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Wang L, Zeng Z, Xue Z, Wang Y. DeepNeuropePred: A robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model. Comput Struct Biotechnol J 2024; 23:309-315. [PMID: 38179071 PMCID: PMC10764246 DOI: 10.1016/j.csbj.2023.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 01/06/2024] Open
Abstract
Neuropeptides play critical roles in many biological processes such as growth, learning, memory, metabolism, and neuronal differentiation. A few approaches have been reported for predicting neuropeptides that are cleaved from precursor protein sequences. However, these models for cleavage site prediction of precursors were developed using a limited number of neuropeptide precursor datasets and simple precursors representation models. In addition, a universal method for predicting neuropeptide cleavage sites that can be applied to all species is still lacking. In this paper, we proposed a novel deep learning method called DeepNeuropePred, using a combination of pre-trained language model and Convolutional Neural Networks for feature extraction and predicting the neuropeptide cleavage sites from precursors. To demonstrate the model's effectiveness and robustness, we evaluated the performance of DeepNeuropePred and four models from the NeuroPred server in the independent dataset and our model achieved the highest AUC score (0.916), which are 6.9%, 7.8%, 8.8%, and 10.9% higher than Mammalian (0.857), insects (0.850), Mollusc (0.842) and Motif (0.826), respectively. For the convenience of researchers, we provide a web server (http://isyslab.info/NeuroPepV2/deepNeuropePred.jsp).
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Affiliation(s)
- Lei Wang
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong 264003, China
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Zilu Zeng
- Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430010, China
| | - Zhidong Xue
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong 264003, China
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yan Wang
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong 264003, China
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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2
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Liang Y, Cao M, Zhang S. NeuroPred-ResSE: Predicting neuropeptides by integrating residual block and squeeze-excitation attention mechanism. Anal Biochem 2024; 695:115648. [PMID: 39154878 DOI: 10.1016/j.ab.2024.115648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/31/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
Neuropeptides play crucial roles in regulating neurological function acting as signaling molecules, which provide new opportunity for developing drugs for the treatment of neurological diseases. Therefore, it is very necessary to develop a rapid and accurate prediction model for neuropeptides. Although a few prediction tools have been developed, there is room for improvement in prediction accuracy by using deep learning approach. In this paper, we establish the NeuroPred-ResSE model based on residual block and squeeze-excitation attention mechanism. Firstly, we extract multi-features by using one-hot coding based on the NT5CT5 sequence, dipeptide deviation from expected mean and natural vector. Then, we integrate residual block and squeeze-excitation attention mechanism, which can capture and identify the most relevant attribute features. Finally, the accuracies of the training set and test set are 97.16 % and 96.60 % based on the 5-fold cross-validation and independent test, respectively, and other evaluation metrics have also obtained satisfactory results. The experimental results show that the performance of the NeuroPred-ResSE model outperforms those of existing state-of-the-art models, and our model is an effective, intelligent and robust prediction tool. The datasets and source codes are available at https://github.com/yunyunliang88/NeuroPred-ResSE.
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Affiliation(s)
- Yunyun Liang
- School of Science, Xi'an Polytechnic University, Xi'an, 710048, PR China.
| | - Mengyi Cao
- School of Science, Xi'an Polytechnic University, Xi'an, 710048, PR China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
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3
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Li H, Jiang L, Yang K, Shang S, Li M, Lv Z. iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features. Int J Mol Sci 2024; 25:7049. [PMID: 39000158 PMCID: PMC11240975 DOI: 10.3390/ijms25137049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 06/17/2024] [Accepted: 06/25/2024] [Indexed: 07/16/2024] Open
Abstract
Neuropeptides are biomolecules with crucial physiological functions. Accurate identification of neuropeptides is essential for understanding nervous system regulatory mechanisms. However, traditional analysis methods are expensive and laborious, and the development of effective machine learning models continues to be a subject of current research. Hence, in this research, we constructed an SVM-based machine learning neuropeptide predictor, iNP_ESM, by integrating protein language models Evolutionary Scale Modeling (ESM) and Unified Representation (UniRep) for the first time. Our model utilized feature fusion and feature selection strategies to improve prediction accuracy during optimization. In addition, we validated the effectiveness of the optimization strategy with UMAP (Uniform Manifold Approximation and Projection) visualization. iNP_ESM outperforms existing models on a variety of machine learning evaluation metrics, with an accuracy of up to 0.937 in cross-validation and 0.928 in independent testing, demonstrating optimal neuropeptide recognition capabilities. We anticipate improved neuropeptide data in the future, and we believe that the iNP_ESM model will have broader applications in the research and clinical treatment of neurological diseases.
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Affiliation(s)
- Honghao Li
- College of Biomedical Engineering, Sichuan University, Chengdu 610041, China
| | - Liangzhen Jiang
- College of Food and Biological Engineering, Chengdu University, Chengdu 610106, China
- Country Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Chengdu 610106, China
| | - Kaixiang Yang
- College of Software Engineering, Sichuan University, Chengdu 610041, China
| | - Shulin Shang
- College of Biomedical Engineering, Sichuan University, Chengdu 610041, China
| | - Mingxin Li
- College of Biomedical Engineering, Sichuan University, Chengdu 610041, China
| | - Zhibin Lv
- College of Biomedical Engineering, Sichuan University, Chengdu 610041, China
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Iglesias V, Bárcenas O, Pintado-Grima C, Burdukiewicz M, Ventura S. Structural information in therapeutic peptides: Emerging applications in biomedicine. FEBS Open Bio 2024. [PMID: 38877295 DOI: 10.1002/2211-5463.13847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/08/2024] [Accepted: 05/27/2024] [Indexed: 06/16/2024] Open
Abstract
Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.
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Affiliation(s)
- Valentín Iglesias
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Oriol Bárcenas
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Institute of Advanced Chemistry of Catalonia (IQAC), CSIC, Barcelona, Spain
| | - Carlos Pintado-Grima
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Michał Burdukiewicz
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Białystok, Poland
| | - Salvador Ventura
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
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Singh V, Singh SK, Sharma R. A novel framework based on explainable AI and genetic algorithms for designing neurological medicines. Sci Rep 2024; 14:12807. [PMID: 38834718 DOI: 10.1038/s41598-024-63561-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024] Open
Abstract
The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences.
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Affiliation(s)
- Vishakha Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India.
| | - Sanjay Kumar Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India.
| | - Ritesh Sharma
- Department of ICT, Manipal Institute of Technology, Manipal, 576104, Uttar Pradesh, India
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Chen Q, Xu N, Zhao C, He Y, Kam SHT, Wu X, Huang P, Yang M, Wong CTT, Radis-Baptista G, Tang B, Fan G, Gong G, Lee SMY. A new invertebrate NPY-like polypeptide, ZoaNPY, from the Zoanthus sociatus, as a novel ligand of human NPY Y2 receptor rescues vascular insufficiency via PLC/PKC and Src- FAK-dependent signaling pathways. Pharmacol Res 2024; 203:107173. [PMID: 38580186 DOI: 10.1016/j.phrs.2024.107173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/23/2024] [Accepted: 04/02/2024] [Indexed: 04/07/2024]
Abstract
Our recent multi-omics studies have revealed rich sources of novel bioactive proteins and polypeptides from marine organisms including cnidarians. In the present study, we initially conducted a transcriptomic analysis to review the composition profile of polypeptides from Zoanthus sociatus. Then, a newly discovered NPY-like polypeptide-ZoaNPY was selected for further in silico structural, binding and virtually pharmacological studies. To evaluate the pro-angiogenic effects of ZoaNPY, we employed an in vitro HUVECs model and an in vivo zebrafish model. Our results indicate that ZoaNPY, at 1-100 pmol, enhances cell survival, migration and tube formation in the endothelial cells. Besides, treatment with ZoaNPY could restore a chemically-induced vascular insufficiency in zebrafish embryos. Western blot results demonstrated the application of ZoaNPY could increase the phosphorylation of proteins related to angiogenesis signaling including PKC, PLC, FAK, Src, Akt, mTOR, MEK, and ERK1/2. Furthermore, through molecular docking and surface plasmon resonance (SPR) verification, ZoaNPY was shown to directly and physically interact with NPY Y2 receptor. In view of this, all evidence showed that the pro-angiogenic effects of ZoaNPY involve the activation of NPY Y2 receptor, thereby activating the Akt/mTOR, PLC/PKC, ERK/MEK and Src- FAK-dependent signaling pathways. Furthermore, in an excision wound model, the treatment with ZoaNPY was shown to accelerate the wound healing process in mice. Our findings provide new insights into the discovery and development of novel pro-angiogenic drugs derived from NPY-like polypeptides in the future.
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Affiliation(s)
- Qian Chen
- Center for Evolution and Conservation Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China; State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, 999078, Macao
| | - Nan Xu
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, 999078, Macao
| | - Chen Zhao
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, 999078, Macao
| | - Yulin He
- Department of Food Science and Nutrition, The Hong Kong Polytechnic University, Hung Hom 999077, Hong Kong, SAR China
| | - Sandy Hio Tong Kam
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, 999078, Macao
| | - Xue Wu
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, 999078, Macao
| | - Pan Huang
- Kunming Institute of Zoology, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Min Yang
- Kunming Institute of Zoology, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Clarence Tsun Ting Wong
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, 999077, Hong Kong, SAR China
| | | | - Benqin Tang
- Department of Food Science and Nutrition, The Hong Kong Polytechnic University, Hung Hom 999077, Hong Kong, SAR China; Research Centre for Chinese Medicine Innovation, The Hong Kong Polytechnic University, Hung Hom 999077, Hong Kong, SAR China
| | - Guangyi Fan
- BGI-Qingdao, BGI-Shenzhen, Qingdao 266555, China
| | - Guiyi Gong
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, 999078, Macao; Department of Food Science and Nutrition, The Hong Kong Polytechnic University, Hung Hom 999077, Hong Kong, SAR China; Research Centre for Chinese Medicine Innovation, The Hong Kong Polytechnic University, Hung Hom 999077, Hong Kong, SAR China.
| | - Simon Ming-Yuen Lee
- State Key Laboratory of Quality Research in Chinese Medicine and Institute of Chinese Medical Sciences, University of Macau, 999078, Macao; Department of Food Science and Nutrition, The Hong Kong Polytechnic University, Hung Hom 999077, Hong Kong, SAR China; Research Centre for Chinese Medicine Innovation, The Hong Kong Polytechnic University, Hung Hom 999077, Hong Kong, SAR China.
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Feng X, Zhan H, Sokol CL. Sensory neuronal control of skin barrier immunity. Trends Immunol 2024; 45:371-380. [PMID: 38653601 PMCID: PMC11102800 DOI: 10.1016/j.it.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 03/18/2024] [Accepted: 03/21/2024] [Indexed: 04/25/2024]
Abstract
Peripheral sensory neurons recognize diverse noxious stimuli, including microbial products and allergens traditionally thought to be targets of the mammalian immune system. Activation of sensory neurons by these stimuli leads to pain and itch responses as well as the release of neuropeptides that interact with their cognate receptors expressed on immune cells, such as dendritic cells (DCs). Neuronal control of immune cell function through neuropeptide release not only affects local inflammatory responses but can impact adaptive immune responses through downstream effects on T cell priming. Numerous neuropeptide receptors are expressed by DCs but only a few have been characterized, presenting opportunities for further investigation of the pathways by which cutaneous neuroimmune interactions modulate host immunity.
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Affiliation(s)
- Xinyi Feng
- Center for Immunology & Inflammatory Diseases, Division of Rheumatology, Allergy & Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Haoting Zhan
- Center for Immunology & Inflammatory Diseases, Division of Rheumatology, Allergy & Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA; Department of Clinical Laboratory, State Key Laboratory of Complex, Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Caroline L Sokol
- Center for Immunology & Inflammatory Diseases, Division of Rheumatology, Allergy & Immunology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
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Iwaniak A, Minkiewicz P, Darewicz M. Bioinformatics and bioactive peptides from foods: Do they work together? ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 108:35-111. [PMID: 38461003 DOI: 10.1016/bs.afnr.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2024]
Abstract
We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.
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Affiliation(s)
- Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland.
| | - Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
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Wang M, Wang L, Xu W, Chu Z, Wang H, Lu J, Xue Z, Wang Y. NeuroPep 2.0: An Updated Database Dedicated to Neuropeptide and Its Receptor Annotations. J Mol Biol 2024; 436:168416. [PMID: 38143020 DOI: 10.1016/j.jmb.2023.168416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 12/26/2023]
Abstract
Neuropeptides not only work through nervous system but some of them also work peripherally to regulate numerous physiological processes. They are important in regulation of numerous physiological processes including growth, reproduction, social behavior, inflammation, fluid homeostasis, cardiovascular function, and energy homeostasis. The various roles of neuropeptides make them promising candidates for prospective therapeutics of different diseases. Currently, NeuroPep has been updated to version 2.0, it now holds 11,417 unique neuropeptide entries, which is nearly double of the first version of NeuroPep. When available, we collected information about the receptor for each neuropeptide entry and predicted the 3D structures of those neuropeptides without known experimental structure using AlphaFold2 or APPTEST according to the peptide sequence length. In addition, DeepNeuropePred and NeuroPred-PLM, two neuropeptide prediction tools developed by us recently, were also integrated into NeuroPep 2.0 to help to facilitate the identification of new neuropeptides. NeuroPep 2.0 is freely accessible at https://isyslab.info/NeuroPepV2/.
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Affiliation(s)
- Mingxia Wang
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Lei Wang
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Wei Xu
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Ziqiang Chu
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Hengzhi Wang
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jingxiang Lu
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Zhidong Xue
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong 264003, China; School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yan Wang
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong 264003, China; School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
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Örüm D, Korkmaz S, İlhan N, Örüm MH, Atmaca M. Leptin, Nesfatin-1, Orexin-A, and Total Ghrelin Levels in Drug-Naive Panic Disorder. Psychiatry Investig 2024; 21:142-150. [PMID: 38433413 PMCID: PMC10910167 DOI: 10.30773/pi.2023.0309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/06/2023] [Accepted: 11/09/2023] [Indexed: 03/05/2024] Open
Abstract
OBJECTIVE This study aimed to examine the changes in serum nesfatin-1, leptin, orexin-A, and total ghrelin levels of patients diagnosed with drug-naive panic disorder (PD) before and after six weeks of the treatment and to compare the findings with the healthy subjects. METHODS The neuropeptides were measured in venous blood samples taken from 32 patients and 32 healthy subjects. The blood samples of the patients who used paroxetine 20 mg/day plus alprazolam 0.5 mg/day were retaken again after six weeks. Measurements were performed with the Enzyme-Linked Immunosorbent Assay (ELISA) method. RESULTS Serum nesfatin-1, leptin, orexin-A and total ghrelin levels of the patient group were found to be significantly lower than the control group (p<0.001, p<0.001, p<0.001, and p<0.001, respectively). When the serum nesfatin-1, leptin, orexin-A and total ghrelin levels of the patient group were compared before and after treatment, significant differences were found in terms of orexin-A and total ghrelin levels (p=0.046, p<0.001, respectively). However, no significant differences were found in terms of nesfatin-1and leptin levels (p=0.205, p=0.988, respectively). CONCLUSION This study reports that PD, like other anxiety disorders, may affect serum nesfatin-1, leptin, orexin-A, and total ghrelin levels, and there may be a relationship between PD treatment and the levels of these neuropeptides. The variability of this relationship among the neuropeptides examined indicates that various factors other than treatment play a role in this process.
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Affiliation(s)
- Dilek Örüm
- Psychiatry, Elazığ Fethi Sekin City Hospital, Elazığ, Turkey
| | - Sevda Korkmaz
- Department of Psychiatry, Firat University, Faculty of Medicine, Elazığ, Turkey
| | - Nevin İlhan
- Department of Medical Biochemistry, Firat University, Faculty of Medicine, Elazığ, Turkey
| | - Mehmet Hamdi Örüm
- Psychiatry, Elazığ Mental Health and Diseases Hospital, Elazığ, Turkey
| | - Murad Atmaca
- Department of Psychiatry, Firat University, Faculty of Medicine, Elazığ, Turkey
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11
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Su J, Huang F, Tian Y, Tian R, Qianqian G, Bello ST, Zeng D, Jendrichovsky P, Lau CG, Xiong W, Yu D, Tortorella M, Chen X, He J. Entorhinohippocampal cholecystokinin modulates spatial learning by facilitating neuroplasticity of hippocampal CA3-CA1 synapses. Cell Rep 2023; 42:113467. [PMID: 37979171 DOI: 10.1016/j.celrep.2023.113467] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 10/01/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023] Open
Abstract
The hippocampus is broadly impacted by neuromodulations. However, how neuropeptides shape the function of the hippocampus and the related spatial learning and memory remains unclear. Here, we discover the crucial role of cholecystokinin (CCK) in heterosynaptic neuromodulation from the medial entorhinal cortex (MEC) to the hippocampus. Systematic knockout of the CCK gene impairs CA3-CA1 LTP and space-related performance. The MEC provides most of the CCK-positive neurons projecting to the hippocampal region, which potentiates CA3-CA1 long-term plasticity heterosynaptically in a frequency- and NMDA receptor (NMDAR)-dependent manner. Selective inhibition of MEC CCKergic neurons or downregulation of their CCK mRNA levels also impairs CA3-CA1 LTP formation and animals' performance in the water maze. This excitatory extrahippocampal projection releases CCK upon high-frequency excitation and is active during animal exploration. Our results reveal the critical role of entorhinal CCKergic projections in bridging intra- and extrahippocampal circuitry at electrophysiological and behavioral levels.
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Affiliation(s)
- Junfeng Su
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China
| | - Fengwen Huang
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China; Centre for Regenerative Medicine and Health, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong SAR, P.R. China.
| | - Yu Tian
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China
| | - Ran Tian
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China
| | - Gao Qianqian
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China
| | - Stephen Temitayo Bello
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China; Centre for Regenerative Medicine and Health, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong SAR, P.R. China
| | - Dingxaun Zeng
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China
| | - Peter Jendrichovsky
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China
| | - C Geoffrey Lau
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China
| | - Wenjun Xiong
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China; City University of Hong Kong Shenzhen Research Institute, Shenzhen, P.R. China
| | - Daiguan Yu
- Key Laboratory of Regenerative Biology, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, P.R. China
| | - Micky Tortorella
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong SAR, P.R. China; Key Laboratory of Regenerative Biology, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, P.R. China
| | - Xi Chen
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China; City University of Hong Kong Shenzhen Research Institute, Shenzhen, P.R. China.
| | - Jufang He
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China; Department of Biomedical Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, P.R. China; City University of Hong Kong Shenzhen Research Institute, Shenzhen, P.R. China.
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12
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Pintado-Grima C, Bárcenas O, Iglesias V, Santos J, Manglano-Artuñedo Z, Pallarès I, Burdukiewicz M, Ventura S. aSynPEP-DB: a database of biogenic peptides for inhibiting α-synuclein aggregation. Database (Oxford) 2023; 2023:baad084. [PMID: 38011719 PMCID: PMC10681447 DOI: 10.1093/database/baad084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/13/2023] [Accepted: 11/14/2023] [Indexed: 11/29/2023]
Abstract
Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, yet effective treatments able to stop or delay disease progression remain elusive. The aggregation of a presynaptic protein, α-synuclein (aSyn), is the primary neurological hallmark of PD and, thus, a promising target for therapeutic intervention. However, the lack of consensus on the molecular properties required to specifically bind the toxic species formed during aSyn aggregation has hindered the development of therapeutic molecules. Recently, we defined and experimentally validated a peptide architecture that demonstrated high affinity and selectivity in binding to aSyn toxic oligomers and fibrils, effectively preventing aSyn pathogenic aggregation. Human peptides with such properties may have neuroprotective activities and hold a huge therapeutic interest. Driven by this idea, here, we developed a discriminative algorithm for the screening of human endogenous neuropeptides, antimicrobial peptides and diet-derived bioactive peptides with the potential to inhibit aSyn aggregation. We identified over 100 unique biogenic peptide candidates and ensembled a comprehensive database (aSynPEP-DB) that collects their physicochemical features, source datasets and additional therapeutic-relevant information, including their sites of expression and associated pathways. Besides, we provide access to the discriminative algorithm to extend its application to the screening of artificial peptides or new peptide datasets. aSynPEP-DB is a unique repository of peptides with the potential to modulate aSyn aggregation, serving as a platform for the identification of previously unexplored therapeutic agents. Database URL: https://asynpepdb.ppmclab.com/.
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Affiliation(s)
- Carlos Pintado-Grima
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Oriol Bárcenas
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Valentín Iglesias
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Jaime Santos
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Center for Molecular Biology of Heidelberg University (ZMBH), Heidelberg 69120, Germany
| | - Zoe Manglano-Artuñedo
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Irantzu Pallarès
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Michał Burdukiewicz
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Clinical Research Centre, Medical University of Białystok, Kilińskiego 1, Białystok 15-369, Poland
| | - Salvador Ventura
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
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13
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Berndt A, Cai D, Cohen A, Juarez B, Iglesias JT, Xiong H, Qin Z, Tian L, Slesinger PA. Current Status and Future Strategies for Advancing Functional Circuit Mapping In Vivo. J Neurosci 2023; 43:7587-7598. [PMID: 37940594 PMCID: PMC10634581 DOI: 10.1523/jneurosci.1391-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 11/10/2023] Open
Abstract
The human brain represents one of the most complex biological systems, containing billions of neurons interconnected through trillions of synapses. Inherent to the brain is a biochemical complexity involving ions, signaling molecules, and peptides that regulate neuronal activity and allow for short- and long-term adaptations. Large-scale and noninvasive imaging techniques, such as fMRI and EEG, have highlighted brain regions involved in specific functions and visualized connections between different brain areas. A major shortcoming, however, is the need for more information on specific cell types and neurotransmitters involved, as well as poor spatial and temporal resolution. Recent technologies have been advanced for neuronal circuit mapping and implemented in behaving model organisms to address this. Here, we highlight strategies for targeting specific neuronal subtypes, identifying, and releasing signaling molecules, controlling gene expression, and monitoring neuronal circuits in real-time in vivo Combined, these approaches allow us to establish direct causal links from genes and molecules to the systems level and ultimately to cognitive processes.
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Affiliation(s)
| | - Denise Cai
- Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | | | | | | | | | - Zhenpeng Qin
- University of Texas-Dallas, Richardson, TX 75080
| | - Lin Tian
- University of California-Davis, Davis, CA 95616
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14
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Fan KT, Hsu CW, Chen YR. Mass spectrometry in the discovery of peptides involved in intercellular communication: From targeted to untargeted peptidomics approaches. MASS SPECTROMETRY REVIEWS 2023; 42:2404-2425. [PMID: 35765846 DOI: 10.1002/mas.21789] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/17/2022] [Accepted: 04/08/2022] [Indexed: 06/15/2023]
Abstract
Endogenous peptide hormones represent an essential class of biomolecules, which regulate cell-cell communications in diverse physiological processes of organisms. Mass spectrometry (MS) has been developed to be a powerful technology for identifying and quantifying peptides in a highly efficient manner. However, it is difficult to directly identify these peptide hormones due to their diverse characteristics, dynamic regulations, low abundance, and existence in a complicated biological matrix. Here, we summarize and discuss the roles of targeted and untargeted MS in discovering peptide hormones using bioassay-guided purification, bioinformatics screening, or the peptidomics-based approach. Although the peptidomics approach is expected to discover novel peptide hormones unbiasedly, only a limited number of successful cases have been reported. The critical challenges and corresponding measures for peptidomics from the steps of sample preparation, peptide extraction, and separation to the MS data acquisition and analysis are also discussed. We also identify emerging technologies and methods that can be integrated into the discovery platform toward the comprehensive study of endogenous peptide hormones.
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Affiliation(s)
- Kai-Ting Fan
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Chia-Wei Hsu
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Yet-Ran Chen
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
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15
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Siletti K, Hodge R, Mossi Albiach A, Lee KW, Ding SL, Hu L, Lönnerberg P, Bakken T, Casper T, Clark M, Dee N, Gloe J, Hirschstein D, Shapovalova NV, Keene CD, Nyhus J, Tung H, Yanny AM, Arenas E, Lein ES, Linnarsson S. Transcriptomic diversity of cell types across the adult human brain. Science 2023; 382:eadd7046. [PMID: 37824663 DOI: 10.1126/science.add7046] [Citation(s) in RCA: 78] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 09/07/2023] [Indexed: 10/14/2023]
Abstract
The human brain directs complex behaviors, ranging from fine motor skills to abstract intelligence, but the diversity of cell types that support these skills has not been fully described. In this work, we used single-nucleus RNA sequencing to systematically survey cells across the entire adult human brain. We sampled more than three million nuclei from approximately 100 dissections across the forebrain, midbrain, and hindbrain in three postmortem donors. Our analysis identified 461 clusters and 3313 subclusters organized largely according to developmental origins and revealing high diversity in midbrain and hindbrain neurons. Astrocytes and oligodendrocyte-lineage cells also exhibited regional diversity at multiple scales. The transcriptomic census of the entire human brain presented in this work provides a resource for understanding the molecular diversity of the human brain in health and disease.
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Affiliation(s)
| | - Rebecca Hodge
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Ka Wai Lee
- Karolinska Institute, 171 77 Stockholm, Sweden
| | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lijuan Hu
- Karolinska Institute, 171 77 Stockholm, Sweden
| | | | - Trygve Bakken
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Michael Clark
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jessica Gloe
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Julie Nyhus
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
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16
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De La Toba EA, Anapindi KDB, Sweedler JV. Assessment and Comparison of Database Search Engines for Peptidomic Applications. J Proteome Res 2023; 22:3123-3134. [PMID: 36809008 PMCID: PMC10440370 DOI: 10.1021/acs.jproteome.2c00307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Protein database search engines are an integral component of mass spectrometry-based peptidomic analyses. Given the unique computational challenges of peptidomics, many factors must be taken into consideration when optimizing search engine selection, as each platform has different algorithms by which tandem mass spectra are scored for subsequent peptide identifications. In this study, four different database search engines, PEAKS, MS-GF+, OMSSA, and X! Tandem, were compared with Aplysia californica and Rattus norvegicus peptidomics data sets, and various metrics were assessed such as the number of unique peptide and neuropeptide identifications, and peptide length distributions. Given the tested conditions, PEAKS was found to have the highest number of peptide and neuropeptide identifications out of the four search engines in both data sets. Furthermore, principal component analysis and multivariate logistic regression were employed to determine whether specific spectral features contribute to false C-terminal amidation assignments by each search engine. From this analysis, it was found that the primary features influencing incorrect peptide assignments were the precursor and fragment ion m/z errors. Finally, an assessment employing a mixed species protein database was performed to evaluate search engine precision and sensitivity when searched against an enlarged search space containing human proteins.
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Affiliation(s)
- Eduardo A. De La Toba
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, 61801
- Department of Chemistry, University of Illinois at Urbana-Champaign, 61801
| | - Krishna D. B. Anapindi
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, 61801
- Department of Chemistry, University of Illinois at Urbana-Champaign, 61801
| | - Jonathan V. Sweedler
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, 61801
- Department of Chemistry, University of Illinois at Urbana-Champaign, 61801
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17
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Lamanna F, Hervas-Sotomayor F, Oel AP, Jandzik D, Sobrido-Cameán D, Santos-Durán GN, Martik ML, Stundl J, Green SA, Brüning T, Mößinger K, Schmidt J, Schneider C, Sepp M, Murat F, Smith JJ, Bronner ME, Rodicio MC, Barreiro-Iglesias A, Medeiros DM, Arendt D, Kaessmann H. A lamprey neural cell type atlas illuminates the origins of the vertebrate brain. Nat Ecol Evol 2023; 7:1714-1728. [PMID: 37710042 PMCID: PMC10555824 DOI: 10.1038/s41559-023-02170-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/18/2023] [Indexed: 09/16/2023]
Abstract
The vertebrate brain emerged more than ~500 million years ago in common evolutionary ancestors. To systematically trace its cellular and molecular origins, we established a spatially resolved cell type atlas of the entire brain of the sea lamprey-a jawless species whose phylogenetic position affords the reconstruction of ancestral vertebrate traits-based on extensive single-cell RNA-seq and in situ sequencing data. Comparisons of this atlas to neural data from the mouse and other jawed vertebrates unveiled various shared features that enabled the reconstruction of cell types, tissue structures and gene expression programs of the ancestral vertebrate brain. However, our analyses also revealed key tissues and cell types that arose later in evolution. For example, the ancestral brain was probably devoid of cerebellar cell types and oligodendrocytes (myelinating cells); our data suggest that the latter emerged from astrocyte-like evolutionary precursors in the jawed vertebrate lineage. Altogether, our work illuminates the cellular and molecular architecture of the ancestral vertebrate brain and provides a foundation for exploring its diversification during evolution.
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Affiliation(s)
- Francesco Lamanna
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany.
| | | | - A Phillip Oel
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - David Jandzik
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA
- Department of Zoology, Comenius University, Bratislava, Slovakia
| | - Daniel Sobrido-Cameán
- Department of Functional Biology, CIBUS, Faculty of Biology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Gabriel N Santos-Durán
- Department of Functional Biology, CIBUS, Faculty of Biology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Megan L Martik
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Department of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA, USA
| | - Jan Stundl
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Stephen A Green
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Thoomke Brüning
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Katharina Mößinger
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Julia Schmidt
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Celine Schneider
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Mari Sepp
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Florent Murat
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany
- INRAE, LPGP, Rennes, France
| | - Jeramiah J Smith
- Department of Biology, University of Kentucky, Lexington, KY, USA
| | - Marianne E Bronner
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - María Celina Rodicio
- Department of Functional Biology, CIBUS, Faculty of Biology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Antón Barreiro-Iglesias
- Department of Functional Biology, CIBUS, Faculty of Biology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Daniel M Medeiros
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Detlev Arendt
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Henrik Kaessmann
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, Heidelberg, Germany.
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18
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Li M, Larsen PA. Single-cell sequencing of entorhinal cortex reveals widespread disruption of neuropeptide networks in Alzheimer's disease. Alzheimers Dement 2023; 19:3575-3592. [PMID: 36825405 DOI: 10.1002/alz.12979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/12/2023] [Accepted: 01/15/2023] [Indexed: 02/25/2023]
Abstract
INTRODUCTION Abnormalities of neuropeptides (NPs) that play important roles in modulating neuronal activities are commonly observed in Alzheimer's disease (AD). We hypothesize that NP network disruption is widespread in AD brains. METHODS Single-cell transcriptomic data from the entorhinal cortex (EC) were used to investigate the NP network disruption in AD. Bulk RNA-sequencing data generated from the temporal cortex by independent groups and machine learning were employed to identify key NPs involved in AD. The relationship between aging and AD-associated NP (ADNP) expression was studied using GTEx data. RESULTS The proportion of cells expressing NPs but not their receptors decreased significantly in AD. Neurons expressing higher level and greater diversity of NPs were disproportionately absent in AD. Increased age coincides with decreased ADNP expression in the hippocampus. DISCUSSION NP network disruption is widespread in AD EC. Neurons expressing more NPs may be selectively vulnerable to AD. Decreased expression of NPs participates in early AD pathogenesis. We predict that the NP network can be harnessed for treatment and/or early diagnosis of AD.
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Affiliation(s)
- Manci Li
- Department of Veterinary and Biomedical Sciences, University of Minnesota, St. Paul, Minnesota, USA
- Minnesota Center for Prion Research and Outreach, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
| | - Peter A Larsen
- Department of Veterinary and Biomedical Sciences, University of Minnesota, St. Paul, Minnesota, USA
- Minnesota Center for Prion Research and Outreach, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA
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19
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Liu D, Lin Z, Jia C. NeuroCNN_GNB: an ensemble model to predict neuropeptides based on a convolution neural network and Gaussian naive Bayes. Front Genet 2023; 14:1226905. [PMID: 37576553 PMCID: PMC10414792 DOI: 10.3389/fgene.2023.1226905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 06/30/2023] [Indexed: 08/15/2023] Open
Abstract
Neuropeptides contain more chemical information than other classical neurotransmitters and have multiple receptor recognition sites. These characteristics allow neuropeptides to have a correspondingly higher selectivity for nerve receptors and fewer side effects. Traditional experimental methods, such as mass spectrometry and liquid chromatography technology, still need the support of a complete neuropeptide precursor database and the basic characteristics of neuropeptides. Incomplete neuropeptide precursor and information databases will lead to false-positives or reduce the sensitivity of recognition. In recent years, studies have proven that machine learning methods can rapidly and effectively predict neuropeptides. In this work, we have made a systematic attempt to create an ensemble tool based on four convolution neural network models. These baseline models were separately trained on one-hot encoding, AAIndex, G-gap dipeptide encoding and word2vec and integrated using Gaussian Naive Bayes (NB) to construct our predictor designated NeuroCNN_GNB. Both 5-fold cross-validation tests using benchmark datasets and independent tests showed that NeuroCNN_GNB outperformed other state-of-the-art methods. Furthermore, this novel framework provides essential interpretations that aid the understanding of model success by leveraging the powerful Shapley Additive exPlanation (SHAP) algorithm, thereby highlighting the most important features relevant for predicting neuropeptides.
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Affiliation(s)
- Di Liu
- Information Science and Technology College, Dalian Maritime University, Dalian, China
| | - Zhengkui Lin
- Information Science and Technology College, Dalian Maritime University, Dalian, China
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian, China
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20
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Patnaik SK, Swaroop AK, Naik MR, Selvaraj J, Chandrasekar MJN. Repurposing of FDA Approved Drugs and Neuropep peptides as Anticancer Agents Against ErbB1 and ErbB2. Drug Res (Stuttg) 2023. [PMID: 37068520 DOI: 10.1055/a-2030-4078] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
ErbB1 and ErbB2 are the most important biological targets in cancer drug discovery and development of dual inhibitors for the cancer therapy. FDA approved drugs and Neuropep peptides were used to fit into the ATP binding site of the tyrosine kinases; ErbB1 and ErbB2 proteins. Cytoscape, iGEMDOCK, HPEPDOCK and DataWarrior softwares were used to study the role of these agents as anticancer drugs. Eleven FDA approved drugs and eleven Neuropep peptides showed the strongest 2D interactions and significant binding energy with the proteins. Invitro MTT anticancer assay revealed that, the test compounds, peptide YSFGL and doxorubicin showed significant IC50 value (µM) of 26.417±0.660 and 7.675±0.278 respectively which are compared with the lapatinib standard IC50 value (µM) of 2.380±0.357 against A549 cells and IC50 value (µM) of 39.047±0.770 and 8.313±0.435 respectively which are compared with the lapatinib standard IC50 value (µM) of 3.026±0.180 against MDA-MB-231 cells.
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Affiliation(s)
- Sunil Kumar Patnaik
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Tamilnadu, India
| | - Akey Krishna Swaroop
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Tamilnadu, India
| | - Mudavath Ravi Naik
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Tamilnadu, India
| | - Jubie Selvaraj
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Tamilnadu, India
| | - Moola Joghee Nanjan Chandrasekar
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, The Nilgiris, Tamilnadu, India
- School of Life Sciences, JSS Academy of Higher Education & Research(Ooty Campus), Longwood, Mysuru Road, Ooty-643001, Tamilnadu, India
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21
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Liu Y, Wang S, Li X, Liu Y, Zhu X. NeuroPpred-SVM: A New Model for Predicting Neuropeptides Based on Embeddings of BERT. J Proteome Res 2023; 22:718-728. [PMID: 36749151 DOI: 10.1021/acs.jproteome.2c00363] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Neuropeptides play pivotal roles in different physiological processes and are related to different kinds of diseases. Identification of neuropeptides is of great benefit for studying the mechanism of these physiological processes and the treatment of neurological disorders. Several state-of-the-art neuropeptide predictors have been developed by using a two-layer stacking ensemble algorithm. Although the two-layer stacking ensemble algorithm can improve the feature representability, these models are complex, which are not as efficient as the models based on one classifier. In this study, we proposed a new model, NeuroPpred-SVM, to predict neuropeptides based on the embeddings of Bidirectional Encoder Representations from Transformers and other sequential features by using a support vector machine (SVM). The experimental results indicate that our model achieved a cross-validation area under the receiver operating characteristic (AUROC) curve of 0.969 on the training data set and an AUROC of 0.966 on the independent test set. By comparing our model with the other four state-of-the-art models including NeuroPIpred, PredNeuroP, NeuroPpred-Fuse, and NeuroPpred-FRL on the independent test set, our model achieved the highest AUROC, Matthews correlation coefficient, accuracy, and specificity, which indicate that our model outperforms the existing models. We believed that NeuroPpred-SVM could be a useful tool for identifying neuropeptides with high accuracy and low cost. The data sets and Python code are available at https://github.com/liuyf-a/NeuroPpred-SVM.
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Affiliation(s)
- Yufeng Liu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Shuyu Wang
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Xiang Li
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Yinbo Liu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
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22
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Phetsanthad A, Vu NQ, Yu Q, Buchberger AR, Chen Z, Keller C, Li L. Recent advances in mass spectrometry analysis of neuropeptides. MASS SPECTROMETRY REVIEWS 2023; 42:706-750. [PMID: 34558119 PMCID: PMC9067165 DOI: 10.1002/mas.21734] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 08/22/2021] [Accepted: 08/28/2021] [Indexed: 05/08/2023]
Abstract
Due to their involvement in numerous biochemical pathways, neuropeptides have been the focus of many recent research studies. Unfortunately, classic analytical methods, such as western blots and enzyme-linked immunosorbent assays, are extremely limited in terms of global investigations, leading researchers to search for more advanced techniques capable of probing the entire neuropeptidome of an organism. With recent technological advances, mass spectrometry (MS) has provided methodology to gain global knowledge of a neuropeptidome on a spatial, temporal, and quantitative level. This review will cover key considerations for the analysis of neuropeptides by MS, including sample preparation strategies, instrumental advances for identification, structural characterization, and imaging; insightful functional studies; and newly developed absolute and relative quantitation strategies. While many discoveries have been made with MS, the methodology is still in its infancy. Many of the current challenges and areas that need development will also be highlighted in this review.
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Affiliation(s)
- Ashley Phetsanthad
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Nhu Q. Vu
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Qing Yu
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, WI 53705, USA
| | - Amanda R. Buchberger
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Zhengwei Chen
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Caitlin Keller
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, WI 53706, USA
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, WI 53705, USA
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23
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Xiong H, Wilson BA, Slesinger PA, Qin Z. Understanding Neuropeptide Transmission in the Brain by Optical Uncaging and Release. ACS Chem Neurosci 2023; 14:516-523. [PMID: 36719384 PMCID: PMC10302814 DOI: 10.1021/acschemneuro.2c00684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Neuropeptides are abundant and essential signaling molecules in the nervous system involved in modulating neural circuits and behavior. Neuropeptides are generally released extrasynaptically and signal via volume transmission through G-protein-coupled receptors (GPCR). Although substantive functional roles of neuropeptides have been discovered, many questions on neuropeptide transmission remain poorly understood, including the local diffusion and transmission properties in the brain extracellular space. To address this challenge, intensive efforts are required to develop advanced tools for releasing and detecting neuropeptides with high spatiotemporal resolution. Because of the rapid development of biosensors and materials science, emerging tools are beginning to provide a better understanding of neuropeptide transmission. In this perspective, we summarize the fundamental advances in understanding neuropeptide transmission over the past decade, highlight the tools for releasing neuropeptides with high spatiotemporal solution in the brain, and discuss open questions and future directions in the field.
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Affiliation(s)
- Hejian Xiong
- Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
| | - Blake A. Wilson
- Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
| | - Paul A. Slesinger
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zhenpeng Qin
- Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX 75080, USA
- Department of Surgery, The University of Texas at Southwestern Medical Center, Dallas, TX 75390, USA
- Center for Advanced Pain Studies, The University of Texas at Dallas, Richardson, TX 75080, USA
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24
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Vu NQ, Yen HC, Fields L, Cao W, Li L. HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search. J Proteome Res 2023; 22:420-431. [PMID: 36696582 PMCID: PMC10160011 DOI: 10.1021/acs.jproteome.2c00597] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Neuropeptides are a class of endogenous peptides that have key regulatory roles in biochemical, physiological, and behavioral processes. Mass spectrometry analyses of neuropeptides often rely on protein informatics tools for database searching and peptide identification. As neuropeptide databases are typically experimentally built and comprised of short sequences with high sequence similarity to each other, we developed a novel database searching tool, HyPep, which utilizes sequence homology searching for peptide identification. HyPep aligns de novo sequenced peptides, generated through PEAKS software, with neuropeptide database sequences and identifies neuropeptides based on the alignment score. HyPep performance was optimized using LC-MS/MS measurements of peptide extracts from various Callinectes sapidus neuronal tissue types and compared with a commercial database searching software, PEAKS DB. HyPep identified more neuropeptides from each tissue type than PEAKS DB at 1% false discovery rate, and the false match rate from both programs was 2%. In addition to identification, this report describes how HyPep can aid in the discovery of novel neuropeptides.
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Affiliation(s)
- Nhu Q Vu
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Hsu-Ching Yen
- Department of Biochemistry, University of Wisconsin-Madison, 433 Babcock Drive, Madison, Wisconsin 53706, United States
| | - Lauren Fields
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Weifeng Cao
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States.,School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705, United States
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25
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Phetsanthad A, Roycroft C, Li L. Enrichment and fragmentation approaches for enhanced detection and characterization of endogenous glycosylated neuropeptides. Proteomics 2023; 23:e2100375. [PMID: 35906894 PMCID: PMC9884999 DOI: 10.1002/pmic.202100375] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 07/18/2022] [Accepted: 07/22/2022] [Indexed: 02/01/2023]
Abstract
Glycosylated neuropeptides were recently discovered in crustaceans, a model organism with a well-characterized neuroendocrine system. Several workflows exist to characterize enzymatically digested peptides; however, the unique properties of endogenous neuropeptides require methods to be re-evaluated. We investigate the use of hydrophilic interaction liquid chromatography (HILIC) enrichment and different fragmentation methods to further probe the expression of glycosylated neuropeptides in Callinectes sapidus. During the evaluation of HILIC, we observed the necessity of a less aqueous solvent for endogenous peptide samples. This modification enabled the number of detected neuropeptide glycoforms to increase almost two-fold, from 18 to 36. Product ion-triggered electron-transfer/higher-energy collision dissociation enabled the site-specific detection of 55 intact N- and O-linked glycoforms, while the faster stepped collision energy higher-energy collisional dissociation resulted in detection of 25. Additionally, applying this workflow to five neuronal tissues enabled the characterization of 36 more glycoforms of known neuropeptides and 11 more glycoforms of nine putative novel neuropeptides. Overall, the database of glycosylated neuropeptides in crustaceans was largely expanded from 18 to 136 glycoforms of 40 neuropeptides from 10 neuropeptide families. Both macro- and micro-heterogeneity were observed, demonstrating the chemical diversity of this simple invertebrate, establishing a framework to use crustacean to probe modulatory effects of glycosylation on neuropeptides.
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Affiliation(s)
- Ashley Phetsanthad
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Caroline Roycroft
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA,College of Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA,School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA,Corresponding author: Prof. Dr. Lingjun Li, School of Pharmacy & Department of Chemistry, University of Wisconsin-Madison, 777 Highland Avenue, Madison, WI 53705-2222, Phone: (608) 265-8491, Fax: (608) 262-5345,
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26
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Mochizuki T, Sakamoto M, Tanizawa Y, Seike H, Zhu Z, Zhou YJ, Fukumura K, Nagata S, Nakamura Y. Best Practices for Comprehensive Annotation of Neuropeptides of Gryllus bimaculatus. INSECTS 2023; 14:121. [PMID: 36835690 PMCID: PMC9960350 DOI: 10.3390/insects14020121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
Genome annotation is critically important data that can support research. Draft genome annotations cover representative genes; however, they often do not include genes that are expressed only in limited tissues and stages, or genes with low expression levels. Neuropeptides are responsible for regulation of various physiological and biological processes. A recent study disclosed the genome draft of the two-spotted cricket Gryllus bimaculatus, which was utilized to understand the intriguing physiology and biology of crickets. Thus far, only two of the nine reported neuropeptides in G. bimaculatus were annotated in the draft genome. Even though de novo assembly using transcriptomic analyses can comprehensively identify neuropeptides, this method does not follow those annotations on the genome locus. In this study, we performed the annotations based on the reference mapping, de novo transcriptome assembly, and manual curation. Consequently, we identified 41 neuropeptides out of 43 neuropeptides, which were reported in the insects. Further, 32 of the identified neuropeptides on the genomic loci in G. bimaculatus were annotated. The present annotation methods can be applicable for the neuropeptide annotation of other insects. Furthermore, the methods will help to generate useful infrastructures for studies relevant to neuropeptides.
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Affiliation(s)
- Takako Mochizuki
- National Institute of Genetics, Research Organization of Information and Systems, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
| | - Mika Sakamoto
- National Institute of Genetics, Research Organization of Information and Systems, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
| | - Yasuhiro Tanizawa
- National Institute of Genetics, Research Organization of Information and Systems, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
| | - Hitomi Seike
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Zhen Zhu
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Yi Jun Zhou
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Keisuke Fukumura
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Shinji Nagata
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Yasukazu Nakamura
- National Institute of Genetics, Research Organization of Information and Systems, 1111 Yata, Mishima, Shizuoka 411-8540, Japan
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27
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Nesterenko M, Miroliubov A. From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala). F1000Res 2023; 11:583. [PMID: 36447930 PMCID: PMC9664023 DOI: 10.12688/f1000research.110492.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, a system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into "zombies". Despite all these amazing traits, there are no genomic or transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: the externa, and the main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite's body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for any rhizocephalan. Based on the results obtained, the spatial heterogeneity of protein-coding gene expression in different regions of the adult female body of P. reticulata was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans "got stuck in their metamorphosis", even at the reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of these extraordinary parasites.
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Affiliation(s)
- Maksim Nesterenko
- Department of Invertebrate Zoology, St Petersburg State University, St Petersburg, 199034, Russian Federation,Laboratory of parasitic worms and protists, Zoological Institute of Russian Academy of Sciences, St Petersburg, 199034, Russian Federation,
| | - Aleksei Miroliubov
- Laboratory of parasitic worms and protists, Zoological Institute of Russian Academy of Sciences, St Petersburg, 199034, Russian Federation
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28
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MODeLING.Vis: A Graphical User Interface Toolbox Developed for Machine Learning and Pattern Recognition of Biomolecular Data. Symmetry (Basel) 2022. [DOI: 10.3390/sym15010042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Many scientific publications that affect machine learning have set the basis for pattern recognition and symmetry. In this paper, we revisit the concept of “Mind-life continuity” published by the authors, testing the symmetry between cognitive and electrophoretic strata. We opted for machine learning to analyze and understand the total protein profile of neurotypical subjects acquired by capillary electrophoresis. Capillary electrophoresis permits a cost-wise solution but lacks modern proteomic techniques’ discriminative and quantification power. To compensate for this problem, we developed tools for better data visualization and exploration in this work. These tools permitted us to examine better the total protein profile of 92 young adults, from 19 to 25 years old, healthy university students at the University of Lisbon, with no serious, uncontrolled, or chronic diseases affecting the nervous system. As a result, we created a graphical user interface toolbox named MODeLING.Vis, which showed specific expected protein profiles present in saliva in our neurotypical sample. The developed toolbox permitted data exploration and hypothesis testing of the biomolecular data. In conclusion, this analysis offered the data mining of the acquired neuroproteomics data in the molecular weight range from 9.1 to 30 kDa. This molecular weight range, obtained by pattern recognition of our dataset, is characteristic of the small neuroimmune molecules and neuropeptides. Consequently, MODeLING.Vis offers a machine-learning solution for probing into the neurocognitive response.
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29
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Chen Z, Wang D, Yu Q, Johnson J, Shipman R, Zhong X, Huang J, Yu Q, Zetterberg H, Asthana S, Carlsson C, Okonkwo O, Li L. In-Depth Site-Specific O-Glycosylation Analysis of Glycoproteins and Endogenous Peptides in Cerebrospinal Fluid (CSF) from Healthy Individuals, Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD) Patients. ACS Chem Biol 2022; 17:3059-3068. [PMID: 34964596 PMCID: PMC9240109 DOI: 10.1021/acschembio.1c00932] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Site-specific O-glycoproteome mapping in complex biological systems provides a molecular basis for understanding the structure-function relationships of glycoproteins and their roles in physiological and pathological processes. Previous O-glycoproteome analysis in cerebrospinal fluid (CSF) focused on sialylated glycoforms, while missing information on other glycosylation types. In order to achieve an unbiased O-glycosylation profile, we developed an integrated strategy combining universal boronic acid enrichment, high-pH fractionation, and electron-transfer and higher-energy collision dissociation (EThcD) for enhanced intact O-glycopeptide analysis. We applied this strategy to analyze the O-glycoproteome in CSF, resulting in the identification of 308 O-glycopeptides from 110 O-glycoproteins, covering both sialylated and nonsialylated glycoforms. To our knowledge, this is the largest data set of O-glycoproteins and O-glycosites reported for CSF to date. We also developed a peptidomics workflow that utilized the EThcD and a three-step database searching strategy for comprehensive PTM analysis of endogenous peptides, including N-glycosylation, O-glycosylation, and other common peptide PTMs. Interestingly, among the 1411 endogenous peptides identified, 89 were O-glycosylated, and only one N-glycosylated peptide was found, indicating that CSF endogenous peptides were predominantly O-glycosylated. Analyses of the O-glycoproteome and endogenous peptidome PTMs were also conducted in the CSF of MCI and AD patients to provide a landscape of glycosylation patterns in different disease states. Our results showed a decreasing trend in fucosylation and an increasing trend of endogenous peptide O-glycosylation, which may play an important role in AD progression.
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Affiliation(s)
- Zhengwei Chen
- Department of Chemistry, University of Wisconsin, Madison, WI 53706, USA
| | - Danqing Wang
- Department of Chemistry, University of Wisconsin, Madison, WI 53706, USA
| | - Qing Yu
- School of Pharmacy, University of Wisconsin, Madison, WI 53705, USA
| | - Jillian Johnson
- School of Pharmacy, University of Wisconsin, Madison, WI 53705, USA
| | - Richard Shipman
- Applied Science Program, University of Wisconsin-Stout, Menomonie, WI 54751, USA
| | - Xiaofang Zhong
- School of Pharmacy, University of Wisconsin, Madison, WI 53705, USA
| | - Junfeng Huang
- School of Pharmacy, University of Wisconsin, Madison, WI 53705, USA
| | - Qinying Yu
- School of Pharmacy, University of Wisconsin, Madison, WI 53705, USA
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, 43180, Mölndal, Sweden,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 43180, Mölndal, Sweden,Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, United Kingdom,UK Dementia Research Institute at UCL, London, WC1E 6BT, United Kingdom
| | - Sanjay Asthana
- School of Medicine and Public Health, University of Wisconsin, Madison, WI 53726, USA
| | - Cynthia Carlsson
- School of Medicine and Public Health, University of Wisconsin, Madison, WI 53726, USA
| | - Ozioma Okonkwo
- School of Medicine and Public Health, University of Wisconsin, Madison, WI 53726, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin, Madison, WI 53706, USA,School of Pharmacy, University of Wisconsin, Madison, WI 53705, USA,Correspondence: Professor Lingjun Li, School of Pharmacy and Department of Chemistry, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705-2222, , Fax: +1-608-262-5345, Phone: +1-608-265-8491
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30
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Kaulich E, Grundy LJ, Schafer WR, Walker DS. The diverse functions of the DEG/ENaC family: linking genetic and physiological insights. J Physiol 2022; 601:1521-1542. [PMID: 36314992 PMCID: PMC10148893 DOI: 10.1113/jp283335] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022] Open
Abstract
The DEG/ENaC family of ion channels was defined based on the sequence similarity between degenerins (DEG) from the nematode Caenorhabditis elegans and subunits of the mammalian epithelial sodium channel (ENaC), and also includes a diverse array of non-voltage-gated cation channels from across animal phyla, including the mammalian acid-sensing ion channels (ASICs) and Drosophila pickpockets. ENaCs and ASICs have wide ranging medical importance; for example, ENaCs play an important role in respiratory and renal function, and ASICs in ischaemia and inflammatory pain, as well as being implicated in memory and learning. Electrophysiological approaches, both in vitro and in vivo, have played an essential role in establishing the physiological properties of this diverse family, identifying an array of modulators and implicating them in an extensive range of cellular functions, including mechanosensation, acid sensation and synaptic modulation. Likewise, genetic studies in both invertebrates and vertebrates have played an important role in linking our understanding of channel properties to function at the cellular and whole animal/behavioural level. Drawing together genetic and physiological evidence is essential to furthering our understanding of the precise cellular roles of DEG/ENaC channels, with the diversity among family members allowing comparative physiological studies to dissect the molecular basis of these diverse functions.
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Affiliation(s)
- Eva Kaulich
- Neurobiology Division, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, UK
| | - Laura J Grundy
- Neurobiology Division, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, UK
| | - William R Schafer
- Neurobiology Division, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, UK.,Department of Biology, KU Leuven, Leuven, Belgium
| | - Denise S Walker
- Neurobiology Division, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, UK
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31
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Madsen CT, Refsgaard JC, Teufel FG, Kjærulff SK, Wang Z, Meng G, Jessen C, Heljo P, Jiang Q, Zhao X, Wu B, Zhou X, Tang Y, Jeppesen JF, Kelstrup CD, Buckley ST, Tullin S, Nygaard-Jensen J, Chen X, Zhang F, Olsen JV, Han D, Grønborg M, de Lichtenberg U. Combining mass spectrometry and machine learning to discover bioactive peptides. Nat Commun 2022; 13:6235. [PMID: 36266275 PMCID: PMC9584923 DOI: 10.1038/s41467-022-34031-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 10/10/2022] [Indexed: 12/25/2022] Open
Abstract
Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development.
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Affiliation(s)
- Christian T. Madsen
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Jan C. Refsgaard
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,Intomics, Kongens Lyngby, Denmark
| | - Felix G. Teufel
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Sonny K. Kjærulff
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,Intomics, Kongens Lyngby, Denmark
| | - Zhe Wang
- Novo Nordisk Research Centre China, Beijing, China
| | - Guangjun Meng
- Novo Nordisk Research Centre China, Beijing, China ,Pulmongene LTD. Rm 502, Building 2, No. 9, Yike Road, Zhongguancun Life Science Park, Changping District, Beijing, China
| | - Carsten Jessen
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Petteri Heljo
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Qunfeng Jiang
- Novo Nordisk Research Centre China, Beijing, China ,Innovent Biologics, Inc. DongPing Jie 168, Suzhou, China
| | - Xin Zhao
- Novo Nordisk Research Centre China, Beijing, China
| | - Bo Wu
- Novo Nordisk Research Centre China, Beijing, China ,QL Biopharmaceutical, Rm 101, Building 7, 20 Life Science Park Road, Beijing, China
| | - Xueping Zhou
- Novo Nordisk Research Centre China, Beijing, China ,grid.421648.d0000 0004 5997 3165Crinetics pharmaceuticals, 10222 Barnes Canyon Rd Building 2, San Diego, CA 92121 USA
| | - Yang Tang
- Novo Nordisk Research Centre China, Beijing, China ,Roche R&D Center (China) Ltd, Building 5, 371 Lishizhen Road, 201203 Pudong, Shanghai China
| | - Jacob F. Jeppesen
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Christian D. Kelstrup
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Stephen T. Buckley
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Søren Tullin
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,grid.420061.10000 0001 2171 7500Boehringer Ingelheim GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach, Germany
| | - Jan Nygaard-Jensen
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,grid.420061.10000 0001 2171 7500Boehringer Ingelheim GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach, Germany
| | - Xiaoli Chen
- Novo Nordisk Research Centre China, Beijing, China
| | - Fang Zhang
- Novo Nordisk Research Centre China, Beijing, China ,Structure Therapeutics. 701 Gateway Blvd., South San Francisco, CA 94080 USA
| | - Jesper V. Olsen
- grid.5254.60000 0001 0674 042XDepartment of Proteomics, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Dan Han
- Novo Nordisk Research Centre China, Beijing, China
| | - Mads Grønborg
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark
| | - Ulrik de Lichtenberg
- grid.425956.90000 0004 0391 2646Global Research Technologies, Novo Nordisk A/S, Maaloev, Denmark ,grid.487026.f0000 0000 9922 7627The Novo Nordisk Foundation, Tuborg Havnevej 19, 2900 Hellerup, Denmark
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32
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Eiden LE, Hernández VS, Jiang SZ, Zhang L. Neuropeptides and small-molecule amine transmitters: cooperative signaling in the nervous system. Cell Mol Life Sci 2022; 79:492. [PMID: 35997826 PMCID: PMC11072502 DOI: 10.1007/s00018-022-04451-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 06/15/2022] [Accepted: 06/23/2022] [Indexed: 12/17/2022]
Abstract
Neuropeptides are expressed in cell-specific patterns throughout mammalian brain. Neuropeptide gene expression has been useful for clustering neurons by phenotype, based on single-cell transcriptomics, and for defining specific functional circuits throughout the brain. How neuropeptides function as first messengers in inter-neuronal communication, in cooperation with classical small-molecule amine transmitters (SMATs) is a current topic of systems neurobiology. Questions include how neuropeptides and SMATs cooperate in neurotransmission at the molecular, cellular and circuit levels; whether neuropeptides and SMATs always co-exist in neurons; where neuropeptides and SMATs are stored in the neuron, released from the neuron and acting, and at which receptors, after release; and how neuropeptides affect 'classical' transmitter function, both directly upon co-release, and indirectly, via long-term regulation of gene transcription and neuronal plasticity. Here, we review an extensive body of data about the distribution of neuropeptides and their receptors, their actions after neuronal release, and their function based on pharmacological and genetic loss- and gain-of-function experiments, that addresses these questions, fundamental to understanding brain function, and development of neuropeptide-based, and potentially combinatorial peptide/SMAT-based, neurotherapeutics.
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Affiliation(s)
- Lee E Eiden
- Section On Molecular Neuroscience, National Institute of Mental Health, Intramural Research Program, National Institutes of Health, 49 Convent Drive, Room 5A38, Bethesda, MD, 20892, USA.
| | - Vito S Hernández
- Department of Physiology, School of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
| | - Sunny Z Jiang
- Section On Molecular Neuroscience, National Institute of Mental Health, Intramural Research Program, National Institutes of Health, 49 Convent Drive, Room 5A38, Bethesda, MD, 20892, USA
| | - Limei Zhang
- Department of Physiology, School of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.
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33
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Chen S, Li Q, Zhao J, Bin Y, Zheng C. NeuroPred-CLQ: incorporating deep temporal convolutional networks and multi-head attention mechanism to predict neuropeptides. Brief Bioinform 2022; 23:6672901. [DOI: 10.1093/bib/bbac319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/27/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Neuropeptides (NPs) are a particular class of informative substances in the immune system and physiological regulation. They play a crucial role in regulating physiological functions in various biological growth and developmental stages. In addition, NPs are crucial for developing new drugs for the treatment of neurological diseases. With the development of molecular biology techniques, some data-driven tools have emerged to predict NPs. However, it is necessary to improve the predictive performance of these tools for NPs. In this study, we developed a deep learning model (NeuroPred-CLQ) based on the temporal convolutional network (TCN) and multi-head attention mechanism to identify NPs effectively and translate the internal relationships of peptide sequences into numerical features by the Word2vec algorithm. The experimental results show that NeuroPred-CLQ learns data information effectively, achieving 93.6% accuracy and 98.8% AUC on the independent test set. The model has better performance in identifying NPs than the state-of-the-art predictors. Visualization of features using t-distribution random neighbor embedding shows that the NeuroPred-CLQ can clearly distinguish the positive NPs from the negative ones. We believe the NeuroPred-CLQ can facilitate drug development and clinical trial studies to treat neurological disorders.
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Affiliation(s)
- Shouzhi Chen
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
| | - Qing Li
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
| | - Jianping Zhao
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
| | - Yannan Bin
- School of Computer Science and Technology, Anhui University , Hefei, China
| | - Chunhou Zheng
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
- School of Computer Science and Technology, Anhui University , Hefei, China
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Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence. MEMBRANES 2022; 12:membranes12070708. [PMID: 35877911 PMCID: PMC9320227 DOI: 10.3390/membranes12070708] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/16/2022]
Abstract
Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.
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Muqaku B, Oeckl P. Peptidomic Approaches and Observations in Neurodegenerative Diseases. Int J Mol Sci 2022; 23:ijms23137332. [PMID: 35806335 PMCID: PMC9266836 DOI: 10.3390/ijms23137332] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/16/2022] [Accepted: 06/28/2022] [Indexed: 02/04/2023] Open
Abstract
Mass spectrometry (MS), with its immense technological developments over the last two decades, has emerged as an unavoidable technique in analyzing biomolecules such as proteins and peptides. Its multiplexing capability and explorative approach make it a valuable tool for analyzing complex clinical samples concerning biomarker research and investigating pathophysiological mechanisms. Peptides regulate various biological processes, and several of them play a critical role in many disease-related pathological conditions. One important example in neurodegenerative diseases is the accumulation of amyloid-beta peptides (Aβ) in the brain of Alzheimer’s disease (AD) patients. When investigating brain function and brain-related pathologies, such as neurodegenerative diseases, cerebrospinal fluid (CSF) represents the most suitable sample because of its direct contact with the brain. In this review, we evaluate publications applying peptidomics analysis to CSF samples, focusing on neurodegenerative diseases. We describe the methodology of peptidomics analysis and give an overview of the achievements of CSF peptidomics over the years. Finally, publications reporting peptides regulated in AD are discussed.
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Affiliation(s)
- Besnik Muqaku
- German Center for Neurodegenerative Diseases (DZNE e.V.), 89081 Ulm, Germany;
| | - Patrick Oeckl
- German Center for Neurodegenerative Diseases (DZNE e.V.), 89081 Ulm, Germany;
- Department of Neurology, Ulm University Hospital, 89081 Ulm, Germany
- Correspondence: ; Tel.: +49-731-500-63143
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36
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Nesterenko M, Miroliubov A. From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala). F1000Res 2022; 11:583. [PMID: 36447930 PMCID: PMC9664023 DOI: 10.12688/f1000research.110492.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2023] [Indexed: 09/16/2023] Open
Abstract
Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, a system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into "zombies". Despite all these amazing traits, there are no genomic or transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: the externa, and the main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite's body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for any rhizocephalan. Based on the results obtained, the spatial heterogeneity of protein-coding gene expression in different regions of the adult female body of P. reticulata was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans "got stuck in their metamorphosis", even at the reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of these extraordinary parasites.
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Affiliation(s)
- Maksim Nesterenko
- Department of Invertebrate Zoology, St Petersburg State University, St Petersburg, 199034, Russian Federation
- Laboratory of parasitic worms and protists, Zoological Institute of Russian Academy of Sciences, St Petersburg, 199034, Russian Federation
| | - Aleksei Miroliubov
- Laboratory of parasitic worms and protists, Zoological Institute of Russian Academy of Sciences, St Petersburg, 199034, Russian Federation
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37
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López-García G, Dublan-García O, Arizmendi-Cotero D, Gómez Oliván LM. Antioxidant and Antimicrobial Peptides Derived from Food Proteins. Molecules 2022; 27:1343. [PMID: 35209132 PMCID: PMC8878547 DOI: 10.3390/molecules27041343] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/11/2022] [Accepted: 02/13/2022] [Indexed: 12/12/2022] Open
Abstract
Recently, the demand for food proteins in the market has increased due to a rise in degenerative illnesses that are associated with the excessive production of free radicals and the unwanted side effects of various drugs, for which researchers have suggested diets rich in bioactive compounds. Some of the functional compounds present in foods are antioxidant and antimicrobial peptides, which are used to produce foods that promote health and to reduce the consumption of antibiotics. These peptides have been obtained from various sources of proteins, such as foods and agri-food by-products, via enzymatic hydrolysis and microbial fermentation. Peptides with antioxidant properties exert effective metal ion (Fe2+/Cu2+) chelating activity and lipid peroxidation inhibition, which may lead to notably beneficial effects in promoting human health and food processing. Antimicrobial peptides are small oligo-peptides generally containing from 10 to 100 amino acids, with a net positive charge and an amphipathic structure; they are the most important components of the antibacterial defense of organisms at almost all levels of life-bacteria, fungi, plants, amphibians, insects, birds and mammals-and have been suggested as natural compounds that neutralize the toxicity of reactive oxygen species generated by antibiotics and the stress generated by various exogenous sources. This review discusses what antioxidant and antimicrobial peptides are, their source, production, some bioinformatics tools used for their obtainment, emerging technologies, and health benefits.
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Affiliation(s)
- Guadalupe López-García
- Food and Environmental Toxicology Laboratory, Chemistry Faculty, Universidad Autónoma del Estado de México, Paseo Colón Intersección Paseo Tollocan s/n. Col. Residencial Colón, Toluca 50120, Mexico; (G.L.-G.); (L.M.G.O.)
| | - Octavio Dublan-García
- Food and Environmental Toxicology Laboratory, Chemistry Faculty, Universidad Autónoma del Estado de México, Paseo Colón Intersección Paseo Tollocan s/n. Col. Residencial Colón, Toluca 50120, Mexico; (G.L.-G.); (L.M.G.O.)
| | - Daniel Arizmendi-Cotero
- Department of Industrial Engineering, Engineering Faculty, Campus Toluca, Universidad Tecnológica de México (UNITEC), Estado de México, Toluca 50160, Mexico;
| | - Leobardo Manuel Gómez Oliván
- Food and Environmental Toxicology Laboratory, Chemistry Faculty, Universidad Autónoma del Estado de México, Paseo Colón Intersección Paseo Tollocan s/n. Col. Residencial Colón, Toluca 50120, Mexico; (G.L.-G.); (L.M.G.O.)
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38
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Yeo XY, Cunliffe G, Ho RC, Lee SS, Jung S. Potentials of Neuropeptides as Therapeutic Agents for Neurological Diseases. Biomedicines 2022; 10:343. [PMID: 35203552 PMCID: PMC8961788 DOI: 10.3390/biomedicines10020343] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/23/2022] [Accepted: 01/24/2022] [Indexed: 02/04/2023] Open
Abstract
Despite recent leaps in modern medicine, progress in the treatment of neurological diseases remains slow. The near impermeable blood-brain barrier (BBB) that prevents the entry of therapeutics into the brain, and the complexity of neurological processes, limits the specificity of potential therapeutics. Moreover, a lack of etiological understanding and the irreversible nature of neurological conditions have resulted in low tolerability and high failure rates towards existing small molecule-based treatments. Neuropeptides, which are small proteinaceous molecules produced by the body, either in the nervous system or the peripheral organs, modulate neurological function. Although peptide-based therapeutics originated from the treatment of metabolic diseases in the 1920s, the adoption and development of peptide drugs for neurological conditions are relatively recent. In this review, we examine the natural roles of neuropeptides in the modulation of neurological function and the development of neurological disorders. Furthermore, we highlight the potential of these proteinaceous molecules in filling gaps in current therapeutics.
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Affiliation(s)
- Xin Yi Yeo
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138667, Singapore; (X.Y.Y.); (G.C.)
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore;
| | - Grace Cunliffe
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138667, Singapore; (X.Y.Y.); (G.C.)
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Roger C. Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore;
- Institute for Health Innovation & Technology (iHealthtech), National University of Singapore, Singapore 117599, Singapore
| | - Su Seong Lee
- NanoBio Lab, Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), Singapore 138667, Singapore
| | - Sangyong Jung
- Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore 138667, Singapore; (X.Y.Y.); (G.C.)
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore
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39
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A Mini-Review on Potential of Neuropeptides as Future Therapeutics. Int J Pept Res Ther 2022. [DOI: 10.1007/s10989-021-10309-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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40
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Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells. Biomedicines 2021; 10:biomedicines10010014. [PMID: 35052689 PMCID: PMC8773225 DOI: 10.3390/biomedicines10010014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/07/2021] [Accepted: 12/17/2021] [Indexed: 11/18/2022] Open
Abstract
Background: As chemical signals of hormones, neuropeptides are essential to regulate cell growth by interacting with their receptors to achieve cell communications in cancer tissues. Previously, neuropeptide transcriptome analysis was limited to tissue-based bulk expression levels. The molecular mechanisms of neuropeptides and their receptors at the single-cell level remain unclear. We conducted a systematic single-cell transcriptome data integration analysis to clarify the similarities and variations of neuropeptide-mediated cell communication between various malignancies. Methods: Based on the single-cell expression information in 72 cancer datasets across 24 cancer types, we characterized actively expressed neuropeptides and receptors as having log values of the quantitative transcripts per million ≥ 1. Then, we created the putative cell-to-cell communication network for each dataset by using the known interaction of those actively expressed neuropeptides and receptors. To focus on the stable cell communication events, we identified neuropeptide and downstream receptors whose interactions were detected in more than half of all conceivable cell-cell interactions (square of the total cell population) in a dataset. Results: Focusing on those actively expressed neuropeptides and receptors, we built over 76 million cell-to-cell communications across 70 cancer datasets. Then the stable cell communication analyses were applied to each dataset, and about 14 million stable cell-to-cell communications could be detected based on 16 neuropeptides and 23 receptors. Further functional analysis indicates these 39 genes could regulate blood pressure and are significantly associated with patients’ survival among over ten thousand The Cancer Genome Atlas (TCGA)pan-cancer samples. By zooming in lung cancer-specific clinical features, we discovered the 39 genes appeared to be enriched in the patients with smoking. In skin cancer, they may differ in the patients with the distinct histological subtype and molecular drivers. Conclusions: At the single-cell level, stable cell communications across cancer types demonstrated some common and distinct neuropeptide-receptor patterns, which could be helpful in determining the status of neuropeptide-based cell communication and developing a peptide-based therapy strategy.
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41
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Grønning AGB, Kacprowski T, Schéele C. MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities. Biol Methods Protoc 2021; 6:bpab021. [PMID: 34909478 PMCID: PMC8665375 DOI: 10.1093/biomethods/bpab021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/28/2021] [Accepted: 11/17/2021] [Indexed: 11/14/2022] Open
Abstract
Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.
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Affiliation(s)
- Alexander G B Grønning
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, 38106 Braunschweig, Germany.,Braunschweig Integrated Centre for Systems Biology (BRICS), 38106 Braunschweig, Germany
| | - Camilla Schéele
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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42
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Peeters MKR, Baggerman G, Gabriels R, Pepermans E, Menschaert G, Boonen K. Ion Mobility Coupled to a Time-of-Flight Mass Analyzer Combined With Fragment Intensity Predictions Improves Identification of Classical Bioactive Peptides and Small Open Reading Frame-Encoded Peptides. Front Cell Dev Biol 2021; 9:720570. [PMID: 34604223 PMCID: PMC8484717 DOI: 10.3389/fcell.2021.720570] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/25/2021] [Indexed: 12/29/2022] Open
Abstract
Bioactive peptides exhibit key roles in a wide variety of complex processes, such as regulation of body weight, learning, aging, and innate immune response. Next to the classical bioactive peptides, emerging from larger precursor proteins by specific proteolytic processing, a new class of peptides originating from small open reading frames (sORFs) have been recognized as important biological regulators. But their intrinsic properties, specific expression pattern and location on presumed non-coding regions have hindered the full characterization of the repertoire of bioactive peptides, despite their predominant role in various pathways. Although the development of peptidomics has offered the opportunity to study these peptides in vivo, it remains challenging to identify the full peptidome as the lack of cleavage enzyme specification and large search space complicates conventional database search approaches. In this study, we introduce a proteogenomics methodology using a new type of mass spectrometry instrument and the implementation of machine learning tools toward improved identification of potential bioactive peptides in the mouse brain. The application of trapped ion mobility spectrometry (tims) coupled to a time-of-flight mass analyzer (TOF) offers improved sensitivity, an enhanced peptide coverage, reduction in chemical noise and the reduced occurrence of chimeric spectra. Subsequent machine learning tools MS2PIP, predicting fragment ion intensities and DeepLC, predicting retention times, improve the database searching based on a large and comprehensive custom database containing both sORFs and alternative ORFs. Finally, the identification of peptides is further enhanced by applying the post-processing semi-supervised learning tool Percolator. Applying this workflow, the first peptidomics workflow combined with spectral intensity and retention time predictions, we identified a total of 167 predicted sORF-encoded peptides, of which 48 originating from presumed non-coding locations, next to 401 peptides from known neuropeptide precursors, linked to 66 annotated bioactive neuropeptides from within 22 different families. Additional PEAKS analysis expanded the pool of SEPs on presumed non-coding locations to 84, while an additional 204 peptides completed the list of peptides from neuropeptide precursors. Altogether, this study provides insights into a new robust pipeline that fuses technological advancements from different fields ensuring an improved coverage of the neuropeptidome in the mouse brain.
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Affiliation(s)
- Marlies K. R. Peeters
- BioBix, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Geert Baggerman
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
- Unit Environmental Risk and Health, Flemish Institute for Technological Research, Mol, Belgium
| | - Ralf Gabriels
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Flanders Institute for Biotechnology, Ghent, Belgium
| | - Elise Pepermans
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
- Unit Environmental Risk and Health, Flemish Institute for Technological Research, Mol, Belgium
| | - Gerben Menschaert
- BioBix, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
- OHMX.bio, Ghent, Belgium
| | - Kurt Boonen
- Centre for Proteomics, University of Antwerp, Antwerp, Belgium
- Unit Environmental Risk and Health, Flemish Institute for Technological Research, Mol, Belgium
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43
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Manoilov KY, Verkhusha VV, Shcherbakova DM. A guide to the optogenetic regulation of endogenous molecules. Nat Methods 2021; 18:1027-1037. [PMID: 34446923 DOI: 10.1038/s41592-021-01240-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 07/09/2021] [Indexed: 12/26/2022]
Abstract
Genetically encoded tools for the regulation of endogenous molecules (RNA, DNA elements and protein) are needed to study and control biological processes with minimal interference caused by protein overexpression and overactivation of signaling pathways. Here we focus on light-controlled optogenetic tools (OTs) that allow spatiotemporally precise regulation of gene expression and protein function. To control endogenous molecules, OTs combine light-sensing modules from natural photoreceptors with specific protein or nucleic acid binders. We discuss OT designs and group OTs according to the principles of their regulation. We outline characteristics of OT performance, discuss considerations for their use in vivo and review available OTs and their applications in cells and in vivo. Finally, we provide a brief outlook on the development of OTs.
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Affiliation(s)
- Kyrylo Yu Manoilov
- Department of Anatomy and Structural Biology and Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Vladislav V Verkhusha
- Department of Anatomy and Structural Biology and Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY, USA. .,Medicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland. .,Science Center for Genetics and Life Sciences, Sirius University of Science and Technology, Sochi, Russia.
| | - Daria M Shcherbakova
- Department of Anatomy and Structural Biology and Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, NY, USA.
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44
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Jiang M, Zhao B, Luo S, Wang Q, Chu Y, Chen T, Mao X, Liu Y, Wang Y, Jiang X, Wei DQ, Xiong Y. NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods. Brief Bioinform 2021; 22:6350884. [PMID: 34396388 DOI: 10.1093/bib/bbab310] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/01/2021] [Accepted: 07/18/2021] [Indexed: 12/13/2022] Open
Abstract
Neuropeptides acting as signaling molecules in the nervous system of various animals play crucial roles in a wide range of physiological functions and hormone regulation behaviors. Neuropeptides offer many opportunities for the discovery of new drugs and targets for the treatment of neurological diseases. In recent years, there have been several data-driven computational predictors of various types of bioactive peptides, but the relevant work about neuropeptides is little at present. In this work, we developed an interpretable stacking model, named NeuroPpred-Fuse, for the prediction of neuropeptides through fusing a variety of sequence-derived features and feature selection methods. Specifically, we used six types of sequence-derived features to encode the peptide sequences and then combined them. In the first layer, we ensembled three base classifiers and four feature selection algorithms, which select non-redundant important features complementarily. In the second layer, the output of the first layer was merged and fed into logistic regression (LR) classifier to train the model. Moreover, we analyzed the selected features and explained the feasibility of the selected features. Experimental results show that our model achieved 90.6% accuracy and 95.8% AUC on the independent test set, outperforming the state-of-the-art models. In addition, we exhibited the distribution of selected features by these tree models and compared the results on the training set to that on the test set. These results fully showed that our model has a certain generalization ability. Therefore, we expect that our model would provide important advances in the discovery of neuropeptides as new drugs for the treatment of neurological diseases.
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Affiliation(s)
- Mingming Jiang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Bowen Zhao
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shenggan Luo
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qiankun Wang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanyi Chu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tianhang Chen
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xueying Mao
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yatong Liu
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanjing Wang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xue Jiang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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Wood EA, Stopka SA, Zhang L, Mattson S, Maasz G, Pirger Z, Vertes A. Neuropeptide Localization in Lymnaea stagnalis: From the Central Nervous System to Subcellular Compartments. Front Mol Neurosci 2021; 14:670303. [PMID: 34093125 PMCID: PMC8172996 DOI: 10.3389/fnmol.2021.670303] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 04/09/2021] [Indexed: 12/02/2022] Open
Abstract
Due to the relatively small number of neurons (few tens of thousands), the well-established multipurpose model organism Lymnaea stagnalis, great pond snail, has been extensively used to study the functioning of the nervous system. Unlike the more complex brains of higher organisms, L. stagnalis has a relatively simple central nervous system (CNS) with well-defined circuits (e.g., feeding, locomotion, learning, and memory) and identified individual neurons (e.g., cerebral giant cell, CGC), which generate behavioral patterns. Accumulating information from electrophysiological experiments maps the network of neuronal connections and the neuronal circuits responsible for basic life functions. Chemical signaling between synaptic-coupled neurons is underpinned by neurotransmitters and neuropeptides. This review looks at the rapidly expanding contributions of mass spectrometry (MS) to neuropeptide discovery and identification at different granularity of CNS organization. Abundances and distributions of neuropeptides in the whole CNS, eleven interconnected ganglia, neuronal clusters, single neurons, and subcellular compartments are captured by MS imaging and single cell analysis techniques. Combining neuropeptide expression and electrophysiological data, and aided by genomic and transcriptomic information, the molecular basis of CNS-controlled biological functions is increasingly revealed.
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Affiliation(s)
- Ellen A. Wood
- Department of Chemistry, The George Washington University, Washington, DC, United States
| | - Sylwia A. Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Linwen Zhang
- Department of Chemistry, The George Washington University, Washington, DC, United States
| | - Sara Mattson
- Department of Chemistry, The George Washington University, Washington, DC, United States
| | - Gabor Maasz
- Balaton Limnological Research Institute, Eötvös Loránd Research Network (ELKH), Tihany, Hungary
- Soós Ernő Research and Development Center, University of Pannonia, Nagykanizsa, Hungary
| | - Zsolt Pirger
- Balaton Limnological Research Institute, Eötvös Loránd Research Network (ELKH), Tihany, Hungary
| | - Akos Vertes
- Department of Chemistry, The George Washington University, Washington, DC, United States
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46
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Hasan MM, Alam MA, Shoombuatong W, Deng HW, Manavalan B, Kurata H. NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning. Brief Bioinform 2021; 22:6272801. [PMID: 33975333 DOI: 10.1093/bib/bbab167] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/23/2021] [Accepted: 04/09/2021] [Indexed: 12/13/2022] Open
Abstract
Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.
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Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.,Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | | | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
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47
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Hashemi ZS, Zarei M, Fath MK, Ganji M, Farahani MS, Afsharnouri F, Pourzardosht N, Khalesi B, Jahangiri A, Rahbar MR, Khalili S. In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions. Front Mol Biosci 2021; 8:669431. [PMID: 33996914 PMCID: PMC8113820 DOI: 10.3389/fmolb.2021.669431] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/06/2021] [Indexed: 01/01/2023] Open
Abstract
Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.
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Affiliation(s)
- Zahra Sadat Hashemi
- ATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, Academic Center for Education, Culture and Research, Tehran, Iran
| | - Mahboubeh Zarei
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohsen Karami Fath
- Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Mahmoud Ganji
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mahboube Shahrabi Farahani
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Fatemeh Afsharnouri
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Navid Pourzardosht
- Cellular and Molecular Research Center, Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran
- Department of Biochemistry, Guilan University of Medical Sciences, Rasht, Iran
| | - Bahman Khalesi
- Department of Research and Production of Poultry Viral Vaccine, Razi Vaccine and Serum Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Abolfazl Jahangiri
- Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Rahbar
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Saeed Khalili
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
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48
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Thiangtrongjit T, Simanon N, Adisakwattana P, Limpanont Y, Chusongsang P, Chusongsang Y, Reamtong O. Identification of Low Molecular Weight Proteins and Peptides from Schistosoma mekongi Worm, Egg and Infected Mouse Sera. Biomolecules 2021; 11:biom11040559. [PMID: 33920436 PMCID: PMC8070599 DOI: 10.3390/biom11040559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/08/2021] [Accepted: 04/08/2021] [Indexed: 11/16/2022] Open
Abstract
Schistosoma mekongi is found in the lower Mekong river region and causes schistosomiasis. Low sensitivity of diagnosis and development of drug resistance are problems to eliminate this disease. To develop novel therapies and diagnostics for S. mekongi, the basic molecular biology of this pathogen needs to be explored. Bioactive peptides have been reported in several worms and play important roles in biological functions. Limited information is available on the S. mekongi peptidome. Therefore, this study aimed to identify S. mekongi peptides using in silico transcriptome mining and mass spectrometry approaches. Schistosoma peptide components were identified in adult worms, eggs, and infected mouse sera. Thirteen neuropeptide families were identified using in silico predictions from in-house transcriptomic databases of adult S. mekongi worms. Using mass spectrometry approaches, 118 peptides (from 54 precursor proteins) and 194 peptides (from 86 precursor proteins) were identified from adult worms and eggs, respectively. Importantly, eight unique peptides of the S. mekongi ubiquitin thioesterase, trabid, were identified in infected mouse sera 14, 28, and 56 days after infection. This protein may be a potential target for diagnosis of schistosomiasis. The S. mekongi peptide profiles determined in this study could be used for further drug and diagnostic development.
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Affiliation(s)
- Tipparat Thiangtrongjit
- Department of Molecular Tropical Medicine and Genetics, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand;
| | - Nattapon Simanon
- National Omics Center (NOC), National Science and Technology Development Agency, Pathum Thani 12120, Thailand;
| | - Poom Adisakwattana
- Department of Helminthology, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand;
| | - Yanin Limpanont
- Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand; (Y.L.); (P.C.); (Y.C.)
| | - Phiraphol Chusongsang
- Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand; (Y.L.); (P.C.); (Y.C.)
| | - Yupa Chusongsang
- Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand; (Y.L.); (P.C.); (Y.C.)
| | - Onrapak Reamtong
- Department of Molecular Tropical Medicine and Genetics, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand;
- Correspondence: ; Tel.: +66-(0)-2306-9138; Fax: +66-(0)-2306-9139
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49
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Mailhot B, Christin M, Tessandier N, Sotoudeh C, Bretheau F, Turmel R, Pellerin È, Wang F, Bories C, Joly-Beauparlant C, De Koninck Y, Droit A, Cicchetti F, Scherrer G, Boilard E, Sharif-Naeini R, Lacroix S. Neuronal interleukin-1 receptors mediate pain in chronic inflammatory diseases. J Exp Med 2021; 217:151879. [PMID: 32573694 PMCID: PMC7478735 DOI: 10.1084/jem.20191430] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 03/03/2020] [Accepted: 05/13/2020] [Indexed: 12/20/2022] Open
Abstract
Chronic pain is a major comorbidity of chronic inflammatory diseases. Here, we report that the cytokine IL-1β, which is abundantly produced during multiple sclerosis (MS), arthritis (RA), and osteoarthritis (OA) both in humans and in animal models, drives pain associated with these diseases. We found that the type 1 IL-1 receptor (IL-1R1) is highly expressed in the mouse and human by a subpopulation of TRPV1+ dorsal root ganglion neurons specialized in detecting painful stimuli, termed nociceptors. Strikingly, deletion of the Il1r1 gene specifically in TRPV1+ nociceptors prevented the development of mechanical allodynia without affecting clinical signs and disease progression in mice with experimental autoimmune encephalomyelitis and K/BxN serum transfer–induced RA. Conditional restoration of IL-1R1 expression in nociceptors of IL-1R1–knockout mice induced pain behavior but did not affect joint damage in monosodium iodoacetate–induced OA. Collectively, these data reveal that neuronal IL-1R1 signaling mediates pain, uncovering the potential benefit of anti–IL-1 therapies for pain management in patients with chronic inflammatory diseases.
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Affiliation(s)
- Benoit Mailhot
- Axe Neurosciences du Centre de recherche du CHU de Québec-Université Laval et Département de médecine moléculaire de l'Université Laval, Québec, Canada
| | - Marine Christin
- Department of Physiology and Cell Information Systems Group, McGill University, Montreal, Canada
| | - Nicolas Tessandier
- Axe Maladies infectieuses et immunitaires du Centre de recherche du CHU de Québec-Université Laval et Département de microbiologie-infectiologie et d'immunologie de l'Université Laval, Québec, Canada
| | - Chaudy Sotoudeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA
| | - Floriane Bretheau
- Axe Neurosciences du Centre de recherche du CHU de Québec-Université Laval et Département de médecine moléculaire de l'Université Laval, Québec, Canada
| | - Roxanne Turmel
- Axe Neurosciences du Centre de recherche du CHU de Québec-Université Laval et Département de médecine moléculaire de l'Université Laval, Québec, Canada
| | - Ève Pellerin
- Axe Neurosciences du Centre de recherche du CHU de Québec-Université Laval et Département de médecine moléculaire de l'Université Laval, Québec, Canada
| | - Feng Wang
- Centre de recherche CERVO, Québec, Canada
| | | | - Charles Joly-Beauparlant
- Axe Endocrinologie-néphrologie du Centre de recherche du CHU de Québec-Université Laval et Département de médecine moléculaire de l'Université Laval, Québec, Canada
| | | | - Arnaud Droit
- Axe Endocrinologie-néphrologie du Centre de recherche du CHU de Québec-Université Laval et Département de médecine moléculaire de l'Université Laval, Québec, Canada
| | - Francesca Cicchetti
- Axe Neurosciences du Centre de recherche du CHU de Québec-Université Laval et Département de psychiatrie et de neurosciences de l'Université Laval, Québec, Canada
| | - Grégory Scherrer
- Department of Cell Biology and Physiology, University of North Carolina Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC.,New York Stem Cell Foundation - Robertson Investigator, The University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Eric Boilard
- Axe Maladies infectieuses et immunitaires du Centre de recherche du CHU de Québec-Université Laval et Département de microbiologie-infectiologie et d'immunologie de l'Université Laval, Québec, Canada
| | - Reza Sharif-Naeini
- Department of Physiology and Cell Information Systems Group, McGill University, Montreal, Canada
| | - Steve Lacroix
- Axe Neurosciences du Centre de recherche du CHU de Québec-Université Laval et Département de médecine moléculaire de l'Université Laval, Québec, Canada
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50
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Torres MDT, Cao J, Franco OL, Lu TK, de la Fuente-Nunez C. Synthetic Biology and Computer-Based Frameworks for Antimicrobial Peptide Discovery. ACS NANO 2021; 15:2143-2164. [PMID: 33538585 PMCID: PMC8734659 DOI: 10.1021/acsnano.0c09509] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Antibiotic resistance is one of the greatest challenges of our time. This global health problem originated from a paucity of truly effective antibiotic classes and an increased incidence of multi-drug-resistant bacterial isolates in hospitals worldwide. Indeed, it has been recently estimated that 10 million people will die annually from drug-resistant infections by the year 2050. Therefore, the need to develop out-of-the-box strategies to combat antibiotic resistance is urgent. The biological world has provided natural templates, called antimicrobial peptides (AMPs), which exhibit multiple intrinsic medical properties including the targeting of bacteria. AMPs can be used as scaffolds and, via engineering, can be reconfigured for optimized potency and targetability toward drug-resistant pathogens. Here, we review the recent development of tools for the discovery, design, and production of AMPs and propose that the future of peptide drug discovery will involve the convergence of computational and synthetic biology principles.
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Affiliation(s)
- Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Jicong Cao
- Synthetic Biology Group, MIT Synthetic Biology Center, Department of Biological Engineering and Electrical Engineering and Computer Science, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Octavio L Franco
- Centro de Análises Proteômicas e Bioquímicas, Universidade Católica de Brasília, Brasília, DF 70790160, Brazil
- S-inova Biotech, Universidade Católica Dom Bosco, Campo Grande, MS 79117010, Brazil
| | - Timothy K Lu
- Synthetic Biology Group, MIT Synthetic Biology Center, Department of Biological Engineering and Electrical Engineering and Computer Science, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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