Biology subjects

Baranger, D. A.

Publications and source records attributed to Baranger, D. A..

4 recordsLinked to original sources

bioRxiv2023

Identification of a convergent spinal neuron population that encodes itch

Itch is a protective sensation that drives scratching. Although specific cell types have been proposed to underlie itch, the neural circuit basis for itch remains unclear. Here, we used two-photon Ca2+ imaging of the dorsal horn to visualize the neuronal populations that are activated by itch-inducing agents. We identify a convergent population of spinal neurons that is defined by the expression of GRPR. Moreover, we discover that itch is conveyed to the brain via GRPR-expressing spinal output neurons that target the lateral parabrachial nucleus. Further, we show that nalfurafine, a clinically effective kappa opioid receptor agonist, relieves itch by inhibiting GRPR spinoparabrachial neurons. Finally, we demonstrate that a subset of GRPR spinal neurons show persistent, cell-intrinsic Ca2+ oscillations. These experiments provide the first population-level view of the spinal neurons that respond to pruritic stimuli, pinpoint the output neurons that convey itch to the brain, and identify the cellular target of kappa opioid receptor agonists for the inhibition of itch. In briefThrough population imaging, Sheahan et al. identify a network of neurons in the dorsal horn that is activated by pruritogens and find that kappa opioid receptor signaling inhibits itch through the selective inhibition of GRPR spinoparabrachial neurons. HighlightsO_LIItch-inducing agents drive activity in a common population of GRPR-expressing spinal interneurons C_LIO_LIGRPR spinal projection neurons transmit itch from the spinal cord to the brain C_LIO_LIKappa opioids reduce itch through the inhibition of GRPR spinoparabrachial neurons C_LIO_LIGRPR activation elicits persistent, intrinsic Ca2+ oscillations C_LI

neuroscience↗

bioRxiv2022

Multi-omics analyses cannot identify true-positive novel associations from underpowered genome-wide association studies of four brain-related traits

BackgroundThe integration of multi-omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome-wide association studies (GWAS). It has additionally been suggested that multi-omics may aid in novel variant discovery, thus circumventing the need to increase GWAS sample sizes. We tested whether incorporating multi-omics information in earlier and smaller sized GWAS boosts true-positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. MethodsWe applied ten different analytic approaches to integrating multi-omics data from twelve sources (e.g., Genotype-Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain-related traits (i.e., alcohol use disorder/problematic alcohol use [AUD/PAU], major depression [MDD], schizophrenia [SCZ], and intracranial volume [ICV]) could detect genes that were revealed by a later and larger GWAS. ResultsMulti-omics data did not reliably identify novel genes in earlier less powered GWAS (PPV<0.2; 80% false-positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1-8 additional genes, but only for well-powered early GWAS of highly heritable traits (i.e., ICV and SCZ). Multi-omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H-MAGMA), was useful for prioritizing genes within genome-wide significant loci (PPVs = 0.5 - 1.0). ConclusionsAlthough the integration of multi-omics information, particularly when multiple methods agree, helps prioritize GWAS findings and translate them into information about disease biology, it does not substantively increase novel gene discovery in brain-related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is a requirement.

genetics↗

bioRxiv2020

The longitudinal stability of fMRI activation during reward processing in adolescents and young adults

BackgroundThe use of functional neuroimaging has been an extremely fruitful avenue for investigating the neural basis of human reward function. This approach has included identification of potential neurobiological mechanisms of psychiatric disease and examination of environmental, experiential, and biological factors that may contribute to disease risk via effects on the reward system. However, a central and largely unexamined assumption of much of this research is that neural reward function is an individual difference characteristic that is relatively stable over time. MethodsIn two independent samples of adolescents and young adults studied longitudinally (Ns = 145 & 153, 100% female & 100% male, ages 15-21 & 20-22, 2-4 scans & 2 scans respectively), we tested within-person stability of reward-task BOLD activation, with a median of 1 and 2 years between scans. We examined multiple commonly used contrasts of active states and baseline in both the anticipation and feedback phases of a card-guessing reward task. We examined the effects of cortical parcellation resolution, contrast, network (reward regions and resting-state networks), region-size, and activation strength and variability on the stability of reward-related activation. ResultsOverall, stability (ICC; intra-class correlation) across 1-2 years was modest. In both samples, contrasts of an active state relative to a baseline were more stable (e.g., Win>Baseline; mean ICC = 0.13 - 0.33) than contrasts of two active states (e.g., Win>Loss; mean ICC = 0.048 - 0.05). Additionally, activation in reward regions was less stable than in many non-task networks (e.g., dorsal attention), and activation in regions with greater between-subject variability showed higher stability in both samples. ConclusionsThese results show that functional neuroimaging activation to reward has modest stability over 1-2 years. Notably, results suggest that contrasts intended to map cognitive function and show robust group-level effects (i.e. Win > Loss) may be less effective in studies of individual differences and disease risk. The robustness of group-level activation should be weighed against other factors when selecting regions of interest in individual difference fMRI studies.

neuroscience↗

bioRxiv2018

Convergent evidence for predispositonal effects of brain volume on alcohol consumption

BackgroundAlcohol use has been reliably associated with smaller subcortical and cortical regional gray matter volumes (GMVs). Whether these associations reflect shared predisposing risk factors and/or causal consequences of alcohol use remains poorly understood.\n\nMethodsData came from 3 neuroimaging samples (total n=2,423), spanning childhood/adolescence to middle age, with prospective or family-based data. First, we identified replicable GMV correlates of alcohol use. Next, we used family-based and longitudinal data to test whether these associations may plausibly reflect a predispositional liability for alcohol use, and/or a causal consequence of alcohol use. Finally, we evaluated whether GWAS-defined genomic risk for alcohol consumption is enriched for genes preferentially expressed in regions identified in our neuroimaging analyses, using heritability and gene-set enrichment, and transcriptome-wide association study (TWAS) approaches.\n\nResultsSmaller right dorsolateral prefrontal cortex (DLPFC; i.e., middle and superior frontal gyri) and insula GMVs were associated with increased alcohol use across samples. Family-based and prospective longitudinal data suggest these associations are genetically conferred and that DLPFC GMV prospectively predicts future use and initiation. Genomic risk for alcohol use was enriched in gene-sets preferentially expressed in the DLPFC and associated with differential expression of C16orf93, CWF19L1, and C18orf8 in the DLPFC.\n\nConclusionsThese data suggest that smaller DLPFC and insula GMV plausibly represent predispositional risk factors for, as opposed to consequences of, alcohol use. Alcohol use, particularly when heavy, may potentiate these predispositional risk factors. DLPFC and insula GMV represent promising biomarkers for alcohol consumption liability and related psychiatric and behavioral phenotypes.

neuroscience↗