Sensory Glia Detect Repulsive Odorants and Drive Olfactory Adaptation

Duan, Duo; Zhang, Hu; Yue, Xiaomin; Fan, Yuedan; Xue, Yi; Shao, Jinchen; Ding, Gang; Du, Cuiwei; Li, Shitian; Cheng, Hankui; Zhang, Xiaoyan; Zou, Wenjuan; Liu, Jia; Zhao, Jian; Wang, Linmei; Wang, Zhiping; Xu, Suhong; Wen, Quan; Liu, Jie; Duan, Shumin; Kang, Lijun

doi:10.1016/j.neuron.2020.08.026

Cited by 46 publications

(119 citation statements)

References 84 publications

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“…Besides, a recent in vivo invertebrate study suggested a new two-receptor olfactory model where both ORNs and the glial supporting cells cooperate promoting olfactory adaptation, highlighting the importance of the cross talk between these cells at the peripheral level (Duan et al 2020 ). Indeed, olfactory adaptation has been suggested to reflect both the peripheral and the central nervous system structures (e.g., piriform cortex) involved in chemosensory processing (Iannilli et al 2017 ; Pellegrino et al 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Olfaction in patients with Parkinson’s disease: a new threshold test analysis through turning points trajectories

et al. 2021

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Olfactory deficit is a widely documented non-motor symptom in Parkinson’s disease (PD). Abnormal turning points trajectories through olfactory threshold testing have been recently reported in patients with olfactory dysfunction, who seem to adapt faster to olfactory stimuli, but data on PD patients are lacking. The aim of this study is to perform olfactory threshold test and explore the turning points trajectories in PD patients in comparison to normal controls. We recruited 59 PD patients without dementia, and no conditions that could influence evaluation of olfaction and cognition. Sixty healthy subjects served as controls. Patients and controls underwent a comprehensive olfactory evaluation with the Sniffin’ Sticks extended test assessing threshold, discrimination and identification and a full neuropsychological evaluation. Besides, threshold test data were analyzed examining all the turning points trajectories. PD patients showed a different olfactory threshold test pattern, i.e., faster olfactory adaptation, than controls with no effect of age. Normosmic PD patients showed different olfactory threshold test pattern, i.e., better threshold score, than normosmic controls. Visuospatial dysfunction was the only factor that significantly influenced this pattern. Olfactory threshold trajectories suggested a possible adaptation phenomenon in PD patients. Our data offered some new insights on normosmic PD patients, which appear to be a subset with a specific psychophysical profile. The analysis of the turning points trajectories, through an olfactory threshold test, could offer additional information on olfactory function in PD patients. Future larger studies should confirm these preliminary findings.

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Section: Discussionmentioning

confidence: 99%

Olfaction in patients with Parkinson’s disease: a new threshold test analysis through turning points trajectories

et al. 2021

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“…The individual or group of neurons involved in the sensing have been identified using ablation [9,16] and RNAi studies [17]. Additionally, sensory glia cells have also been implicated in olfaction [18,19], albeit in a neuron independent manner. The olfactory representation of molecules in the brain, however, remains an unsolved problem even with a considerable amount of work being put into deciphering the underlying structures and mechanisms involved.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A machine learning based analysis to probe the relationship between odorant structure and olfactory behaviour inC. elegans

Vishnoi

Sharma

2021

Preprint

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The chemical basis of smell remains an unsolved problem, with ongoing studies mapping perceptual descriptor data from human participants to the chemical structures using computational methods. These approaches are, however, limited by linguistic capabilities and inter-individual differences in participants. We use olfactory behaviour data from the nematode C. elegans, which has isogenic populations in a laboratory setting, and employ machine learning approaches for a binary classification task predicting whether or not the worm will be attracted to a given monomolecular odorant. Among others, we use architectures based on Natural Language Processing methods on the SMILES representation of chemicals for molecular descriptor generation and show that machine learning algorithms trained on the descriptors give robust prediction results. We further show, by data augmentation, that increasing the number of samples increases the accuracy of the models. From this detailed analysis, we are able to achieve accuracies comparable to that in human studies and infer that there exists a non trivial relationship between the features of chemical structures and the nematode's behaviour.

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“…For example, humans can discriminate more than one trillion odor stimuli (Bushdid et al, 2014). The effects of one given odorant can be influenced by many factors including intensity, context and experience (Bargmann, 2006;Gottfried, 2009;Li and Liberles, 2015;Harris et al, 2019;Duan et al, 2020). However, it remains poorly understood how intensity of odorant signals are encoded by the olfactory system in higher organisms because of the complexity of their nervous systems.…”

Section: Introductionmentioning

confidence: 99%

“…The nematode Caenorhabditis elegans offers a powerful model to mechanistically study how the nervous system responds to various odorants at the molecular, cellular and circuit levels. Equipped with a compact nervous system with only 302 neurons and 56 glial cells, C. elegans can sense a vast number of odors and execute a wide range of olfactory-related behaviors such as chemotaxis to food sources, pathogen avoidance, social feeding, olfactory associativelearning and imprinting (de Bono and Maricq, 2005;Bargmann, 2006;Chalasani et al, 2007;Ha et al, 2010;Ohno et al, 2014;Duan et al, 2020). Five pairs of sensory neurons have been identified as the main olfactory receptor neurons in C. elegans-amphid wing "A" cell (AWA), amphid wing "C" cell (AWC), amphid wing "B" cell (AWB), amphid neuron "H" cell with single ciliated sensory ending (ASH), and amphid neuron "L" cell with dual ciliated sensory endings (ADL) (Bargmann, 2006).…”

Section: Introductionmentioning

confidence: 99%

Data_Sheet_1.pdf

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Sensory Glia Detect Repulsive Odorants and Drive Olfactory Adaptation

Cited by 46 publications

References 84 publications

Olfaction in patients with Parkinson’s disease: a new threshold test analysis through turning points trajectories

Olfaction in patients with Parkinson’s disease: a new threshold test analysis through turning points trajectories

A machine learning based analysis to probe the relationship between odorant structure and olfactory behaviour inC. elegans

Data_Sheet_1.pdf

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