Search

Additional file 1: Figure S1. Normalization of RNA sequencing data. Distributional differences in GC content and variability in sequencing depth are sources of technical variability in RNA Sequencing data. GC content distributions (a) before normalization, (b) after full quantile GC content normalization, and (c) upper quartile sequencing depth normalization. Relative log expression (RLE) plots (d) before normalization, (e) after full quantile GC content normalization, and (f) upper quartile sequencing depth normalization. Principal component analysis (PCA) plots (g) before normalization, (h) after full quantile GC content normalization, and (i) upper quartile sequencing depth normalization. Figure S2. Validation of negative controls. From an independent cohort of mice (n = 6 in each group), RT-qPCR was used to validate the findings of negative control genes that showed no differential expression in the RNA sequencing results. (a) Lama5 (P-value = 0.9393), (b) Fzd5 (P-value = 0.1573), and (c) Trpm3 (P-value = 0.5076). Data are presented as mean ± SEM and normalized against two housekeeping genes (Tubulin and Hprt). Comparisons are evaluated using an unpaired t-test and n.s. denotes non-significant differences (P > 0.05).

Additional file 1: Fig. S1. CBPKIX/KIX mice show normal learning, reduced swim speed and impaired long-term memory. a CBPKIX/KIX mice show similar performance during trial 1 across training days 1–4 but shows significantly higher escape latency on day 5. 2-Way ANOVA: significant main effect of training days: F (3.318, 99.55) = 4.538, p = 0.0038, significant main effect of genotype: F (1, 30) = 9.507, p = 0.0044. Sidak’s multiple comparisons test, comparing CBPKIX/KIX vs WT on Day 5: adjusted p = 0.0120. b CBPKIX/KIX mice showed lower swim speed during the training days (acquisition). Significant main effect of genotype: F (1, 30) = 15.33, p = 0.0005. Sidak’s multiple comparisons test revealed significant decrease in swim speed (CBPKIX/KIX vs WT mice) on day 4 (p = 0.0067) and day 5 (p = 0.0417). c Male and female CBPKIX/KIX mice showed similar performances during the 1 h probe test (short-term memory). d Male and female CBPKIX/KIX mice showed similar performances in the 24 h probes test (long-term memory). Mixed-effects analysis: Significant main effect of Genotype F (1, 12) = 11.36, p = 0.0056. No significant main effect of sex F (1, 12) = 0.3330, p = 0.5746. Fig. S2. Learning-induced CREB phosphorylation at S133 is decreased in CBPKIX/KIX mice. (a) Scheme of the experiment. (b) CREB phosphorylation at S133 is significantly increased after MWM training ([Unpaired t-test: t(11) = 2.407, p = 0.0348, WT HC Vs WT MWM], while no such enhancement were observed in CBPKIX/KIX mice [Unpaired t-test: t(7) = 0.7450, p = 0.4805]. (c) Western blot showing CREB and p-CREB expression. Fig. S3. RRHO analysis. a Rank-rank hypergeometric overlap (RRHO) analysis comparing differential expression results between the effect of the KIX genotype after learning (X-axis) to the effect of learning (Y-axis). Although a threshold of FDR 0.99). As experimental batch is directly accounted for in the differential expression model, we excluded it here and considered PC 2–4 for visualization and the assessment of RUV normalization. Fig. S11. RUV normalization for analysis of homecage and trained wildtype mice RNAseq experiments. Left. Without applying RUV normalization, uncorrected P-values from the differential expression analysis are uniformly distributed and few genes are found to be statistically significant at false discovery rate (FDR)

Additional file 1: Figure S1. Genes regulated by sleep deprivation in the transcriptome. Arc and Hspb1 showed significantly increased expression in hippocampus following acute sleep deprivation in the transcriptome from the hippocampus, while Arrdc4 was unchanged following sleep deprivation in the transcriptome. Unpaired t-test, Arc: p = 0.0010, Hspb1: p = 0.0023 and Arrdc4: p = 0.7291.

Individuals with bipolar disorder are at increased risk for suicide, and this can be influenced by a range of biological, clinical, and environmental risk factors. Biological components associated with suicide include DNA modifications that lead to changes in gene expression. Common genetic variation and DNA methylation changes are some of the most frequent types of DNA findings associated with an increased risk for suicidal behavior. Importantly, the interplay between genetic predisposition and DNA methylation patterns is becoming more prevalent in genetic studies. We hypothesized that DNA methylation patterns in specific loci already genetically associated with suicide would be altered in individuals with bipolar disorder and a history of suicide attempt. To test this hypothesis, we searched the literature to identify common genetic variants (N=34) previously associated with suicidal thoughts and behaviors in individuals with bipolar disorder. We then created a customized sequencing panel that covered our chosen genomic loci. We profiled DNA methylation patterns from blood samples collected from bipolar disorder participants with suicidal behavior (N=55) and without suicidal behavior (N=51). We identified seven differentially methylated CpG sites and five differentially methylated regions between the two groups. Additionally, we found that DNA methylation changes in MIF and CACNA1C were associated with lethality or number of suicide attempts. Finally, we identified three meQTLs in SIRT1 , IMPA2 , and INPP1 . This study illustrates that DNA methylation is altered in individuals with bipolar disorder and a history of suicide attempts in regions known to harbor suicide-related variants.

Suicide attempt (SA) risk is elevated in individuals with bipolar disorder (BD), and DNA methylation patterns may serve as possible biomarkers of SA. We conducted epigenome-wide association studies (EWAS) of blood DNA methylation associated with BD and SA. DNA methylation was measured at > 700,000 positions in a discovery cohort of n = 84 adults with BD with a history of SA (BD/SA), n = 79 adults with BD without history of SA (BD/non-SA), and n = 76 non-psychiatric controls (CON). EWAS revealed six differentially methylated positions (DMPs) and seven differentially methylated regions (DMRs) between BD/SA and BD/non-SA, with multiple immune-related genes implicated. There were no epigenome-wide significant differences when BD/SA and BD/non-SA were each compared to CON, and patterns suggested that epigenetics differentiating BD/SA from BD/non-SA do not differentiate BD/non-SA from CON. Weighted gene co-methylation network analysis and trait enrichment analysis of the BD/SA vs. BD/non-SA contrast further corroborated immune system involvement, while gene ontology analysis implicated calcium signalling. In an independent replication cohort of n = 48 BD/SA and n = 47 BD/non-SA, fold-changes at the discovery cohort's significant sites showed moderate correlation across cohorts and agreement on direction. In both cohorts, classification accuracy for SA history among individuals with BD was highest when methylation at the significant CpG sites as well as information from clinical interviews were combined, with an AUC of 88.8% (CI = 83.8-93.8%) and 82.1% (CI = 73.6-90.5%) for the combined epigenetic-clinical predictor in the discovery and replication cohorts, respectively. Our results provide novel insight to the role of immune system functioning in SA and BD and also suggest that integrating information from multiple levels of analysis holds promise to improve risk assessment for SA in adults with BD.

Neurodevelopmental disorders (NDDs) are polygenic in nature and copy number variants (CNVs) are ideal candidates to study the nature of this polygenic risk. The disruption of striatal circuits is considered a central mechanism in NDDs. The 16p11.2 hemi-deletion (16p11.2 del) is one of the most common CNVs associated with NDD, and 16p11.2 del/+ mice show sex-specific striatum-related behavioral phenotypes. However, the critical genes among the 27 genes in the 16p11.2 region that underlie these phenotypes remain unknown. Previously, we applied a novel strategy to identify candidate genes associated with the sex-specific phenotypes of 16p11.2 del/+ mice and identified 3 genes of particular importance within the deleted region: thousand and one amino acid protein kinase 2 ( Taok2 ), seizure-related 6 homolog-like 2 ( Sez6l2 ), and major vault protein ( Mvp ). Using the CRISPR/Cas9 technique, we generated 3 gene hemi-deletion (3g del/+) mice carrying null mutations in Taok2, Sez6l2 , and Mvp . We assessed striatum-dependent phenotypes of these 3g del/+ mice in behavioral, molecular, and imaging studies. Hemi-deletion of Taok2, Sez6l2 , and Mvp induces sex-specific behavioral alterations in striatum-dependent behavioral tasks, specifically male-specific hyperactivity and impaired motivation for reward seeking, resembling behavioral phenotypes of 16p11.2 del/+ mice. Moreover, RNAseq analysis revealed that 3g del/+ mice exhibit gene expression changes in the striatum similar to 16p11.2 del/+ mice, but only in males. Pathway analysis identified ribosomal dysfunction and translation dysregulation as molecular mechanisms underlying male-specific, striatum-dependent behavioral alterations. Together, the mutation of 3 genes within the 16p11.2 region phenocopies striatal sex-specific phenotypes of 16p11.2 del/+ mice, unlike single gene mutation studies. These results support the importance of a polygenic approach to study NDDs and our novel strategy to identify genes of interest using gene expression patterns in brain regions, such as the striatum, which are impacted in these disorders.