Analysis of fast calcium dynamics of honey bee olfactory coding

eLife, Год журнала: 2024, Номер 13

Опубликована: Сен. 5, 2024

Odour processing exhibits multiple parallels between vertebrate and invertebrate olfactory systems. Insects, in particular, have emerged as relevant models for studies because of the tractability their circuits. Here, we used fast calcium imaging to track activity projection neurons honey bee antennal lobe (AL) during stimulation at high temporal resolution. We observed a heterogeneity response profiles an abundance inhibitory activities, resulting various latencies stimulus-specific post-odour neural signatures. Recorded signals were fed mushroom body (MB) model constructed implementing fundamental features connectivity neurons, Kenyon cells (KC), MB output (MBON). The accounts increase odorant discrimination compared AL reveals recruitment two distinct KC populations that represent odorants aftersmell separate but temporally coherent objects. Finally, showed learning-induced modulation KC-to-MBON synapses can explain both variations associative learning scores across different conditioning protocols bees bees' latency. Thus, it provides simple explanation how time contingency stimulus reward be encoded without need tracking. This study broadens our understanding coding bees. It demonstrates based on rules with real physiological data aspects odour learning.

Язык: Английский

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Tyramine and its Amtyr1 receptor modulate attention in honey bees (Apis mellifera)

eLife, Год журнала: 2023, Номер 12

Опубликована: Окт. 10, 2023

Animals must learn to ignore stimuli that are irrelevant survival and attend ones enhance survival. When a stimulus regularly fails be associated with an important consequence, subsequent excitatory learning about can delayed, which is form of nonassociative conditioning called 'latent inhibition'. Honey bees show latent inhibition toward odor they have experienced without association food reinforcement. Moreover, individual honey from the same colony differ in degree inhibition, these differences genetic basis. To investigate mechanisms underly we selected two bee lines for high low respectively. We crossed those mapped Quantitative Trait Locus region genome contains tyramine receptor gene Amtyr1 [We use denote AmTYR1 throughout text.]. then disruption signaling either pharmacologically or through RNAi qualitatively changes expression but has little slight effects on appetitive conditioning, results suggest modulates inhibitory processing CNS. Electrophysiological recordings brain during pharmacological blockade consistent model indirectly regulates at synapses Our therefore identify distinct Amtyr1-based modulatory pathway this type learning, propose how acts as gain control modulate hebbian plasticity defined shown elsewhere modulation also underlies potentially adaptive intracolonial among individuals benefit Finally, our neural suggests mechanism broad pleiotropy several different behaviors.

Язык: Английский

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Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field

Опубликована: Янв. 21, 2025

Preventing beneficial insects like honey bees ( Apis mellifera ) from contacting pesticides on crops using odorants could counter current pollinator declines. However, the discovery of behaviorally aversive is impeded by complexity bee olfactory system where >180 odorant receptors detect volatiles and generate valence. To solve this systems-level challenge we generated a machine-learning model to predict valence chemical structure published behavior data in bees. We refine predictive generating species level behavioral for honeybees Drosophila an initial set novel predicted repellents. The improved second computational was then used screen space >50 million compounds identify >130 repellent candidates. Behavioral validation laboratory show high success. Additional testing top seven candidates freely foraging field assay confirmed strong repellency, thus predicting probability repel pesticide-treated crops. Machine learning, with iterative modeling, therefore provides powerful approach rational control which limited available.

Язык: Английский

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Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field

Опубликована: Янв. 21, 2025

Preventing beneficial insects like honey bees ( Apis mellifera ) from contacting pesticides on crops using odorants could counter current pollinator declines. However, the discovery of behaviorally aversive is impeded by complexity bee olfactory system where >180 odorant receptors detect volatiles and generate valence. To solve this systems-level challenge we generated a machine-learning model to predict valence chemical structure published behavior data in bees. We refine predictive generating species level behavioral for honeybees Drosophila an initial set novel predicted repellents. The improved second computational was then used screen space >50 million compounds identify >130 repellent candidates. Behavioral validation laboratory show high success. Additional testing top seven candidates freely foraging field assay confirmed strong repellency, thus predicting probability repel pesticide-treated crops. Machine learning, with iterative modeling, therefore provides powerful approach rational control which limited available.

Язык: Английский

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Spatiotemporal Patterns in Profiles of Amino Acids Indicates They Are Unlikely Singular Olfactory Cues Underlying Natal Homing in Migratory Fishes

Freshwater Biology, Год журнала: 2025, Номер 70(2)

Опубликована: Фев. 1, 2025

ABSTRACT Migratory fishes are renowned for their ability to home natal streams spawning. Learned olfactory cues play a critical role in homing of Pacific salmon and other fishes, but the underlying chemical signature remains poorly understood after decades study. The molecules that convey stream‐specific odour must differ among sites remain constant over time. Among leading odorant candidates amino acids; however, little research has assessed spatial temporal variability acid profiles streams. We report comprehensive study dissolved acids as potential by migratory fish. Specifically, we profiled water from 23 upper Laurentian Great Lakes basin 2 years. investigated variation (1) regions rivers within year, (2) between years (3) across seasons migration early life history stream. Liquid‐chromatography tandem mass spectrometry revealed nanomolar concentrations most 20 L‐amino measured, above levels detectable studied fishes. Moreover, were temporally stable an annual season adult spawning through offspring early‐life development However, differences evident primarily large geographic distances (among regions) not tributaries or Collectively, our results indicate may be consistent components rivers' suggest additional likely important specific sites. future studies consider combined importance classes. Understanding basis olfactory‐guided is especially human activities could alter thereby disrupt fish migrations negatively impact population recruitment.

Язык: Английский

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Fast Learners: One Trial Olfactory Learning in Insects

Frontiers in Ecology and Evolution, Год журнала: 2022, Номер 10

Опубликована: Апрель 11, 2022

Despite their comparatively small brains, insects are able to survive and thrive in environment. In the past, it was thought that driven mainly by instincts. However, today is well established they possess unique abilities learn use experience future decisions. Like many higher animals acquire retain information on when where forage, which mate choose, lay eggs how navigate complex habitats. Learning can be surprisingly fast with only one single encounter a suitable food source or oviposition site shaping an insect's preference for up lifetime. this review, we discuss scope limits of insect learning, focusing specific olfactory raise question whether currently used learning paradigms artificial lab set-ups answer all ecologically relevant questions.

Язык: Английский

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New insights on the adsorption of floral odorants on Apis cerana cerana olfactory receptor AcerOr1: Theoretical modeling and thermodynamic study

International Journal of Biological Macromolecules, Год журнала: 2023, Номер 236, С. 124007 - 124007

Опубликована: Март 13, 2023

Язык: Английский

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Ozone exposure induces metabolic stress and olfactory memory disturbance in honey bees.

Chemosphere, Год журнала: 2023, Номер 346, С. 140647 - 140647

Опубликована: Ноя. 8, 2023

Язык: Английский

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Recent advances on the influence of fipronil on insect behavior

Current Opinion in Insect Science, Год журнала: 2024, Номер 65, С. 101251 - 101251

Опубликована: Авг. 13, 2024

Язык: Английский

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Sub-lethal pesticide exposure interferes with honey bee memory of learnt colours

The Science of The Total Environment, Год журнала: 2025, Номер 962, С. 178460 - 178460

Опубликована: Янв. 1, 2025

Язык: Английский

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