The myocyte enhancer factor 2 C (MEF2C) gene encodes a transcription factor important for neurogenesis and synapse development and contains common variants associated with intelligence (IQ) and educational attainment (EA). Here, we took gene expression data from the mouse cortex of a Mef2c mouse model with a heterozygous DNA binding-deficient mutation of Mef2c (Mef2c-het) and combined these data with MEF2C ChIP-seq data from cortical neurons and single-cell data from the mouse brain. This enabled us to create a set of genes that were differentially regulated in Mef2c-het mice, represented direct target genes of MEF2C and had elevated in expression in cortical neurons. We found this gene-set to be enriched for genes containing common genetic variation associated with IQ and EA. Genes within this gene-set that were down-regulated, i.e. have reduced expression in Mef2c-het mice versus controls, were specifically significantly enriched for both EA and IQ associated genes. These down-regulated genes were enriched for functionality in the adenylyl cyclase signalling system, which is known to positively regulate synaptic transmission and has been linked to learning and memory. Within the adenylyl cyclase signalling system, three genes regulated by MEF2C, CRHR1, RGS6, and GABRG3, are associated at genome-wide significant levels with IQ and/or EA. Our results indicate that genetic variation in MEF2C and its direct target genes within cortical neurons contribute to variance in cognition within the general population, and the molecular mechanisms involved include the adenylyl cyclase signalling system’s role in synaptic function.

Background: Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD). Methods: Differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA)-LUAD cohort. Cox regression analysis was performed on DEGs to identify the gene prognostics of OS. Attention-based multiple instance learning (AMIL) models were trained to predict the expression of identified prognostic genes from WSIs using the TCGA-LUAD dataset. Models were externally validated in the Clinical Proteomic Tumour Analysis Consortium (CPTAC)-LUAD dataset. The prognostic value of predicted gene expression values was then compared to the true gene expression measurements. Results: The expression of 239 prognostic genes could be predicted in TCGA-LUAD with cross-validated Pearson’s R > 0.4. Predicted gene expression demonstrated prognostic performance, attaining a cross-validated concordance index of up to 0.615 in TCGA-LUAD through Cox regression. In total, 36 genes had predicted expression in the external validation cohort that was prognostic of OS. Conclusions: Gene expression predicted from WSIs is an effective method of evaluating OS in patients with LUAD. These results may open up new avenues of cost- and time-efficient prognosis assessment in LUAD treatment.