Biology subjects

Jin, H.

Publications and source records attributed to Jin, H..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

Methodological guidelines for isolation and purification of plant extracellular vesicles

Plant extracellular vesicles (EVs) have become the focus of rising interest due to their important roles in the cross-kingdom trafficking of molecules from hosts to interacting microbes to modulate pathogen virulence. However, the isolation of pure intact EVs from plants still represents a considerable challenge. Currently, plant EVs have been isolated from apoplastic washing fluid (AWF) using a variety of methods. Here, we compare two published methods used for isolating plant EVs, and provide a detailed recommended method for AWF collection from Arabidopsis thaliana, followed by EV isolation via differential ultracentrifugation. To further separate and purify specific subclasses of EV from heterogeneous vesicles, sucrose or iodixanol density-based separation and immunoaffinity capture are then utilized. We found that immunoaffinity capture provides a significant advantage for specific EV isolation when suitable specific EV biomarkers and their corresponding antibodies are available. Overall, this study guides the selection and optimization of EV isolation methods for desired downstream applications.

plant biology↗

bioRxiv2021

A promiscuity locus confers Lotus burttii nodulation with rhizobia from five different genera

Legumes acquire access to atmospheric nitrogen through nitrogen fixation by rhizobia in root nodules. Rhizobia are soil dwelling organisms and there is a tremendous diversity of rhizobial species in different habitats. From the legume perspective, host range is a compromise between the ability to colonize new habitats, where the preferred symbiotic partner may be absent, and guarding against infection by suboptimal nitrogen fixers. Here, we investigate natural variation in rhizobial host range across Lotus species. We find that Lotus burttii is considerably more promiscuous than Lotus japonicus, represented by the Gifu accession, in its interactions with rhizobia. This promiscuity allows Lotus burttii to form nodules with Mesorhizobium, Rhizobium, Sinorhizobium, Bradyrhizobium, and Allorhizobium species that represent five distinct genera. Using recombinant inbred lines, we have mapped the Gifu/burttii promiscuity QTL to the same genetic locus regardless of rhizobial genus, suggesting a general genetic mechanism for host-range expansion. The Gifu/burttii QTL now provides an opportunity for genetic and mechanistic understanding of promiscuous legume-rhizobia interactions.

plant biology↗

bioRxiv2021

The SARS-CoV-2 Delta variant is poised to acquire complete resistance to wild-type spike vaccines

mRNA-based vaccines provide effective protection against most common SARS-CoV-2 variants. However, identifying likely breakthrough variants is critical for future vaccine development. Here, we found that the Delta variant completely escaped from anti-N-terminal domain (NTD) neutralizing antibodies, while increasing responsiveness to anti-NTD infectivity-enhancing antibodies. Although Pfizer-BioNTech BNT162b2-immune sera neutralized the Delta variant, when four common mutations were introduced into the receptor binding domain (RBD) of the Delta variant (Delta 4+), some BNT162b2-immune sera lost neutralizing activity and enhanced the infectivity. Unique mutations in the Delta NTD were involved in the enhanced infectivity by the BNT162b2-immune sera. Sera of mice immunized by Delta spike, but not wild-type spike, consistently neutralized the Delta 4+ variant without enhancing infectivity. Given the fact that a Delta variant with three similar RBD mutations has already emerged according to the GISAID database, it is necessary to develop vaccines that protect against such complete breakthrough variants.

microbiology↗

bioRxiv2021

Increases in multiple resources promote plant invasion

Invasion by alien plants is frequently attributed to increased resource availabilities. Still, our understanding is mainly based on effects of single resources despite the fact that plants rely on multiple resources. How multiple resources affect success of alien plants remains largely unexplored. Here, with two common garden experiments, one in China and one in Germany, we tested whether nutrient and light availabilities affected the competitive outcomes between alien and native plants. We found that under low resource availabilities or with addition of only one type of resource aliens were not more competitive than natives. However, with a joint increase of nutrients and light intensity, aliens outcompeted natives. Our finding indicates that addition of multiple resources could greatly reduce the number of limiting factors (i.e. niche dimensionality), and that this favors the dominance of alien species. It also indicates that habitats experiencing multiple global changes might be more vulnerable to plant invasion.

ecology↗

bioRxiv2021

A bacterium-like particle vaccine displaying Zika virus prM-E induces systemic immune responses in mice

The emergence of Zika virus (ZIKV) infection, which is an unexpectedly associated with congenital defects, has prompted the development of safe and effective vaccines. The gram-positive enhancer matrix-protein anchor (GEM-PA) display system has emerged as a versatile and highly effective platform for delivering target proteins for vaccines. In this article, we developed a bacterium-like particle vaccine ZI-{Delta}-PA-GEM based on the GEM-PA system. The fusion protein ZI-{Delta}-PA, which contains the prM-E-{Delta} protein of ZIKV (with a stem-transmembrane region deletion) and the protein anchor PA3, was expressed. The fusion protein was successfully displayed on the GEM surface, forming ZI-{Delta}-PA-GEM. Moreover, when BALB/c mice were immunized intramuscularly with ZI-{Delta}-PA-GEM combined with 201 VG and poly(I:C) adjuvants, durable ZIKV-specific IgG and protective neutralizing antibody responses were induced. Potent B cell/DC activation was also be stimulated early after immunization. Remarkably, splenocyte proliferation, the secretion of multiple cytokines, T/B cell activation and central memory T cell responses were elicited. These data indicate that ZI-{Delta}-PA-GEM is a promising bacterium-like particle vaccine candidate for ZIKV. Author summaryBecause Zika virus (ZIKV) infection is considered as an example of "disease X", the development of a safe and effective ZIKV vaccine is essential. The gram-positive enhancer matrix-protein anchor (GEM-PA) display system has been used in many vaccine studies due to its advantages. In this study, prM-E-{bigtriangleup} protein of ZIKV (with a stem-transmembrane region deletion) and the protein anchor PA3 was fusion expressed, termed ZI-{bigtriangleup}-PA. Then the fusion protein ZI-{bigtriangleup}-PA could be displayed on the surface of GEM, forming ZI-{bigtriangleup}-PA-GEM. The author evaluated the immunogenicity of ZI-{bigtriangleup}-PA-GEM with the 201 VG and poly(I:C) adjuvants. The study demonstrates that ZI-{bigtriangleup}-PA-GEM induced mice to produce neutralizing antibody and specific cellular immune responses. The author believe that the bacterium-like particle vaccine ZI-{bigtriangleup}-PA-GEM has the potential to be used as the ZIKV vaccine.

microbiology↗

bioRxiv2021

Prediction of therapy outcomes of CLL using gene expression intensity, clustering, and ANN classification of single cell transcriptomes

BackgroundSingle cell transcriptomics is a new technology that enables us to measure the expression levels of genes from an individual cell. The expression information reflects the activity of that individual cell which could be used to indicate the cell types. Chronic lymphocytic leukemia (CLL) is a malignancy of B cells, one of the peripheral blood mononuclear cells subtypes. We applied five analytical tools for the study of single cell gene expression in CLL course of therapy. These tools included the analysis of gene expression distributions - median, interquartile ranges, and percentage above quality control (QC) threshold; hierarchical clustering applied to all cells within individual single cell data sets; and artificial neural network (ANN) for classification of healthy peripheral blood mononuclear cell (PBMC) subtypes. These tools were applied to the analysis of CLL data representing states before and during the therapy. ResultsWe identified patterns in gene expression that distinguished two patients that had complete remission (complete response), a patient that had a relapse, and a patient that had partial remission within three years of Ibrutinib therapy. Patients with complete remission showed a rapid decline of median gene expression counts, and the total number of gene counts below the QC threshold for healthy cells (670 counts) in 80% of more of the cells. These patients also showed the emergence of healthy-like PBMC cluster maps within 120 days of therapy and distinct changes in predicted proportions of PBMC cell types. ConclusionsThe combination of basic statistical analysis, hierarchical clustering, and supervised machine learning identified patterns from gene expression that distinguish four CLL patients treated with Ibrutinib that experienced complete remission, partial remission, or relapse. These preliminary results suggest that new bioinformatics tools for single cell transcriptomics, including ANN comparison to healthy PBMC, offer promise in prognostics of CLL.

bioinformatics↗

bioRxiv2021

Artificial Neural Networks for classification of single cell gene expression

BackgroundSingle-cell transcriptome (SCT) sequencing technology has reached the level of high-throughput technology where gene expression can be measured concurrently from large numbers of cells. The results of gene expression studies are highly reproducible when strict protocols and standard operating procedures (SOP) are followed. However, differences in sample processing conditions result in significant changes in gene expression profiles making direct comparison of different studies difficult. Unsupervised machine learning (ML) uses clustering algorithms combined with semi-automated cell labeling and manual annotation of individual cells. They do not scale up well and a workflow used on a specific dataset will not perform well with other studies. Supervised ML classification shows superior classification accuracy and generalization properties as compared to unsupervised ML methods. We describe a supervised ML method that deploys artificial neural networks (ANN), for 5-class classification of healthy peripheral blood mononuclear cells (PBMC) from multiple diverse studies. ResultsWe used 58 data sets to train ANN incrementally - over ten cycles of training and testing. The sample processing involved four protocols: separation of PBMC, separation of PBMC + enrichment (by negative selection), separation of PBMC + FACS, and separation of PBMC + MACS. The training data set included between 85 and 110 thousand cells, and the test set had approximately 13 thousand cells. Training and testing were done with various combinations of data sets from four principal data sources. The overall accuracy of classification on independent data sets reached 5-class classification accuracy of 94%. Classification accuracy for B cells, monocytes, and T cells exceeded 95%. Classification accuracy of natural killer (NK) cells was 75% because of the similarity between NK cells and T cell subsets. The accuracy of dendritic cells (DC) was low due to very low numbers of DC in the training sets. ConclusionsThe incremental learning ANN model can accurately classify the main types of PBMC. With the inclusion of more DC and resolving ambiguities between T cell and NK cell gene expression profiles, we will enable high accuracy supervised ML classification of PBMC. We assembled a reference data set for healthy PBMC and demonstrated a proof-of-concept for supervised ANN method in classification of previously unseen SCT data. The classification shows high accuracy, that is consistent across different studies and sample processing methods.

bioinformatics↗

bioRxiv2021

Soil fauna may buffer the negative effects of drought on alien plant invasion

Although many studies have tested the direct effects of drought on alien plant invasion, less is known about how drought affects alien plant invasion indirectly via other groups of organisms such as soil fauna. To test for such indirect effects, we grew single plant of nine naturalized alien target species in pot-mesocosms with a native community of five native grassland species under four combinations of two drought (well-watered vs drought) and two soil-fauna-inoculation (with vs without) treatments. We found that drought decreased the absolute and the relative biomass production of the alien plants, and thus reduced their competitive advantage in the native community. Drought decreased the abundance of soil fauna, particularly the soil mites, but did not affect abundance and richness of soil herbivores. Soil-fauna inoculation increased the biomass of the native plant community and thereby decreased the relative biomass production of the alien species. The increased invasion resistance due to soil fauna, however, tended (p = 0.09) to be stronger for plants growing under well-watered conditions than under drought. Our multispecies experiment thus shows that soil fauna might help native resident communities to resist alien plant invasions, but that this effect might be diminished by drought.

ecology↗

bioRxiv2021

Single-Molecule Manipulation of Macromolecules on Membranes Using High-Resolution Optical Tweezers

Despite their wide applications into soluble macromolecules, optical tweezers have rarely been used to characterize dynamics of membrane proteins, mainly due to lack of model membranes compatible with optical trapping. Here, we found that optical tweezers can stably trap giant unilamellar vesicles (GUVs) containing iodixanol with controlled membrane tension, which can potentially serve as a model membrane to study dynamics of membranes, membrane proteins, or their interactions. We also observed that small unilamellar vesicles (SUVs) are rigid enough to resist large pulling force and offer potential advantages to pull membrane proteins. To demonstrate the use of both model membranes, we pulled membrane tethers from the trapped GUVs and measured the folding or binding dynamics of a single DNA hairpin or synaptotagmin-1 C2 domain attached to the GUV or SUV with high spatiotemporal resolution. Our methodologies facilitate single-molecule manipulation studies of membranes or membrane proteins using optical tweezers.

biophysics↗

bioRxiv2021

Hierarchical Harmonization of Atom-Resolved Metabolic Re-actions Across Metabolic Databases

Metabolic models have been proven to be useful tools in system biology and have been successfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in constructing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big challenge. Incomplete and inconsistent atomistic details in compound representations across databases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization relationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmonization across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we established hierarchical relationships between metabolic reactions, enabling the harmonization of 3,856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the consistency of atom mappings within and between harmonized reactions, detecting some consistency issues between the reaction and compound descriptions in these metabolic databases.

systems biology↗

bioRxiv2021

CovidExpress: an interactive portal for intuitive investigation on SARS-CoV-2 related transcriptomes

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans could cause coronavirus disease 2019 (COVID-19). Since its first discovery in Dec 2019, SARS-CoV-2 has become a global pandemic and caused 3.3 million direct/indirect deaths (2021 May). Amongst the scientific communitys response to COVID-19, data sharing has emerged as an essential aspect of the combat against SARS-CoV-2. Despite the ever-growing studies about SARS-CoV-2 and COVID-19, to date, only a few databases were curated to enable access to gene expression data. Furthermore, these databases curated only a small set of data and do not provide easy access for investigators without computational skills to perform analyses. To fill this gap and advance open-access to the growing gene expression data on this deadly virus, we collected about 1,500 human bulk RNA-seq datasets from publicly available resources, developed a database and visualization tool, named CovidExpress (https://stjudecab.github.io/covidexpress). This open access database will allow research investigators to examine the gene expression in various tissues, cell lines, and their response to SARS-CoV-2 under different experimental conditions, accelerating the understanding of the etiology of this disease to inform the drug and vaccine development. Our integrative analysis of this big dataset highlights a set of commonly regulated genes in SARS-CoV-2 infected lung and Rhinovirus infected nasal tissues, including OASL that were under-studied in COVID-19 related reports. Our results also suggested a potential FURIN positive feedback loop that might explain the evolutional advantage of SARS-CoV-2.

bioinformatics↗

bioRxiv2021

A genetic mouse model with postnatal Nf1 and p53 loss recapitulates the histology and transcriptome of human malignant peripheral nerve sheath tumor

BackgroundMalignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas. Somatic inactivation of NF1 and cooperating tumor suppressors, including CDKN2A/B, PRC2, and p53, is found in most MPNST. Inactivation of the LATS1/2 kinases of the Hippo pathway was recently shown to cause tumors resembling MPNST histologically, although Hippo pathway mutations are rarely found in MPNST. Because existing genetically engineered mouse (GEM) models of MPNST do not recapitulate some of the key genetic features of human MPNST, we aimed to establish a mouse MPNST model that recapitulated the human disease genetically, histologically, and molecularly. MethodsWe combined two genetically modified alleles, an Nf1;Trp53 cis-conditional allele and an inducible Plp-CreER allele (NP-Plp), to model the somatic, possibly postnatal, mutational events in human MPNST. We also generated conditional Lats1;Lats2 knockout mice. We performed histopathologic analysis of mouse MPNST models and transcriptomic comparison of mouse models and human nerve sheath tumors. ResultsPostnatal Nf1;Trp53 cis-deletion resulted in GEM-MPNST that was histologically more similar to human MPNST than the widely used germline Nf1;Trp53 cis-heterozygous (NPcis) model and showed partial loss of H3K27me3. At the transcriptome level, Nf1;p53-driven GEM-MPNST were distinct from Lats-driven GEM-MPNST and resembled human MPNST more closely than do Lats-driven tumors. ConclusionsThe NP-Plp model recapitulates human MPNST genetically, histologically, and molecularly. Key PointsO_LIPostnatal Nf1;p53 cis-deletion in NP-Plp mice results in tumors similar to MPNST. C_LIO_LIThe transcriptomes of Nf1;p53-driven and Lats-driven MPNST models are distinct. C_LIO_LINP-Plp model resembles human MPNST genetically, histologically, and molecularly. C_LI Importance of the StudyMalignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas with a poor prognosis and limited treatment options. Existing genetically engineered mouse (GEM) models of MPNST do not recapitulate some of the key genetic features of human MPNST. To model the somatic, possibly postnatal, mutational events seen in MPNST patients, we generated a GEM-MPNST model by combining two genetically modified alleles, an Nf1;Trp53 cis-conditional allele and a Plp-CreER allele. Our histologic and transcriptomic analyses showed that this NP-Plp model resembles human MPNST genetically, histologically, and molecularly--more so than the widely used NPcis model and the recently published Lats-driven model. The NP-Plp model is genetically simple, making it easy to maintain and an ideal platform for preclinical studies. Given its tamoxifen-inducible nature, this model can be used to study the time/stage dependency of the tumorigenic potential of Schwann cells.

cancer biology↗

bioRxiv2021

Time-varying Dynamic Network Model For Dynamic Resting State Functional Connectivity in fMRI and MEG imaging

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occurs over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods, have various limitations due to their inherent non-adaptive nature and high-dimensionality including an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multimodal functional imaging datasets. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns to detect dynamic state transitions in data and a low-dimensional manifold of dynamic RSFC. TVDN is generalizable to handle multimodal functional neuroimaging data (fMRI and MEG/EEG). The resulting estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

bioengineering↗

bioRxiv2021

Medulla glutamatergic neurons control wake-sleep transitions

Sleep is a ubiquitous behavior in animal species. Yet, brain circuits controlling sleep remain poorly understood. Previous studies have identified several brain structures that promote sleep, but whether these structures are involved in sleep initiation or sleep maintenance remains largely unknown. Here we identified a population of glutamatergic neurons in the medulla that project to the preoptic area (POA), a prominent sleep-promoting region. Chemogenetic silencing of POA-projecting medulla neurons disrupts the transitions from wakefulness to Non-Rapid Eye Movement (NREM) sleep, whereas chemogenetic activation of these neurons promotes NREM sleep. Moreover, we show that optogenetic activation of medulla glutamatergic neurons or their projections in the POA reliably initiates long-lasting NREM sleep in awake mice. Together, our findings uncover a novel excitatory brainstem-hypothalamic circuit that controls the wake-sleep transitions.

neuroscience↗

bioRxiv2021

Comparative Analysis of Genetically-Modified Crops: Conditional Equivalence Criteria

The comparative assessment of genetically-modified (GM) crops relies on the principle of substantial equivalence, which states that such products should be compared to conventional counterparts that have an established history of safe use. In an effort to operationalize this principle, the GMO Panel of the European Food Safety Authority proposed an equivalence test that directly compares a GM test variety with a set of unrelated, conventionally-bred reference varieties with part of the difference as the known background of the test (the same as the given control). The criterion of the EFSA test, however, is defined solely by genotypic differences between the non-traited control and reference varieties (i.e. the background effect) while assuming the so-called GM trait effect as zero. As the outcome of an EFSA equivalence test is determined primarily by the similarity, or lack thereof, of the control and references, a conditional equivalence criterion is proposed in this investigation that focuses on "unintended" effects of a GM trait which is irrespective of the (random) genotypic value of a given control. The new criterion also includes a mean-scaled standard similar to the 80-125% rule for bioequivalence assessment practiced in the pharmaceutical industry as an alternative when the reference variation is zero or close to zero. In addition, optional criteria are proposed with a step-wise procedure to control the rate of false negatives (non-equivalence by chance) providing a comprehensive assessment under multiple comparisons. An application to maize grain composition data demonstrates that the conditional equivalence criterion provides effect-specific and more robust assessment of equivalence than the EFSA criterion did, especially for GM traits showing negligible or no unintended effects which are likely true for most traits in the current market.

plant biology↗

bioRxiv2021

Methylomic alteration in peripheral blood lymphocytes of prodromal stage and first-episode Chinese Han schizophrenia patients

BackgroundAlthough epigenetic dysregulation has long been proposed to promote the onset of schizophrenia, the landscape of the methylomic changes across the whole genome is yet established. MethodsUsing Infinium Human Methylation 850 BeadChip Array and MethylTarget sequencing method, we investigated the genome-wide methylation profiles and further validated methylation profiles of target genes in peripheral blood lymphocytes between individuals with psychosis risk syndrome (PRS), patients with first-episode schizophrenia (FES) and healthy controls (HC) in Chinese Han population. ResultsWe detected 372 sites between psychosis risk syndrome (PRS) and healthy controls (HC), which increased to 460 sites in first-episode schizophrenia (FES) with 207 sites shared. Both PRS and FES featured profound hypomethylation within gene body. Gene ontology and network annotation merged on loci enriched in disease associated signaling pathways (MAPK(Mitogen Activated Protein Kinases), Glutamatergic, GABAergic etc.). ConclusionsOur study implicated characteristic hypomethylation in both the discovery and validation cohorts in SYNGAP1, one of the frequently studied genes in neurodevelopmental disorders. This is the first methylome-wide association study between PRS and FES in Chinese Han population. Our findings provide potential biomarkers that can be used for future development of disease therapy and management.

genetics↗

bioRxiv2021

Spray-induced gene silencing for disease control is dependent on the efficiency of pathogen RNA uptake

Recent discoveries show that fungi can take up environmental RNA, which can then silence fungal genes through environmental RNA interference. This discovery prompted the development of Spray-Induced Gene Silencing (SIGS) for plant disease management. In this study, we aimed to determine the efficacy of SIGS across a variety of eukaryotic microbes. We first examined the efficiency of RNA uptake in multiple pathogenic and non-pathogenic fungi, and an oomycete pathogen. We observed efficient double-stranded RNA (dsRNA) uptake in the fungal plant pathogens Botrytis cinerea, Sclerotinia sclerotiorum, Rhizoctonia solani, Aspergillus niger, and Verticillium dahliae, but no uptake in Colletotrichum gloeosporioides, and weak uptake in a beneficial fungus, Trichoderma virens. For the oomycete plant pathogen, Phytophthora infestans, RNA uptake was limited, and varied across different cell types and developmental stages. Topical application of dsRNA targeting virulence-related genes in the pathogens with high RNA uptake efficiency significantly inhibited plant disease symptoms, whereas the application of dsRNA in pathogens with low RNA uptake efficiency did not suppress infection. Our results have revealed that dsRNA uptake efficiencies vary across eukaryotic microbe species and cell types. The success of SIGS for plant disease management can largely be determined by the pathogen RNA uptake efficiency.

microbiology↗

bioRxiv2021

BNP facilitates NMB-mediated histaminergic itch via NPRC-NMBR crosstalk

B-type natriuretic peptide (BNP) binds to its two cognate receptors NPRA and NPRC, encoded by Npr1 and Npr3, respectively, with equal potency and both are expressed in the spinal cord. Moreover, natriuretic peptides (NP) signal through the inhibitory cGMP pathway, raising the question of how BNP may transmit itch information. We report that Npr3 is highly restricted to laminae I-II of the dorsal horn, and partially overlaps with neuromedin B receptor (NMBR) that encodes histaminergic itch. 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 gastrin releasing peptide (GRP) that encodes nonhistaminergic itch. Consistently, BNP evoked Ca2+ response in NMBR/NPRC HEK 293 cells and BNP-saporin that ablated both Npr1 and Npr3 neurons impaired histamine-, but not CQ-evoked, itch. These results reveal a previously unknown mechanism by which BNP changes its inhibitory mode of action to the facilitation of itch through a novel NPRC-NMBR cross-talk. Our studies suggest that neuropeptides encode histaminergic and nonhistaminergic itch not only through distinct modes but also in synergy.

neuroscience↗