at the cellular level, are olfactory receptor promoter and specific Pcdh promoter choice in the olfactory gene cluster of individual mammalian olfactory sensory neurons, which are both activated upon epigenetic switch (Magklara and Lomvardas, 2013). The stochastic and irreversible decision on Pcdh promoter use results from a combination of copy number variation, changes in DNA methylation, and non-coding RNA transcription (Canzio et al., 2019). In parallel, stochasticity in gene expression and remodeling of signal transduction in specific tissues, including the hippocampus, among genetically identical rodents have been observed before (Alfonso et al., 2002; I GH et al., 2014V). Our study presents one evidence showing that phenotypic variation of different individuals, specifically the variation in their spatial learning and memory capability, is modulated by stochasticity of CREB activation in the hippocampus and consequent transcriptional activation of the miR- 466-669 cluster, which leads to elevated levels of a specific miRNA (miR-466f-3p) inhibiting the expression of a memory negative regulator (MEF2A). However, it is not clear yet if other miRNAs encoded by the miR-466-669 cluster also contribute to better learning and memory capability. This phenotypic heterogeneity could be due to cellular heterogeneity in the hippocampus, which may lead to variations in activity-induced engram gene expression (Jaeger et al., 2018; Rao-Ruiz et al., 2019).
At this moment, when and how the stochasticity of hippocampal CREB activation upon specific neuronal stimuli is determined are unknown. It is likely that there is a locus-specific mechanism permitting the stochastically phosphorylated CREB to activate the promoter of the miR-466-669 cluster. However, currently, there is no available public database on the transcriptional start site (TSS) of this miRNA cluster, and there is only one potential CREB-binding motif located 5 kb upstream of the putative TSS of the miR-466-669 cluster. Whether pCREB activates this miRNA cluster directly or indirectly is unknown at the moment, so are the underlying mechanisms. Notably, the miR-466-669 cluster only exists in rodents. However, the human miRNAs has- miR-466 and hsa-miR-3941 have similar seed sequences as mouse mmu-miR-466f-3p and are capable of base-paring with the 30 UTR of the human MEF2A mRNA, as predicted by miRWalk 2.0 (Sticht et al., 2018). Furthermore, another known human miRNA (hsa-miR-1) has also been shown to negatively regulate the expression of MEF2A (Ikeda et al., 2009). Thus, the stochastic activation of CREB-pCREB-miR-466f-3p-MEF2Aaxisuncovered in this study represent sageneral mechanism for the generation of within-species variation of the spatial learning and memory capability among different individuals that could be evolutionarily beneficial for natural selection.
STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:
d KEY RESOURCES TABLE
d RESOURCE AVAILABILITY
B Lead contact
B Materials availability
B Data and code availability
d EXPERIMENTAL MODEL AND SUBJECT DETAILS
B Animals
B Cell cultures
d METHOD DETAILS
B Morris water maze task
B Novel object recognition test (NOR)
B Barnes maze (BM) task
B miRNA microarray hybridization and RT-qPCR analysis
B Plasmid construction
B Cell transfection and chemical treatment B miRNA in situ hybridization (ISH)
B Protein lysate preparation, western blotting, Immuno-fluorescence staining, and imaging analysis
B Golgi staining
B Recombinant lentivirus infection of mouse primary hippocampal neurons
B Recombinant lentivirus injection of mouse hippocampus
B Whole-cell patch-clamp recording
B Electrophysiology
B Luciferase reporter assay d QUANTIFICATION AND STATISTICAL ANALYSIS
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j. celrep.2021.109477.
ACKNOWLEDGMENTS
We thank the National RNAi Core Facility at Academia Sinica for recombinant lentivirus preparation, the Neuroscience Core Facility at Academia Sinica (AS- CFII-108-106) for fEPSP recording techniques and whole-cell recording in cultured neurons, and the Institute of Molecular Biology Imaging Core and Bioinformatics Core for technical assistance. We also thank Dr. Hsien-Sung Huang (National Taiwan University) for providing the lentiviral vector pFUGW-dsRed. This research was supported by Taipei Medical University, the Frontier of Science Award (MOST 107-2321-B-001-016); grants from the Ministry of Science and Technology (MOST), Taipei, Taiwan (MOST 108- 2320-B-038-066 and MOST 109-2320-B-038-071); and a Senior Investigator Award from Academia Sinica, Taipei, Taiwan.
AUTHOR CONTRIBUTIONS
I.-F.W., K.-J.T., and C.-K.J.S. designed the experiments. G.-J.H. did the Golgi staining. I.-F.W., with the help of Y.W. and Y.-H.Y., performed all other experiments. I.-F.W. did the data analysis. I.-F.W. and C.-K.J.S. wrote the manuscript.
DECLARATION OF INTERESTS
The authors declare no competing interests.
INCLUSION AND DIVERSITY
We worked to ensure sex balance in the selection of non-human subjects. We worked to ensure diversity in experimental samples through the selection of the cell lines. While citing references scientifically relevant for this work, we also actively worked to promote gender balance in our reference list.
Received: April 27, 2020
Revised: June 7, 2021
Accepted: July 13, 2021
Published: August 3, 2021
REFERENCES
Abraham, W.C., Jones, O.D., and Glanzman, D.L. (2019). Is plasticity of synapses the mechanism of long-term memory storage? NPJ Sci. Learn.4, 9. Alfonso, J., Pollevick, G.D., Castensson, A., Jazin, E., and Frasch, A.C.C. (2002). Analysis of gene expression in the rat hippocampus using Real-Time PCR reveals high inter-individual variation in mRNA expression levels. J. Neurosci. Res. 67, 225–234.
Asok, A., Leroy, F., Rayman, J.B., and Kandel, E.R. (2019). Molecular Mechanisms of the Memory Trace. Trends Neurosci. 42, 14–22.
Attar, A., Liu, T., Chan, W.T., Hayes, J., Nejad, M., Lei, K., and Bitan, G. (2013). A shortened Barnes maze protocol reveals memory deficits at 4-months of age in the triple-transgenic mouse model of Alzheimer’s disease. PLoS ONE8, e80355. Bale, T.L. (2015). Epigenetic and transgenerational reprogramming of brain development. Nat. Rev. Neurosci. 16, 332–344. Belfield, J.L., Whittaker, C., Cader, M.Z., and Chawla, S. (2006). Differential effects of Ca2+ and cAMP on transcription mediated by MEF2D and cAMP-response element-binding protein in hippocampal neurons. J. Biol. Chem. 281, 27724–27732.
Humeau, Y., and Choquet, D. (2019). The next generation of approaches to investigate the link between synaptic plasticity and learning. Nat. Neurosci.
Bendesky, A., and Bargmann, C.I. (2011). Genetic contributions to behavioral diversity at the gene-environment interface. Nat. Rev. Genet. 12, 809–820.
Bridi, M.S., Hawk, J.D., Chatterjee, S., Safe, S., and Abel, T. (2017). Pharmacological Activators of the NR4A Nuclear Receptors Enhance LTP in a CREB/ CBP-Dependent Manner. Neuropsychopharmacology 42, 1243–1253.
Canzio, D., Nwakeze, C.L., Horta, A., Rajkumar, S.M., Coffey, E.L., Duffy, E.E., Duffie´, R., Monahan, K., O’Keeffe, S., Simon, M.D., et al. (2019). Antisense lncRNA Transcription Mediates DNA Demethylation to Drive Stochastic Proto- cadherin a Promoter Choice. Cell 177, 639–653.e15.
Capitano, F., Camon, J., Licursi, V., Ferretti, V., Maggi, L., Scianni, M., Del Vec- chio, G., Rinaldi, A., Mannironi, C., Limatola, C., et al. (2017). MicroRNA-335- 5p modulates spatial memory and hippocampal synaptic plasticity. Neurobiol. Learn. Mem. 139, 63–68.
Casellas, J. (2011). Inbred mouse strains and genetic stability: a review. Animal 5, 1–7.
Chen, Y.K., and Hsueh, Y.P. (2012). Cortactin-binding protein 2 modulates the mobility of cortactin and regulates dendritic spine formation and maintenance. J. Neurosci. 32, 1043–1055.
Chen, Y.L., and Shen, C.K. (2013). Modulation of mGluR-dependent MAP1B translation and AMPA receptor endocytosis by microRNA miR-146a-5p. J. Neurosci. 33, 9013–9020.
Chu, J.F., Majumder, P., Chatterjee, B., Huang, S.L., and Shen, C.J. (2019). TDP-43 Regulates Coupled Dendritic mRNA Transport-Translation Processes in Co-operation with FMRP and Staufen1. Cell Rep. 29, 3118–3133.e6.
Cohen, J.E., Lee, P.R., Chen, S., Li, W., and Fields, R.D. (2011). MicroRNA regulation of homeostatic synaptic plasticity. Proc. Natl. Acad. Sci. USA 108, 11650–11655.
Cole, C.J., Mercaldo, V., Restivo, L., Yiu, A.P., Sekeres, M.J., Han, J.H., Vetere, G., Pekar, T., Ross, P.J., Neve, R.L., et al. (2012). MEF2 negatively regulates learning-induced structural plasticity and memory formation. Nat. Neu- rosci. 15, 1255–1264.
Danchin, E´ ., Charmantier, A., Champagne, F.A., Mesoudi, A., Pujol, B., and Blanchet, S. (2011). Beyond DNA: integrating inclusive inheritance into an extended theory of evolution. Nat. Rev. Genet. 12, 475–486.
Daugaard, I., and Hansen, T.B. (2017). Biogenesis and Function of Ago-Associated RNAs. Trends Genet. 33, 208–219.
Ekstrom, A.D., Arnold, A.E., and Iaria, G. (2014). A critical review of the allocentric spatial representation and its neural underpinnings: toward a network-based perspective. Front. Hum. Neurosci. 8, 803.
Eling, N., Morgan, M.D., and Marioni, J.C. (2019). Challenges in measuring and understanding biological noise. Nat. Rev. Genet. 20, 536–548.
Flavell, S.W., Cowan, C.W., Kim, T.K., Greer, P.L., Lin, Y., Paradis, S., Griffith, E.C., Hu, L.S., Chen, C., and Greenberg, M.E. (2006). Activity-dependent regulation of MEF2 transcription factors suppresses excitatory synapse number. Science 311, 1008–1012.
Gao, J., Wang, W.Y., Mao, Y.W., Grff, J., Guan, J.S., Pan, L., Mak, G., Kim, D., Su, S.C., and Tsai, L.H. (2010). A novel pathway regulates memory and plasticity via SIRT1 and miR-134. Nature 466, 1105–1109.
Hansen, K.F., Sakamoto, K., Aten, S., Snider, K.H., Loeser, J., Hesse, A.M., Page, C.E., Pelz, C., Arthur, J.S., Impey, S., and Obrietan, K. (2016). Targeted deletion of miR-132/-212 impairs memory and alters the hippocampal transcriptome. Learn. Mem. 23, 61–71.
Holmes, J.R., and Berkowitz, A. (2014). Dendritic orientation and branching distinguish a class of multifunctional turtle spinal interneurons. Front. Neural Circuits 8, 136.
Huang, G.J., Ben-David, E., Tort Piella, A., Edwards, A., Flint, J., and Shifman, S. (2012). Neurogenomic evidence for a shared mechanism of the antidepressant effects of exercise and chronic fluoxetine in mice. PLoS ONE 7, e35901.
Ikeda, S., He, A., Kong, S.W., Lu, J., Bejar, R., Bodyak, N., Lee, K.H., Ma, Q., Kang, P.M., Golub, T.R., and Pu, W.T. (2009). MicroRNA-1 negatively regulates the expression of the hypertrophy-associated calmodulin and Mef2a genes. Mol. Cell. Biol. 29, 2193–2204.
Inoue, K., Hirose, M., Inoue, H., Hatanaka, Y., Honda, A., Hasegawa, A., Mo- chida, K., and Ogura, A. (2017). The Rodent-Specific MicroRNA Cluster within the Sfmbt2 Gene Is Imprinted and Essential for Placental Development. Cell Rep. 19, 949–956.
Issler, O., and Chen, A. (2015). Determining the role of microRNAs in psychiatric disorders. Nat. Rev. Neurosci. 16, 201–212.
Jaeger, B.N., Linker, S.B., Parylak, S.L., Barron, J.J., Gallina, I.S., Saavedra, C.D., Fitzpatrick, C., Lim, C.K., Schafer, S.T., Lacar, B., et al. (2018). A novel environment-evoked transcriptional signature predicts reactivity in single dentate granule neurons. Nat. Commun. 9, 3084.
Jensen, P., Myhre, C.L., Lassen, P.S., Metaxas, A., Khan, A.M., Lambertsen, K.L., Babcock, A.A., Finsen, B., Larsen, M.R., and Kempf, S.J. (2017). TNFa affects CREB-mediated neuroprotective signaling pathways of synaptic plasticity in neurons as revealed by proteomics and phospho-proteomics. Once- target 8, 60223–60242.
Kandel, E.R. (2012). The molecular biology of memory: cAMP, PKA, CRE, CREB-1, CREB-2, and CPEB. Mol. Brain 5, 14.
Kluiver, J., Gibcus, J.H., Hettinga, C., Adema, A., Richter, M.K., Halsema, N., Slezak-Prochazka, I., Ding, Y., Kroesen, B.J., and van den Berg, A. (2012). Rapid generation of microRNA sponges for microRNA inhibition. PLoS ONE 7, e29275.
Lee, M.C., Yu, W.C., Shih, Y.H., Chen, C.Y., Guo, Z.H., Huang, S.J., Chan, J.C.C., and Chen, Y.R. (2018). Zinc ion rapidly induces toxic, off-pathway amyloid-b oligomers distinct from amyloid-b derived diffusible ligands in Alzheimer’s disease. Sci. Rep. 8, 4772.
Leger, M., Quiedeville, A., Bouet, V., Haelewyn, B., Boulouard, M., Schumann- Bard, P., and Freret, T. (2013). Object recognition test in mice. Nat. Protoc.8, 2531–2537.
Lisman, J., Cooper, K., Sehgal, M., and Silva, A.J. (2018). Memory formation depends on both synapse-specific modifications of synaptic strength and cell-specific increases in excitability. Nat. Neurosci. 21, 309–314.
Locke, M.E., Milojevic, M., Eitutis, S.T., Patel, N., Wishart, A.E., Daley, M., and Hill, K.A. (2015). Genomic copy number variation in Mus musculus. BMC Genomics 16, 497.
Lois, C., Hong, E.J., Pease, S., Brown, E.J., and Baltimore, D. (2002). Germline transmission and tissue-specific expression of transgenes delivered by lentiviral vectors. Science 295, 868–872.
Loos, M., Koopmans, B., Aarts, E., Maroteaux, G., van der Sluis, S., Verhage, M., and Smit, A.B.; Neuro-BSIK Mouse Phenomics Consortium (2015). Within-strain variation in behavior differs consistently between common inbred strains of mice. Mamm. Genome 26, 348–354.
Lorsch, Z.S., Hamilton, P.J., Ramakrishnan, A., Parise, E.M., Salery, M., Wright, W.J., Lepack, A.E., Mews, P., Issler, O., McKenzie, A., et al. (2019). Stress resilience is promoted by a Zfp189-driven transcriptional network in the prefrontal cortex. Nat. Neurosci. 22, 1413–1423.
Magklara, A., and Lomvardas, S. (2013). Stochastic gene expression in mammals: lessons from olfaction. Trends Cell Biol. 23, 449–456.
Malhotra, S.S., Suman, P., and Gupta, S.K. (2015). Alpha or beta-human chorionic gonadotropin knockdown decrease BeWo cell fusion by down-regulating PKA and CREB activation. Sci. Rep. 5, 11210.
McNeill, E., and Van Vactor, D. (2012). MicroRNAs shape the neuronal landscape. Neuron 75, 363–379.
Michlewski, G., and Ca´ ceres, J.F. (2019). Post-transcriptional control of miRNA biogenesis. RNA 25, 1–16.
Oey, H., Isbel, L., Hickey, P., Ebaid, B., and Whitelaw, E. (2015). Genetic and epigenetic variation among inbred mouse littermates: identification of inter-individual differentially methylated regions. Epigenetics Chromatin8, 54.
Siegel, G., Obernosterer, G., Fiore, R., Oehmen, M., Bicker, S., Christensen, M., Khudayberdiev, S., Leuschner, P.F., Busch, C.J., Kane, C., et al. (2009). A functional screen implicates microRNA-138-dependent regulation of the de-
Pavlicev, M., Cheverud, J.M., and Wagner, G.P. (2011). Evolution of adaptive phenotypic variation patterns by direct selection for evolvability. Proc. Biol.
Sci. 278, 1903–1912. Pedersen, C.A., Vadlamudi, S., Boccia, M.L., and Moy, S.S. (2011). Variations in Maternal Behavior in C57BL/6J Mice: Behavioral Comparisons between Adult Offspring of High and Low Pup-Licking Mothers. Front. Psychiatry2,42. Pitts, M.W. (2018). Barnes Maze Procedure for Spatial Learning and Memory in Mice. Biol. Protoc. 8, e2774. Porte, Y., Buhot, M.C., and Mons, N.E. (2008). Spatial memory in the Morris water maze and activation of cyclic AMP response element-binding (CREB) protein within the mouse hippocampus. Learn. Mem. 15, 885–894. Rajasethupathy, P., Fiumara, F., Sheridan, R., Betel, D., Puthanveettil, S.V., Russo, J.J., Sander, C., Tuschl, T., and Kandel, E. (2009). Characterization of small RNAs in Aplysia reveals a role for miR-124 in constraining synaptic plasticity through CREB. Neuron 63, 803–817. Rao-Ruiz, P., Couey, J.J., Marcelo, I.M., Bouwkamp, C.G., Slump, D.E., Ma- tos, M.R., van der Loo, R.J., Martins, G.J., van den Hout, M., van IJcken, W.F., et al. (2019). Engram-specific transcriptome profiling of contextual memory consolidation. Nat. Commun. 10, 2232. Reinius, B., and Sandberg, R. (2015). Random monoallelic expression of auto- somal genes: stochastic transcription and allele-level regulation. Nat. Rev. Genet. 16, 653–664. Rogerson, T., Cai, D.J., Frank, A., Sano, Y., Shobe, J., Lopez-Aranda, M.F., and Silva, A.J. (2014). Synaptic tagging during memory allocation. Nat. Rev. Neurosci. 15, 157–169. Ronovsky, M., Zambon, A., Cicvaric, A., Boehm, V., Hoesel, B., Moser, B.A., Yang, J., Schmid, J.A., Haubensak, W.E., Monje, F.J., and Pollak, D.D. (2019). A role for miR-132 in learned safety. Sci. Rep. 9, 528. Salta, E., and De Strooper, B. (2017). Noncoding RNAs in neurodegeneration. Nat. Rev. Neurosci. 18, 627–640. Sastry, L., Johnson, T., Hobson, M.J., Smucker, B., and Cornetta, K. (2002). Titering lentiviral vectors: comparison of DNA, RNA, and marker expression methods. Gene Ther. 9, 1155–1162. Schneider, A., Hommel, G., and Blettner, M. (2010). Linear regression analysis: part 14 of a series on evaluation of scientific publications. Dtsch. Arztebl. Int. 107, 776–782. Schneider, C.A., Rasband, W.S., and Eliceiri, K.W. (2012). NIH Image to Im- ageJ: 25 years of image analysis. Nat. Methods 9, 671–675. Shaltiel, G., Hanan, M., Wolf, Y., Barbash, S., Kovalev, E., Shoham, S., and Soreq, H. (2013). Hippocampal microRNA-132 mediates stress-inducible cognitive deficits through its acetylcholinesterase target. Brain Struct. Funct. 218, 59–72.
Sillivan, S.E., Jamieson, S., de Nijs, L., Jones, M., Snijders, C., Klengel, T., Jo- seph, N.F., Krauskopf, J., Kleinjans, J., Vinkers, C.H., et al. (2020). MicroRNA regulation of persistent stress-enhanced memory. Mol. Psychiatry25, 965–976.
Siomi, H., and Siomi, M.C. (2010). Posttranscriptional regulation of microRNA biogenesis in animals. Mol. Cell 38, 323–332.
Sticht, C., De La Torre, C., Parveen, A., and Gretz, N. (2018). miRWalk: An online resource for prediction of microRNA binding sites. PLoS ONE13, e0206239.
Thomas, K.T., Anderson, B.R., Shah, N., Zimmer, S.E., Hawkins, D., Valdez, A.N., Gu, Q., and Bassell, G.J. (2017). Inhibition of the Schizophrenia-Associated MicroRNA miR-137 Disrupts Nrg1a Neurodevelopmental Signal Transduction. Cell Rep. 20, 1–12.
Tsai, K.J., Chen, S.K., Ma, Y.L., Hsu, W.L., and Lee, E.H. (2002). sgk, a primary glucocorticoid-induced gene, facilitates memory consolidation of spatial learning in rats. Proc. Natl. Acad. Sci. USA 99, 3990–3995.
Vedell, P.T., Svenson, K.L., and Churchill, G.A. (2011). Stochastic variation of transcript abundance in C57BL/6J mice. BMC Genomics 12, 167.
e´gh, M.J., Rausell, A., Loos, M., Heldring, C.M., Jurkowski, W., van Nierop,V P., Paliukhovich, I., Li, K.W., del Sol, A., Smit, A.B., et al. (2014). Hippocampal extracellular matrix levels and stochasticity in synaptic protein expression increase with age and are associated with age-dependent cognitive decline. Mol. Cell. Proteomics 13, 2975–2985.
Vorhees, C.V., and Williams, M.T. (2014). Assessing spatial learning and memory in rodents. ILAR J. 55, 310–332.
Wang, I.F., Guo, B.S., Liu, Y.C., Wu, C.C., Yang, C.H., Tsai, K.J., and Shen, C.K. (2012a). Autophagy activators rescue and alleviate pathogenesis of a mouse model with proteinopathies of the TAR DNA-binding protein 43. Proc. Natl. Acad. Sci. USA 109, 15024–15029.
Wang, W., Kwon, E.J., and Tsai, L.H. (2012b). MicroRNAs in learning, memory, and neurological diseases. Learn. Mem. 19, 359–368.
Woldemichael, B.T., Jawaid, A., Kremer, E.A., Gaur, N., Krol, J., Marchais, A., and Mansuy, I.M. (2016). The microRNA cluster miR-183/96/182 contributes to long-term memory in a protein phosphatase 1-dependent manner. Nat. Com- mun. 7, 12594.
Xie, F., Li, B.X., Kassenbrock, A., Xue, C., Wang, X., Qian, D.Z., Sears, R.C., and Xiao, X. (2015). Identification of a Potent Inhibitor of CREB-Mediated Gene Transcription with Efficacious in Vivo Anticancer Activity. J. Med. Chem. 58, 5075–5087.
