Hay, E. — Page 2 | bioRxiv Science

bioRxiv2019

CRISPR disruption and UK Biobank analysis of a highly conserved polymorphic enhancer suggests its role in anxiety and male alcohol intake.

Excessive alcohol intake is associated with 5.9% of global deaths. However, this figure is especially acute in men such that 7.6% of deaths can be attributed to alcohol intake. Previous studies identified a significant interaction between genotypes of the galanin (GAL) gene with anxiety and alcohol abuse in different male populations but were unable to define a mechanism. To address these issues the current study analysed the human UK Biobank cohort and identified a significant interaction (n=115,865; p=0.0007) between allelic variation (GG or CA genotypes) in the highly conserved human GAL5.1 enhancer, alcohol intake (AUDIT questionnaire scores) and anxiety in men that was consistent with these previous studies. Critically, disruption of GAL5.1 in mice using CRISPR genome editing significantly reduced GAL expression in the amygdala and hypothalamus whilst producing a corresponding reduction in ethanol intake in KO mice. Intriguingly, we also found evidence of reduced anxiety-like behaviour in male GAL5.1KO animals mirroring that seen in humans. Using bioinformatic analysis and co-transfection studies we further identified the EGR1 transcription factor, that is co expressed with GAL in amygdala and hypothalamus, as being important in the protein kinase C (PKC) supported activity of the GG genotype of GAL5.1 but less so in the CA genotype. Our unique study uses a novel combination of human association analysis, CRISPR genome editing in mice, animal behavioural analysis and cell culture studies to identify a highly conserved regulatory mechanism linking anxiety and alcohol intake that might contribute to increased susceptibility to anxiety and alcohol abuse in men.

genetics↗

bioRxiv2018

Synaptic integration across first-order tactile neurons can discriminate edge orientations with high acuity and speed

Our ability to manipulate objects relies on tactile inputs from first-order tactile neurons that innervate the glabrous skin of the hand. The distal axon of these neurons branches in the skin and innervates many mechanoreceptors, yielding spatially-complex receptive fields. Here we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3{degrees}) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We use machine learning to train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. In particular, our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs refine discrimination and maintain robustness over longer timescales. Taken together, our results provide new insight into the computations occurring in the earliest stages of the human tactile processing pathway and how they may be critical for supporting hand function.\n\nAuthor SummaryOur ability to manipulate objects relies on tactile inputs signaled by first-order neurons that innervate mechanoreceptors in the skin of the hand and have spatially-complex receptive fields. Here we show how synaptic integration across the rich inputs from first-order neurons can rapidly and accurately process the orientation of edges moving across the fingertip. We derive spiking models of human first-order tactile neurons, then use the models to simulate the peripheral neuronal population that innervates a fingertip. We use machine learning to train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons could underlie human ability to process edge orientations with high acuity and speed.

neuroscience↗