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Beito MR, Ashraf S, Odogwu D, Harmancey R. Role of Ectopic Olfactory Receptors in the Regulation of the Cardiovascular-Kidney-Metabolic Axis. Life (Basel) 2024; 14:548. [PMID: 38792570 PMCID: PMC11122380 DOI: 10.3390/life14050548] [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: 04/02/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/26/2024] Open
Abstract
Olfactory receptors (ORs) represent one of the largest yet least investigated families of G protein-coupled receptors in mammals. While initially believed to be functionally restricted to the detection and integration of odors at the olfactory epithelium, accumulating evidence points to a critical role for ectopically expressed ORs in the regulation of cellular homeostasis in extranasal tissues. This review aims to summarize the current state of knowledge on the expression and physiological functions of ectopic ORs in the cardiovascular system, kidneys, and primary metabolic organs and emphasizes how altered ectopic OR signaling in those tissues may impact cardiovascular-kidney-metabolic health.
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Affiliation(s)
| | | | | | - Romain Harmancey
- Division of Cardiology, Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.R.B.); (S.A.); (D.O.)
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2
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Lalis M, Hladiš M, Khalil SA, Briand L, Fiorucci S, Topin J. M2OR: a database of olfactory receptor-odorant pairs for understanding the molecular mechanisms of olfaction. Nucleic Acids Res 2024; 52:D1370-D1379. [PMID: 37870437 PMCID: PMC10767820 DOI: 10.1093/nar/gkad886] [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: 08/13/2023] [Revised: 09/13/2023] [Accepted: 10/04/2023] [Indexed: 10/24/2023] Open
Abstract
Mammalian sense of smell is triggered by interaction between odorant molecules and a class of proteins, called olfactory receptors (ORs). These receptors, expressed at the surface of olfactory sensory neurons, encode myriad of distinct odors via a sophisticated activation pattern. However, determining the molecular recognition spectrum of ORs remains a major challenge. The Molecule to Olfactory Receptor database (M2OR, https://m2or.chemsensim.fr/) provides curated data that allows an easy exploration of the current state of the research on OR-molecule interaction. We have gathered a database of 75,050 bioassay experiments for 51 395 distinct OR-molecule pairs. Drawn from published literature and public databases, M2OR contains information about OR responses to molecules and their mixtures, receptor sequences and experimental details. Users can obtain information on the activity of a chosen molecule or a group of molecules, or search for agonists for a specific OR or a group of ORs. Advanced search allows for fine-grained queries using various metadata such as species or experimental assay system, and the database can be queried by multiple inputs via a batch search. Finally, for a given search query, users can access and download a curated aggregation of the experimental data into a binarized combinatorial code of olfaction.
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Affiliation(s)
- Maxence Lalis
- Institut de Chimie de Nice, Université Côte d’Azur, UMR 7272 CNRS, 06108 Nice, France
| | - Matej Hladiš
- Institut de Chimie de Nice, Université Côte d’Azur, UMR 7272 CNRS, 06108 Nice, France
| | - Samar Abi Khalil
- Institut de Chimie de Nice, Université Côte d’Azur, UMR 7272 CNRS, 06108 Nice, France
| | - Loïc Briand
- Centre des Sciences du Goût et de l’Alimentation, CNRS, INRAE, Institut Agro, Université de Bourgogne, F-21000 Dijon, France
| | - Sébastien Fiorucci
- Institut de Chimie de Nice, Université Côte d’Azur, UMR 7272 CNRS, 06108 Nice, France
| | - Jérémie Topin
- Institut de Chimie de Nice, Université Côte d’Azur, UMR 7272 CNRS, 06108 Nice, France
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3
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Mittal A, Ahuja G. Advancing chemical carcinogenicity prediction modeling: opportunities and challenges. Trends Pharmacol Sci 2023; 44:400-410. [PMID: 37183054 DOI: 10.1016/j.tips.2023.04.002] [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: 03/14/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 05/16/2023]
Abstract
Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challenges concerning the inadequacy of available experimentally validated (non)carcinogen datasets and variabilities within bioassays, which contribute to the compromised model training. Existing AI solutions that leverage classical chemistry-driven descriptors do not provide adequate biological interpretability involved in imparting carcinogenicity. This highlights the urgency to devise alternative AI strategies. We propose multiple strategies, including implementing data-driven (integrated databases) and known carcinogen-characteristic-derived features to overcome these apparent shortcomings. In summary, these next-generation approaches will continue facilitating robust chemical carcinogenicity prediction, concomitant with deeper mechanistic insights.
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Affiliation(s)
- Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi, 110020, India.
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi, 110020, India.
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4
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Qian WW, Wei JN, Sanchez-Lengeling B, Lee BK, Luo Y, Vlot M, Dechering K, Peng J, Gerkin RC, Wiltschko AB. Metabolic activity organizes olfactory representations. eLife 2023; 12:e82502. [PMID: 37129358 PMCID: PMC10154027 DOI: 10.7554/elife.82502] [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: 08/06/2022] [Accepted: 04/11/2023] [Indexed: 05/03/2023] Open
Abstract
Hearing and vision sensory systems are tuned to the natural statistics of acoustic and electromagnetic energy on earth and are evolved to be sensitive in ethologically relevant ranges. But what are the natural statistics of odors, and how do olfactory systems exploit them? Dissecting an accurate machine learning model (Lee et al., 2022) for human odor perception, we find a computable representation for odor at the molecular level that can predict the odor-evoked receptor, neural, and behavioral responses of nearly all terrestrial organisms studied in olfactory neuroscience. Using this olfactory representation (principal odor map [POM]), we find that odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related; metabolic reaction sequences (Caspi et al., 2014) also follow smooth paths in POM despite large jumps in molecular structure. Just as the brain's visual representations have evolved around the natural statistics of light and shapes, the natural statistics of metabolism appear to shape the brain's representation of the olfactory world.
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Affiliation(s)
- Wesley W Qian
- OsmoCambridgeUnited States
- Google Research, Brain TeamCambridgeUnited States
| | | | | | - Brian K Lee
- Google Research, Brain TeamCambridgeUnited States
| | - Yunan Luo
- Department of Computer Science, University of IllinoisUrbanaUnited States
| | | | | | - Jian Peng
- Department of Computer Science, University of IllinoisUrbanaUnited States
| | - Richard C Gerkin
- OsmoCambridgeUnited States
- Google Research, Brain TeamCambridgeUnited States
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5
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Abstract
Chemical biosensors are an increasingly ubiquitous part of our lives. Beyond enzyme-coupled assays, recent synthetic biology advances now allow us to hijack more complex biosensing systems to respond to difficult to detect analytes, such as chemical small molecules. Here, we briefly overview recent advances in the biosensing of small molecules, including nucleic acid aptamers, allosteric transcription factors, and two-component systems. We then look more closely at a recently developed chemical sensing system, G protein-coupled receptor (GPCR)-based sensors. Finally, we consider the chemical sensing capabilities of the largest GPCR subfamily, olfactory receptors (ORs). We examine ORs' role in nature, their potential as a biomedical target, and their ability to detect compounds not amenable for detection using other biological scaffolds. We conclude by evaluating the current challenges, opportunities, and future applications of GPCR- and OR-based sensors.
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Affiliation(s)
- Amisha Patel
- School
of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Pamela Peralta-Yahya
- School
of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States,School
of Chemistry and Biochemistry, Georgia Institute
of Technology, Atlanta, Georgia 30332, United States,E-mail:
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Mittal A, Mohanty SK, Gautam V, Arora S, Saproo S, Gupta R, Sivakumar R, Garg P, Aggarwal A, Raghavachary P, Dixit NK, Singh VP, Mehta A, Tayal J, Naidu S, Sengupta D, Ahuja G. Artificial intelligence uncovers carcinogenic human metabolites. Nat Chem Biol 2022; 18:1204-1213. [PMID: 35953549 DOI: 10.1038/s41589-022-01110-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 07/07/2022] [Indexed: 12/14/2022]
Abstract
The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations.
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Affiliation(s)
- Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Sanjay Kumar Mohanty
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Vishakha Gautam
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Sakshi Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Sheetanshu Saproo
- Department of Bio-Medical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - Ria Gupta
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Roshan Sivakumar
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Prakriti Garg
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Anmol Aggarwal
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Padmasini Raghavachary
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Nilesh Kumar Dixit
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India
| | - Vijay Pal Singh
- CSIR-Institute of Genomics & Integrative Biology, New Delhi, Delhi, India
| | - Anurag Mehta
- Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, Delhi, India
| | - Juhi Tayal
- Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, Delhi, India
| | - Srivatsava Naidu
- Department of Bio-Medical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - Debarka Sengupta
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India.
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi, Okhla, Phase III, New Delhi, Delhi, India.
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7
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Abstract
What makes a molecule have a smell? This simple question represents a significant gap in our understanding of olfaction. To answer it, we trained models to predict whether molecules were odorous based on molecular characteristics and noted which characteristics were needed to make correct predictions. We found that molecules with sufficient volatility and hydrophobicity are generally odorous, which suggests that reaching olfactory receptors is the dominant barrier for prospective olfactory stimuli. Based on these criteria, there are billions of molecules that are likely odorous but have never been smelled. We can now recognize an odorous molecule from its structure, and this guides us to discover new classes of odorants and include all types of odorants in our study of smell. In studies of vision and audition, stimuli can be chosen to span the visible or audible spectrum; in olfaction, the axes and boundaries defining the analogous odorous space are unknown. As a result, the population of olfactory space is likewise unknown, and anecdotal estimates of 10,000 odorants have endured. The journey a molecule must take to reach olfactory receptors (ORs) and produce an odor percept suggests some chemical criteria for odorants: a molecule must 1) be volatile enough to enter the air phase, 2) be nonvolatile and hydrophilic enough to sorb into the mucous layer coating the olfactory epithelium, 3) be hydrophobic enough to enter an OR binding pocket, and 4) activate at least one OR. Here, we develop a simple and interpretable quantitative model that reliably predicts whether a molecule is odorous or odorless based solely on the first three criteria. Applying our model to a database of all possible small organic molecules, we estimate that at least 40 billion possible compounds are odorous, six orders of magnitude larger than current estimates of 10,000. With this model in hand, we can define the boundaries of olfactory space in terms of molecular volatility and hydrophobicity, enabling representative sampling of olfactory stimulus space.
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