The schizophrenia risk gene product miR-137 alters presynaptic plasticity

Non-coding variants in the human MIR137 gene locus increase schizophrenia risk at a genome-wide significance level. However, the functional consequence of these risk alleles is unknown. Here, we examined induced human neurons harboring the minor alleles of four disease-associated single nucleotide polymorphisms (SNPs) in MIR137, and observed increased MIR137 levels compared to major allele-carrying cells. We found that miR-137 gain-of-function causes downregulation of the presynaptic target genes, Complexin-1 (Cplx1), Nsf, and Synaptotagmin-1 (Syt1), leading to impaired vesicle release. In vivo, miR-137 gain-of-function results in changes in synaptic vesicle pool distribution, impaired mossy fiber-LTP induction and deficits in hippocampus-dependent learning and memory. By sequestering endogenous miR-137, we were able to ameliorate the synaptic phenotypes. Moreover, reinstatement of Syt1 expression partially restored synaptic plasticity, demonstrating the importance of Syt1 as a miR-137 target. Our data provide new insight into the mechanism by which miR-137 dysregulation can impair synaptic plasticity in the hippocampus.

 Statistics reporting, by figure Please specify the following information for each panel reporting quantitative data, and where each item is reported (section, e.g. Results, & paragraph number).
Each figure legend should ideally contain an exact sample size (n) for each experimental group/condition, where n is an exact number and not a range, a clear definition of how n is defined (for example x cells from x slices from x animals from x litters, collected over x days), a description of the statistical test used, the results of the tests, any descriptive statistics and clearly defined error bars if applicable.
For any experiments using custom statistics, please indicate the test used and stats obtained for each experiment.
Each figure legend should include a statement of how many times the experiment shown was replicated in the lab; the details of sample collection should be sufficiently clear so that the replicability of the experiment is obvious to the reader.
For experiments reported in the text but not in the figures, please use the paragraph number instead of the figure number.
Note: Mean and standard deviation are not appropriate on small samples, and plotting independent data points is usually more informative. When technical replicates are reported, error and significance measures reflect the experimental variability and not the variability of the biological process; it is misleading not to state this clearly.
Even if no sample size calculation was performed, authors should report why the sample size is adequate to measure their effect size.
Method section, Statistical methods: Sample size. For biochemical (immunohistochemistry, western blotting) and molecular (quantitative PCR, luciferase) the minimum number of biological replicates needed for non-parametric statistical analysis is three animals per condition, per experiment. To improve our statistical power and ensure that our hypotheses are rigorously tested, we try to include 4 to 5 biological replicates in each experiment, and to replicate each experiment at least once, when possible. Behavior and in vivo experiments have a higher variability inherent to behavioral experiments. It is customary to include in > 8 animals to achieve appropriate statistical power. For behavior, these experiments are typically repeated at least once with a separate cohort of animals. The number of animals used for survival surgery is dictated by the subsequent experiments that those animals will be engaged in (behavior, tissue harvesting, both).
2. Are statistical tests justified as appropriate for every figure?
See section "Statistical methods" in the "Method" part of the manuscript. Each statistical test is included in the Figure legend. Details of post-hoc analysis result can be found in the Method part "Statistical methods" a. If there is a section summarizing the statistical methods in the methods, is the statistical test for each experiment clearly defined? Yes.
Welch two sample t-test (t), independent t-test (Ty, yuen), analysis of variance for one or more fitted model objects (F), multivariate analysis of variance (V), Wilcoxon-rank sum and signed rank tests (W) Kruskal-Wallis rank sum test (H) b. Do the data meet the assumptions of the specific statistical test you chose (e.g. normality for a parametric test)?
Where is this described (section, paragraph #)?
Yes. See section "Statistical methods" in the "Method" part of the manuscript. "Each data set was analyzed for its ability to meet the statistical assumptions for equality of the variance, for normal distribution, and for sphericity"... "The assumption of the parametric test was calculated using the Levene test" c. Is there any estimate of variance within each group of data?
Is the variance similar between groups that are being statistically compared?
Where is this described (section, paragraph #)? see above.
"If the assumption was met, the following tests were used: Welch two sample t-test, independent t-test, analysis of variance for one or more fitted model objects (F), multivariate analysis of variance (V). If the assumption was validated, the following tests were performed: Wilcoxon-rank sum and signed rank tests (W) and Kruskal-Wallis rank sum test (H)." d. Are tests specified as one-or two-sided? see above e. Are there adjustments for multiple comparisons? see above 3. Are criteria for excluding data points reported?
Was this criterion established prior to data collection?
Where is this described (section, paragraph #)?
Before data collection the following criteria were established: Experiments, in which the control failed, were excluded from the study. Animals, in which the virus expression could not be observed, were excluded. Deposition is strongly recommended for many other datasets for which structured public repositories exist; more details on our data policy are available here. We encourage the provision of other source data in supplementary information or in unstructured repositories such as Figshare and Dryad.