BNP facilitates NMB-encoded histaminergic itch via NPRC-NMBR crosstalk
Abstract
Histamine-dependent and -independent itch is conveyed by parallel peripheral neural pathways that express gastrin-releasing peptide (GRP) and neuromedin B (NMB), respectively, to the spinal cord of mice. B-type natriuretic peptide (BNP) has been proposed to transmit both types of itch via its receptor NPRA encoded by Npr1. However, BNP also binds to its cognate receptor, NPRC encoded by Npr3 with equal potency. Moreover, natriuretic peptides (NP) signal through the Gi-couped inhibitory cGMP pathway that is supposed to inhibit neuronal activity, raising the question of how BNP may transmit itch information. Here we report that Npr3 expression in laminae I-II of the dorsal horn partially overlaps with NMB receptor (NMBR) that transmits histaminergic itch via Gq-couped PLCb-Ca2+ signaling pathway. Functional studies indicate that NPRC is required for itch evoked by histamine but not chloroquine (CQ), a nonhistaminergic pruritogen. Importantly, BNP significantly facilitates scratching behaviors mediated by NMB, but not GRP. Consistently, BNP evoked Ca2+ responses in NMBR/NPRC HEK 293 cells and NMBR/NPRC dorsal horn neurons. These results reveal a previously unknown mechanism by which BNP facilitates NMB-encoded itch through a novel NPRC-NMBR cross-signaling in mice. Our studies uncover distinct modes of action for neuropeptides in transmission and modulation of itch in mice.
Data availability
All data generated or analyzed during this study are included in the manuscript and supporting file; Source Data files have been provided for Figure 1B, C, Figure 1-figure supplement 1A, B, Figure 1-figure supplement 2G, H, Figure 2A-F, Figure 3A-I, Figure 4F, G, Figure 5F, H, I, Figure 6A-C, and Figure 6-figure supplement 1A, B, E.
Article and author information
Author details
Funding
National Institutes of Health (R01NS094344,R01 DA037261-01A1 and R01NS113938-01A1)
- Qing-Tao Meng
- Xian-Yu Liu
- Juan Liu
- Admire Munanairi
- Devin M Barry
- Benlong Liu
- Hua Jin
- Yu Sun
- Qianyi Yang
- Fang Gao
- Li Wan
- Jiahang Peng
- Jin-Hua Jin
- Kai-Feng Shen
- Ray Kim
- Jun Yin
- Zhou-Feng Chen
National Natural Science Foundation of China (82171764)
- Xue-Ting Liu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All experiments were performed in accordance with the guidelines of the National Institutes of Health and the International Association for the Study of Pain and were approved by the Animal Studies Committee at Washington University School of Medicine. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols of Washington University School of Medicine (#20190163).
Copyright
© 2021, Meng et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,271
- views
-
- 292
- downloads
-
- 11
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
The relation between neural activity and behaviorally relevant variables is at the heart of neuroscience research. When strong, this relation is termed a neural representation. There is increasing evidence, however, for partial dissociations between activity in an area and relevant external variables. While many explanations have been proposed, a theoretical framework for the relationship between external and internal variables is lacking. Here, we utilize recurrent neural networks (RNNs) to explore the question of when and how neural dynamics and the network’s output are related from a geometrical point of view. We find that training RNNs can lead to two dynamical regimes: dynamics can either be aligned with the directions that generate output variables, or oblique to them. We show that the choice of readout weight magnitude before training can serve as a control knob between the regimes, similar to recent findings in feedforward networks. These regimes are functionally distinct. Oblique networks are more heterogeneous and suppress noise in their output directions. They are furthermore more robust to perturbations along the output directions. Crucially, the oblique regime is specific to recurrent (but not feedforward) networks, arising from dynamical stability considerations. Finally, we show that tendencies toward the aligned or the oblique regime can be dissociated in neural recordings. Altogether, our results open a new perspective for interpreting neural activity by relating network dynamics and their output.
-
- Neuroscience
In this paper, we provide an overview and analysis of the BRAIN Initiative data-sharing ecosystem. First, we compare and contrast the characteristics of the seven BRAIN Initiative data archives germane to data sharing and reuse, namely data submission and access procedures and aspects of interoperability. Second, we discuss challenges, benefits, and future opportunities, focusing on issues largely specific to sharing human data and drawing on N = 34 interviews with diverse stakeholders. The BRAIN Initiative-funded archive ecosystem faces interoperability and data stewardship challenges, such as achieving and maintaining interoperability of data and archives and harmonizing research participants’ informed consents for tiers of access for human data across multiple archives. Yet, a benefit of this distributed archive ecosystem is the ability of more specialized archives to adapt to the needs of particular research communities. Finally, the multiple archives offer ample raw material for network evolution in response to the needs of neuroscientists over time. Our first objective in this paper is to provide a guide to the BRAIN Initiative data-sharing ecosystem for readers interested in sharing and reusing neuroscience data. Second, our analysis supports the development of empirically informed policy and practice aimed at making neuroscience data more findable, accessible, interoperable, and reusable.