Gene expression networks regulated by human personality.

Genome-wide association studies of human personality have been carried out, but transcription of the whole genome has not been studied in relation to personality in humans. We collected genome-wide expression profiles of adults to characterize the regulation of expression and function in genes related to human personality. We devised an innovative multi-omic approach to network analysis to identify the key control elements and interactions in multi-modular networks. We identified sets of transcribed genes that were co-expressed in specific brain regions with genes known to be associated with personality. Then we identified the minimum networks for the co-localized genes using bioinformatic resources. Subjects were 459 adults from the Young Finns Study who completed the Temperament and Character Inventory and provided peripheral blood for genomic and transcriptomic analysis. We identified an extrinsic network of 45 regulatory genes from seed genes in brain regions involved in self-regulation of emotional reactivity to extracellular stimuli (e.g., self-regulation of anxiety) and an intrinsic network of 43 regulatory genes from seed genes in brain regions involved in self-regulation of interpretations of meaning (e.g., production of concepts and language). We discovered that interactions between the two networks were coordinated by a control hub of 3 miRNAs and 3 protein-coding genes shared by both. Interactions of the control hub with proteins and ncRNAs identified more than 100 genes that overlap directly with known personality-related genes and more than another 4000 genes that interact indirectly. We conclude that the six-gene hub is the crux of an integrative network that orchestrates information-transfer throughout a multi-modular system of over 4000 genes enriched in liquid-liquid-phase-separation (LLPS)-related RNAs, diverse transcription factors, and hominid-specific miRNAs and lncRNAs. Gene expression networks associated with human personality regulate neuronal plasticity, epigenesis, and adaptive functioning by the interactions of salience and meaning in self-awareness.

miR-124-3p target genes identify globus pallidus role in suicide ideation recovery in borderline personality disorder.

Borderline personality disorder (BPD) is characterized by patterns of unstable affect, unstable interpersonal relationships, and chronic suicidal tendencies. Research on the genetics, epigenetics, and brain function of BPD is lacking. MicroRNA-124-3p (miR-124-3p) was recently identified in a Genome-Wide Association Study as likely associated with BPD. Here, we identified the anatomical brain expression of genes likely modulated by miR-124-3p and compared morphometry in those brain regions in BPD inpatients vs. controls matched for psychiatric comorbidities. We isolated lists of targets likely modulated by miR-124-3p from TargetScan (v 8.0) by their preferentially conserved targeting (Aggregate PCT > 0.99, see Supplementary Table 1). We applied Process Genes List (PGL) to identify regions of interest associated with the co-expression of miR-124-3p target genes. We compared the gray matter volume of the top region of interest co-expressing those genes between BPD inpatients (n = 111, 46% female) and psychiatric controls (n = 111, 54% female) at The Menninger Clinic in Houston, Texas. We then correlated personality measures, suicidal ideation intensity, and recovery from suicidal ideation with volumetrics. Gene targets of miR-124-3p were significantly co-expressed in the left Globus Pallidus (GP), which was smaller in BPD than in psychiatric controls. Smaller GP volume was negatively correlated with agreeableness and with recovery from suicidal ideation post-treatment. In BPD, GP volume may be reduced through miR-124-3p regulation and suppression of its target genes. Importantly, we identified that a reduction of the GP in BPD could serve as a potential biomarker for recovery from suicidal ideation.

Process genes list: An approach to link genetics and human brain imaging.

BACKGROUND: Linking human genetics and brain imaging data is extremely challenging, among other reasons because both fields suffer from multiple comparison problems. NEW METHOD: ProcessGeneLists (PGL) links genetics and human brain imaging by using genes associated with a disease and calculating a normalized mRNA expression average of those genes in each brain region. Brain regions in which those genes are most co-expressed become regions of interest (ROIs) to perform brain imaging in participants with and without the disease, decreasing multiple comparisons. Once a region is identified as "imaging-relevant", the genes most responsible for that ROI being highlighted can be genotyped in the imaged sample. This allows to re-analyze imaging data under the light of likely relevant genetics, to study possible brain imaging/gene variant interactions. RESULTS: As proof-of-concept, we created two lists of genes expressed in the habenula and the striatum, to verified that PGL would highlight those regions. Next, we used a list of genes likely important in alcohol abuse from the literature, which identified several brain regions previously associated with alcohol abuse such as the striatum, habenula, and hippocampus. COMPARISON WITH EXISTING METHODS: To our knowledge there is no current method to obtain brain regions of interest from genetics data. CONCLUSIONS: Genetics typically asks "which genes are associated with a disease?" while human brain imaging typically asks "which brain regions are associated with a disease?" PGL asks "which genes, via modulation within specific brain regions, are found to be associated with a disease?".