Integrative Analysis of Disease Signatures Shows Inflammation Disrupts Juvenile Experience-Dependent Cortical Plasticity

Diseases that dysregulate the juvenile plasticity signature are associated with inflammatory processes. a, b, Juvenile plasticity signature of 176 genes (represented by green bars) generated by differential expression of P29 vs >P60 C57BL/6 male mice primary visual cortex was computationally matched to 436 disease signatures (represented by orange bars; a) using rank-based molecular matching where large scores indicate shared transcriptional phenotype (b). c, DLA systematically identified processes associated to diseases that dysregulate the plasticity signature. Seven of the 14 largest associations were inflammation-related gene sets, and 7 of 7 of inflammation-related gene sets were strongly associated with plasticity (all at p_corrected < 5 × 10⁻⁵: OR = 25.8, 95% CI = 1.4–491.2, p = 2.7 × 10⁻³, Fisher’s exact test). See Figure 1-1 for source DLA data.

DLA identifies biological processes common to diseases that perturb the juvenile plasticity signature. To identify shared pathophysiology across the diverse list of diseases predicted to dysregulate the juvenile plasticity signature, we applied DLA. This approach calculates the association between diseases that dysregulate the plasticity signature genes and 50 well defined and distinct biological pathways. To do so, it computes a regression between the molecular match score (a measure that indicates the strength of association between the disease and plasticity signatures; see Table 3 and Materials and Methods) and the pathology score (a measure of activity of the biological pathway in that disease; see Materials and Methods). Large regression coefficients indicate that the biological pathway may disrupt juvenile plasticity. Using a multiple test-corrected, empirical p < 5 × 10⁻⁵, seven of the 14 largest DLA associations were inflammation-related gene sets, and every inflammation-related gene set in the hallmark library was strongly associated with diseases that dysregulate plasticity genes (seven of seven inflammation gene sets at p_corrected < 5 × 10⁻⁵: OR = 25.8, 95% CI = 1.4–491.2, p = 2.7 × 10⁻³, Fisher’s exact test). A total of 20,000 permutations of the gene sets was used to estimate p values and to normalize the regression coefficients to allow comparison between effect sizes for different biological pathways. Inflammation-related gene sets: TNF-α signaling via NF-κB, IFN-γ response, inflammatory response, complement, IL-2–Stat5 signaling, IFN-α response, IL-6–Jak–Stat3 signaling.

Diseases that dysregulate the adult Lynx1^−/− plasticity signature are associated with inflammatory processes. Using an adult Lynx1^−/− plasticity signature of 98 genes (generated by differential expression of primary visual cortex from >P60 Lynx1^−/− vs >P60 C57BL/6 male mice), DLA systematically identified biological processes associated with diseases that dysregulate the adult Lynx1^−/− plasticity signature genes. Using adult Lynx1^−/− animals controls for age, as these adult animals have elevated plasticity similar to that of juvenile animals. Seven of the 11 largest associations were inflammation-related gene sets, and every inflammation-related gene set was strongly associated with plasticity (7 of 7 inflammation gene sets at p_corrected < 5 × 10⁻⁵: OR = 63.0, 95% CI = 3.2–1229.8, p = 7.6 × 10⁻⁵, Fisher’s exact test). See Figure 2-1 for source DLA data.

DLA identifies biological processes common to diseases that perturb the Lynx1^−/− plasticity signature. To identify shared pathophysiology across the diverse list of diseases predicted to dysregulate the Lynx1^−/− plasticity signature, we applied DLA. Large regression coefficients indicate that the biological pathway may disrupt Lynx1^−/− plasticity. As with the juvenile plasticity signature, using a multiple test-corrected, empirical p < 5 × 10⁻⁵, we found that every inflammation-related gene set in the hallmark library was strongly associated with diseases that dysregulate plasticity genes (7 of 7 inflammation gene sets at p_corrected < 5 × 10⁻⁵: OR = 63.0, 95% CI = 3.2–1229.8, p = 7.6 × 10⁻⁵ Fisher’s exact test). A total of 20,000 permutations of the gene sets were used to estimate p values and to normalize the regression coefficients to allow comparison between effect sizes for different biological pathways. Inflammation-related gene sets: TNF-α signaling via NF-κB, IFN-γ response, inflammatory response, complement, IL-2–Stat5 signaling, IFN-α response, IL-6–Jak–Stat3 signaling.

LPS reverses plasticity signature gene expression. a, LPS disease signature shares plasticity signature genes in silico (molecular match rank #14, p = 7.9 × 10⁻⁴; Table 3; disease signature is from GSE3253: adult mouse whole-brain homogenate harvested 4 h after peripheral LPS injected intraperitoneally; genes with absolute value of the standardized expression (z-score) ≥2 SDs from the mean were selected as the most differentially expressed by LPS). b, The expression of the 16 genes shared between juvenile plasticity and the LPS disease signatures is anticorrelated (Spearman’s ρ = −0.77, p = 7.4 × 10⁻⁴; LPS disease signature gene expression values fell in the range [−1,+1]; for plotting purposes, the plasticity gene expression fold change was linear transformed to [−1,+1]). c, Of the 13 genes of the 16 predicted to be reversed by LPS, the majority (8 of 13; 61.5%) showed a complete reversal in their differential expression pattern in V1 after LPS administration (300 μg/kg LPS, intraperitoneally, at P26 during the peak of juvenile plasticity) relative to saline. LPS downregulated Cldn5 and Slc40a1 and upregulated Alox12b, S100a9a, Ch25h, Lrg1, S100a8, and Lcn2 (n = 5 mice per group). ***p < 0.001, **p > 0.001 and ≤0.01, *p > 0.01 and ≤0.05 (two-sided t tests of ΔCTs). Log2 fold change is −ΔΔ CT. Error bars indicate the SEM.