bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.05.04.538315

Deciphering the role of brainstem glycinergic neurons during startle and prepulse inhibition

Abstract

Prepulse inhibition (PPI) of the auditory startle response is the gold standard operational measure of sensorimotor gating. Affected by various neurological and neuropsychiatric illnesses, PPI also declines during aging. While PPI deficits are often associated with cognitive overload, attention impairments and motor dysfunctions, their reversal is routinely used in experimental systems for drug screening. Yet, the cellular and circuit-level mechanisms of PPI remain unclear, even under non-pathological conditions. Recent evidence shows that neurons located in the brainstem caudal pontine reticular nucleus (PnC) expressing the glycine transporter type 2 (GlyT2+) receive inputs from the central nucleus of the amygdala (CeA) and contribute to PPI via an uncharted pathway. Using tract-tracing and immunohistochemical analyses in GlyT2-eGFP mice, we reveal the neuroanatomical location of CeA glutamatergic neurons innervating GlyT2+ neurons. Our precise in vitro optogenetic manipulations coupled to field electrophysiological recordings demonstrate that CeA glutamatergic inputs do suppress auditory neurotransmission in PnC neurons but not via action on transmitter release from auditory afferents. Rather, our data is consistent with excitatory drive onto GlyT2+ neurons. Indeed, our PPI experiments in vivo demonstrate that optogenetic activation of GlyT2+ PnC neurons increases PPI and is sufficient to induce PPI, clarifying the crucial role of these neurons in young GlyT2-Cre mice. In contrast, in older GlyT2-Cre mice, PPI is reduced and not further altered by optogenetic inhibition of GlyT2+ neurons. We conclude that GlyT2+ PnC neurons innervated by CeA glutamatergic inputs are crucial for PPI and we highlight their reduced activity during the age-dependent decline in PPI. SIGNIFICANCE STATEMENTSensorimotor gating is a pre-attentive mechanism that declines with age and that is affected by neuropsychiatric and neurological disorders. Prepulse inhibition (PPI) of startle commonly measures sensorimotor gating to assess cognitive and motor symptoms and to screen drug efficacy. Yet, the neuronal mechanisms underlying PPI are still unresolved, limiting therapeutic advances. Here, we identify brainstem glycinergic neurons essential for PPI using tract tracing, in vitro electrophysiology and precise in vivo optogenetic manipulations during startle measurements in mice. Innervated by amygdala glutamatergic inputs, we show that these glycinergic neurons are essential and sufficient to induce PPI in young mice. In contrast, these neurons do not contribute to PPI in older mice. We provide new insights to the theoretical construct of PPI.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Huang, W., Cano, J., Fenelon, K.. 2023-05-04. Deciphering the role of brainstem glycinergic neurons during startle and prepulse inhibition. https://doi.org/10.1101/2023.05.04.538315

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗