A microbial amino-acid-conjugated bile acid,tryptophan-cholic acid, improves glucose homeostasis via the orphan receptor MRGPRE

Jun Lin ¹²³¹⁸, Qixing Nie ¹²⁴¹⁸, Jie Cheng ⁵⁶¹⁸, YaNi Zhong ⁵¹⁸, Tianyao Zhang ⁵¹⁸, Xiuying Zhang ⁷¹⁸, Xiaoyan Ge ⁶¹⁸, Yong Ding ¹²³¹⁸, Canyang Niu ⁵⁸, Yuhua Gao ¹²³, Kai Wang ¹²³, Mingxin Gao ⁹, Xuemei Wang ¹²³, Weixuan Chen ¹⁰, Chuyu Yun ¹⁰, Chuan Ye ¹²³, Jinkun Xu ¹²³, Weike Shaoyong ¹²³, Lijun Zhang ⁹, Pan Shang ⁵⁶, Xi Luo ¹²³, Zhiwei Zhang ¹²³, Xin Zheng ⁹, Xueying Sha ⁹, Jinxin Zhang ¹²³, Shaoping Nie ⁴, Xuguang Zhang ¹¹, Fazheng Ren ¹², Huiying Liu ¹²³, Erdan Dong ⁸¹³¹⁴, Xiao Yu ⁹, Linong Ji ⁷, Yanli Pang ¹¹⁵¹⁶, Jin-Peng Sun ⁵⁶, Changtao Jiang ^1231719

¹Department of Immunology, School of Basic Medical Sciences, State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Third Hospital, Peking University, Beijing, China²NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China³Department of Physiology and Pathophysiology, Center for Obesity and Metabolic Disease Research, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100191, China⁴State Key Laboratory of Food Science and Resources, China-Canada Joint Lab of Food Science and Technology, Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China⁵Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shandong University, Jinan, China⁶Advanced Medical Research Institute, Meili Lake Translational Research Park, Cheeloo College of Medicine, Shandong University, Jinan, China⁷Department of Endocrinology and Metabolism, Peking University People’s Hospital, Peking University Diabetes Centre, Beijing, China⁸Research Center for Cardiopulmonary Rehabilitation, University of Health and Rehabilitation Sciences Qingdao Hospital (Qingdao Municipal Hospital), School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, China⁹Key Laboratory Experimental Teratology of the Ministry of Education and Department of Physiology, School of Basic Medical Sciences, Shandong University, Jinan, China¹⁰Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China¹¹Shanghai Institute of Nutrition and Health, The Chinese Academy of Sciences, Shanghai, China¹²Department of Nutrition and Health, Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, No. 10 Tianxiu Road, Haidian District, Beijing 100193, China¹³The Institute of Cardiovascular Sciences, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Health Science Center, Peking University, Beijing, China¹⁴Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China¹⁵National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, China¹⁶Beijing Advanced Center of Cellular Homeostasis and Aging-Related Diseases, Institute of Advanced Clinical Medicine, Peking University, Beijing, China¹⁷Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China

Received 25 February 2024, Revised 2 October 2024, Accepted 8 May 2025, Available online 29 May 2025.

https://doi.org/10.1016/j.cell.2025.05.010

Highlights

• Revealed microbiota-host interaction via microbial amino-acid-conjugated bile acids
• Trp-CA serves as the endogenous ligand of the orphan GPCR MRGPRE
• Identified a non-itch function of the itch family receptor MRGPRE in glucose control
• MRGPRE activation boosts GLP-1 secretion via the Gs-cAMP and β-arrestin-1-ALDOA pathways

Summary

Recently, microbial amino-acid-conjugated bile acids (MABAs) have been found to be prevalent in human samples. However, their physiological significance is still unclear. Here, we identify tryptophan-conjugated cholic acid (Trp-CA) as the most significantly decreased MABA in patients with type 2 diabetes (T2D), and its abundance is negatively correlated with clinical glycemic markers. We further demonstrate that Trp-CA improves glucose tolerance in diabetic mice. Mechanistically, we find that Trp-CA is a ligand of the orphan G protein-coupled receptor (GPCR) Mas-related G protein-coupled receptor family member E (MRGPRE) and determine the binding mode between the two. Both MRGPRE-Gs-cyclic AMP (cAMP) and MRGPRE-β-arrestin-1-aldolase A (ALDOA) signaling pathways contribute to the metabolic benefits of Trp-CA. Additionally, we find that the bacterial bile salt hydrolase/transferase of Bifidobacterium is responsible for the production of Trp-CA. Together, our findings pave the way for further research on MABAs and offer additional therapeutic targets for the treatment of T2D.

TPepPro: a deep learning model for predicting peptide–protein interactions

Xiaohong Jin, Zimeng Chen, Dan Yu, Qianhui Jiang, Zhuobin Chen, Bin Yan, Jing Qin, Yong Liu, Junwen Wang 

Bioinformatics, Volume 41, Issue 1, January 2025, btae708, https://doi.org/10.1093/bioinformatics/btae708

Published:

25 November 2024

 Article history

Abstract

Motivation

Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates by the FDA of USA. To identify the most potential peptides, study on peptide-protein interactions (PepPIs) presents a very important approach but poses considerable technical challenges. In experimental aspects, the transient nature of PepPIs and the high flexibility of peptides contribute to elevated costs and inefficiency. Traditional docking and molecular dynamics simulation methods require substantial computational resources, and the predictive accuracy of their results remain unsatisfactory.

Results

To address this gap, we proposed TPepPro, a Transformer-based model for PepPI prediction. We trained TPepPro on a dataset of 19,187 pairs of peptide-protein complexes with both sequential and structural features. TPepPro utilizes a strategy that combines local protein sequence feature extraction with global protein structure feature extraction. Moreover, TPepPro optimizes the architecture of structural featuring neural network in BN-ReLU arrangement, which notably reduced the amount of computing resources required for PepPIs prediction. According to comparison analysis, the accuracy reached 0.855 in TPepPro, achieving an 8.1% improvement compared to the second-best model TAGPPI. TPepPro achieved an AUC of 0.922, surpassing the second-best model TAGPPI with 0.844. Moreover, the newly developed TPepPro identify certain PepPIs that can be validated according to previous experimental evidence, thus indicating the efficiency of TPepPro to detect high potential PepPIs that would be helpful for amino acid drug applications.

Availability and implementation

The source code of TPepPro is available at https://github.com/wanglabhku/TPepPro.

Scratching promotes allergic inflammation and host defense via neurogenic mast cell activation

Andrew W. Liu^1,2, Youran R. Zhang^1,2, Chien-Sin Chen^1,2, Tara N. Edwards^1,2, Sumeyye Ozyaman^1,2†,
Torben Ramcke^1,2, Lindsay M. McKendrick^1,2, Eric S. Weiss^1,2, Jacob E. Gillis^1,2, Colin R. Laughlin²‡,
Simran K. Randhawa², Catherine M. Phelps², Kazuo Kurihara^1,2, Hannah M. Kang^1,2,
Sydney-Lam N. Nguyen^1,2, Jiwon Kim3, Tayler D. Sheahan3§, Sarah E. Ross3,4, Marlies Meisel^2,5,
Tina L. Sumpter^1,2, Daniel H. Kaplan^1,2*

¹Department of Dermatology, University of Pittsburgh, Pittsburgh, PA, USA. ²Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA. ³Department of Anesthesiology, University of Pittsburgh, Pittsburgh, PA, USA. ⁴Pittsburgh Center for Pain Research, Pittsburgh, PA, USA. ⁵Cancer Immunology and Immunotherapy Program, UPMC Hillman Cancer Center, Pittsburgh, PA, USA.
*Corresponding author. Email: dankaplan@pitt.edu
†Present address: Department of Histology and Embryology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey.
‡Present address: Department of Immunobiology, Yale University, New Haven, CT, USA.
§Present address: Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA.

Editor’s summary

Itch, the sensation that stimulates scratching behavior, is often triggered by skin irritants and inflammation. Liu et al. found that ablating itch-sensing neurons or physically preventing scratching decreased the inflammation associated with antigen-dependent mast cell responses in response to chemicals that induce allergic immune responses (see the Perspective by Ver Heul). Scratching promoted pain-sensing neurons to release a neuropeptide that stimulated mast cells, and this peptide hormone synergized with antigen-dependent activation to increase the mast cell’s degranulation and ability to produce inflammatory mediators. In a model of skin infection associated with antigen-specific mast cell responses, scratching contributed to decreasing the bacterial load. —Sarah H. Ross

Divergent sensory pathways of sneezing and coughing

Haowu Jiang ¹⁴, Huan Cui ¹⁴, Mengyu Chen ¹, Fengxian Li ¹, Xiaolei Shen ¹, Changxiong J. Guo ¹, George E. Hoekel ¹, Yuyan Zhu ², Liang Han ², Kangyun Wu ³, Michael J. Holtzman ³, Qin Liu ¹⁵

¹Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA

²The School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA

³Pulmonary and Critical Care Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, USA

4These authors contributed equally

https://doi.org/10.1016/j.cell.2024.08.009

Highlights

• Sneezing and coughing are mediated by distinct sensory populations
• Nasal MrgprC11-expressing sensory neurons serve as a core “sneeze” population
• Airway SST-expressing sensory neurons mediate chemically induced cough
• Sneezing and coughing are transmitted and modulated by divergent neuropathways

Summary

Sneezing and coughing are primary symptoms of many respiratory viral infections and allergies. It is generally assumed that sneezing and coughing involve common sensory receptors and molecular neurotransmission mechanisms. Here, we show that the nasal mucosa is innervated by several discrete populations of sensory neurons, but only one population (MrgprC11⁺MrgprA3⁻) mediates sneezing responses to a multitude of nasal irritants, allergens, and viruses. Although this population also innervates the trachea, it does not mediate coughing, as revealed by our newly established cough model. Instead, a distinct sensory population (somatostatin [SST⁺]) mediates coughing but not sneezing, unraveling an unforeseen sensory difference between sneezing and coughing. At the circuit level, sneeze and cough signals are transmitted and modulated by divergent neuropathways. Together, our study reveals the difference in sensory receptors and neurotransmission/modulation mechanisms between sneezing and coughing, offering neuronal drug targets for symptom management in respiratory viral infections and allergies.

Sensory neuronal STAT3 is critical for IL-31 receptor expression and inflammatory itch

Sonoko Takahashi ¹¹³, Sotaro Ochiai ¹¹⁰¹³ Jianshi Jin ²¹¹, Noriko Takahashi ¹, Susumu Toshima ¹³, Harumichi Ishigame ¹¹², Kenji Kabashima ⁴⁵, Masato Kubo ⁶⁷, Manabu Nakayama ⁸, Katsuyuki Shiroguchi ², Takaharu Okada ¹⁹¹⁴

https://doi.org/10.1016/j.celrep.2023.113433

Highlights

• Sensory neuronal IL-31RA and STAT3 are essential for IL-31-induced itch
• STAT3 is important for expression and downstream signaling of IL-31 receptor
• IL-31 enhances GPCR-induced itch transmitted by multiple sensory neuronal subsets
• Sensory neuronal STAT3 contributes to IL-31-independent inflammatory itch

Summary

IL-31 receptor blockade suppresses pruritus of atopic dermatitis. However, cell-type-specific contributions of IL-31 receptor to itch, its expression mechanism, and the downstream signaling pathway to induce itch remain unknown. Here, using conditional knockout mice, we demonstrate that IL-31-induced itch requires sensory neuronal IL-31 receptor and STAT3. We find that IL-31 receptor expression is dependent on STAT3 in sensory neurons. In addition, pharmacological experiments suggest that STAT3 activation is important for the itch-inducing signaling downstream of the IL-31 receptor. A cutaneous IL-31 injection induces the nuclear accumulation of activated STAT3 first in sensory neurons that abundantly express IL-31 receptor and then in other itch-transmitting neurons. IL-31 enhances itch induced by various pruritogens including even chloroquine. Finally, pruritus associated with dermatitis is partially dependent on sensory neuronal IL-31 receptor and strongly on sensory neuronal STAT3. Thus, sensory neuronal STAT3 is essential for IL-31-induced itch and further contributes to IL-31-independent inflammatory itch.

Comprehensive Evaluation of Fourteen Docking Programs on Protein-Peptide Complexes

Gaoqi Weng ¹, Junbo Gao ¹, Zhe Wang ¹, Ercheng Wang ¹, Xueping Hu ¹, Xiaojun Yao ², Dongsheng Cao ³, Tingjun Hou ^1 4

Affiliations expand

• PMID: 32324992
• DOI: 10.1021/acs.jctc.9b01208

Abstract

A large number of protein-protein interactions (PPIs) are mediated by the interactions between proteins and peptide segments binding partners, and therefore determination of protein-peptide interactions (PpIs) is quite crucial to elucidate important biological processes and design peptides or peptidomimetic drugs that can modulate PPIs. Nowadays, as a powerful computation tool, molecular docking has been widely utilized to predict the binding structures of protein-peptide complexes. However, although a number of docking programs have been available, the systematic study on the assessment of their performance for PpIs has never been reported. In this study, a benchmark data set called PepSet consisting of 185 protein-peptide complexes with peptide length ranging from 5 to 20 residues was employed to evaluate the performance of 14 docking programs, including three protein-protein docking programs (ZDOCK, FRODOCK, and HawkDock), three small molecule docking programs (GOLD, Surflex-Dock, and AutoDock Vina), and eight protein-peptide docking programs (GalaxyPepDock, MDockPeP, HPEPDOCK, CABS-dock, pepATTRACT, DINC, AutoDock CrankPep (ADCP), and HADDOCK peptide docking). A new evaluation parameter, named IL_RMSD, was proposed to measure the docking accuracy with f_nat (the fraction of native contacts). In global docking, HPEPDOCK performs the best for the entire data set and yields the success rates of 4.3%, 24.3%, and 55.7% at the top 1, 10, and 100 levels, respectively. In local docking, overall, ADCP achieves the best predictions and reaches the success rates of 11.9%, 37.3%, and 70.3% at the top 1, 10, and 100 levels, respectively. It is expected that our work can provide some helpful insights into the selection and development of improved docking programs for PpIs. The benchmark data set is freely available at http://cadd.zju.edu.cn/pepset/.

Molecular determinants for the chemical activation of the warmth-sensitive TRPV3 channel by the natural monoterpenoid carvacrol

Canyang Niu ¹, Xiaoying Sun ², Fang Hu ², Xiaowen Tang ³, KeWei Wang ⁴

Affiliations expand

• PMID: 35150742
• PMCID: PMC8920929
• DOI: 10.1016/j.jbc.2022.101706

Abstract

Transient receptor potential vanilloid 3 (TRPV3), robustly expressed in the skin, is a nonselective calcium-permeable cation channel activated by warm temperature, voltage, and certain chemicals. Natural monoterpenoid carvacrol from plant oregano is a known skin sensitizer or allergen that specifically activates TRPV3 channel. However, how carvacrol activates TRPV3 mechanistically remains to be understood. Here, we describe the molecular determinants for chemical activation of TRPV3 by the agonist carvacrol. Patch clamp recordings reveal that carvacrol activates TRPV3 in a concentration-dependent manner, with an EC₅₀ of 0.2 mM, by increasing the probability of single-channel open conformation. Molecular docking of carvacrol into cryo-EM structure of TRPV3 combined with site-directed mutagenesis further identified a unique binding pocket formed by the channel S2-S3 linker important for mediating this interaction. Within the binding pocket consisting of four residues (Ile505, Leu508, Arg509, and Asp512), we report that Leu508 is the most critical residue for the activation of TRPV3 by carvacrol, but not 2-APB, a widely used nonspecific agonist and TRP channel modulator. Our findings demonstrate a direct binding of carvacrol to TRPV3 by targeting the channel S2-S3 linker that serves as a critical domain for chemical-mediated activation of TRPV3. We also propose that carvacrol can function as a molecular tool in the design of novel specific TRPV3 modulators for the further understanding of TRPV3 channel pharmacology.

Keywords: 2-APB; TRPV3; amino acid mutation; carvacrol; molecular docking; surface structure.

Vitexin inhibits pain and itch behavior via modulating TRPV4 activity in mice

Zhiqiang Qin ^a1, Lan Xiang ^a1, Siyu Zheng ^a1, Yuchen Zhao ^b, Yanyan Qin ^a, Lei Zhang ^a, Lanlan Zhou ^a

^aSchool of Medical Technology and Nursing, Shenzhen Polytechnic, Shenzhen 518055, China ^bDepartment of Mathematics, University of California, Los Angeles, CA 90095, USA Received 6 March 2023, Revised 27 June 2023, Accepted 28 June 2023, Available online 3 July 2023, Version of Record 3 July 2023.

https://doi.org/10.1016/j.biopha.2023.115101

Abstract
Itching and pain are distinct unpleasant sensations. The transient receptor potential cation channel subfamily V member 4 (TRPV4) pathway is regarded as a shared pathway that mediates pain and itching. Vitexin (Mujingsu, MJS), a C-glycosylflavonoid, is an effective analgesic. This study aimed to explore the antinociceptive and anti-pruritic effects of MJS and whether its effects are mediated via the TRPV4 pathway. Mice were treated with MJS (7.5 mg/kg) 0.5 h prior to the initiation of the pain or itch modeling process. The results showed that MJS suppressed pain-like behavior in hot plate, thermal infiltration, glacial acetic acid twisting, and formalin tests. Administration of MJS decreased the pruritus response induced by histamine, C48/80, chloroquine and BAM8-22 within 30 min. MJS reduced scratching bouts and lessened the wiping reaction of mice under TRPV4 activation by GSK101 (10 µg/5 μl). MJS inhibited scratching behavior in acetone–ether–water (AEW)-treated mice within 60 min. An H1 receptor antagonist—chlorpheniramine (CLP, 400 mg/kg)—and a TRPV4 antagonist—HC067047 (250 ng/kg), exhibited similar effects to those of MJS. Moreover, MJS ameliorated dry skin itch-associated cutaneous barrier disruption in mice. MJS did not inhibit the expression of TRPV4 in the dorsal root ganglion neurons at L2–L3 in AEW mice. These results indicate that the analgesic and anti-pruritic effects of MJS in acute and chronic pain and itching, as well as itching caused by TRPV4 activation, could be attributed to the TRPV4 pathway modulation.

Propionate alleviates itch in murine models of atopic dermatitis by modulating sensory TRP channels of dorsal root ganglion

Yao Xu, Zhuoqiong Qiu, Chaoying Gu, Su Yu, Shangshang Wang, Changlin Li, Xu Yao, Wei Li

First published: 02 January 2024

https://doi.org/10.1111/all.1599

Interpretable bilinear attention network with domain adaptation improves drug–target prediction

Peizhen Bai, Filip Miljković, Bino John & Haiping Lu

Abstract

Predicting drug–target interaction is key for drug discovery. Recent deep learning-based methods show promising performance, but two challenges remain: how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation and how to optimize generalization performance of predictions on novel drug–target pairs. Here, we present DrugBAN, a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn pairwise local interactions between drugs and targets, and adapt in response to out-of-distribution data. DrugBAN works on drug molecular graphs and target protein sequences to perform prediction, with conditional domain adversarial learning to align learned interaction representations across different distributions for better generalization on novel drug–target pairs. Experiments on three benchmark datasets under both in-domain and cross-domain settings show that DrugBAN achieves the best overall performance against five state-of-the-art baseline models. Moreover, visualizing the learned bilinear attention map provides interpretable insights from prediction results.