Dataset Information

Automated monitoring and quantitative analysis of feeding behaviour in Drosophila.

ABSTRACT: Food ingestion is one of the defining behaviours of all animals, but its quantification and analysis remain challenging. This is especially the case for feeding behaviour in small, genetically tractable animals such as Drosophila melanogaster. Here, we present a method based on capacitive measurements, which allows the detailed, automated and high-throughput quantification of feeding behaviour. Using this method, we were able to measure the volume ingested in single sips of an individual, and monitor the absorption of food with high temporal resolution. We demonstrate that flies ingest food by rhythmically extending their proboscis with a frequency that is not modulated by the internal state of the animal. Instead, hunger and satiety homeostatically modulate the microstructure of feeding. These results highlight similarities of food intake regulation between insects, rodents, and humans, pointing to a common strategy in how the nervous systems of different animals control food intake.

SUBMITTER: Itskov PM

PROVIDER: S-EPMC4143931 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automated monitoring and quantitative analysis of feeding behaviour in Drosophila.

Itskov Pavel M PM Moreira José-Maria JM Vinnik Ekaterina E Lopes Gonçalo G Safarik Steve S Dickinson Michael H MH Ribeiro Carlos C

Nature communications 20140804

Food ingestion is one of the defining behaviours of all animals, but its quantification and analysis remain challenging. This is especially the case for feeding behaviour in small, genetically tractable animals such as Drosophila melanogaster. Here, we present a method based on capacitive measurements, which allows the detailed, automated and high-throughput quantification of feeding behaviour. Using this method, we were able to measure the volume ingested in single sips of an individual, and mo ...[more]

PMID: 25087594

Similar Datasets

Project description:Focus of this study is to design an automated image processing pipeline for handling uncontrolled acquisition conditions of images acquired in the field. The pipeline has been tested on the automated identification and count of uncapped brood cells in honeybee (Apis Mellifera) comb images to reduce the workload of beekeepers during the study of the hygienic behavior of honeybee colonies. The images used to develop and test the model were acquired by beekeepers on different days and hours in summer 2020 and under uncontrolled conditions. This resulted in images differing for background noise, illumination, color, comb tilts, scaling, and comb sizes. All the available 127 images were manually cropped to approximately include the comb area. To obtain an unbiased evaluation, the cropped images were randomly split into a training image set (50 images), which was used to develop and tune the proposed model, and a test image set (77 images), which was solely used to test the model. To reduce the effects of varied illuminations or exposures, three image enhancement algorithms were tested and compared followed by the Hough Transform, which allowed identifying individual cells to be automatically counted. All the algorithm parameters were automatically chosen on the training set by grid search. When applied to the 77 test images the model obtained a correlation of 0.819 between the automated counts and the experts' counts. To provide an assessment of our model with publicly available images acquired by a different equipment and under different acquisition conditions, we randomly extracted 100 images from a comb image dataset made available by a recent literature work. Though it has been acquired under controlled exposure, the images in this new set have varied illuminations; anyhow, our pipeline obtains a correlation between automatic and manual counts equal to 0.997. In conclusion, our tests on the automatic count of uncapped honey bee comb cells acquired in the field and on images extracted from a publicly available dataset suggest that the hereby generated pipeline successfully handles varied noise artifacts, illumination, and exposure conditions, therefore allowing to generalize our method to different acquisition settings. Results further improve when the acquisition conditions are controlled.

Project description:ObjectiveMore than 300 genetic variants have been robustly associated with measures of human adiposity. Highly penetrant mutations causing human obesity do so largely by disrupting satiety pathways in the brain and increasing food intake. Most of the common obesity-predisposing variants are in, or near, genes expressed highly in the brain, but little is known of their function. Exploring the biology of these genes at scale in mammalian systems is challenging. We sought to establish and validate the use of a multicomponent screen for feeding behaviour phenotypes, taking advantage of the tractable model organism Drosophila melanogaster.MethodsWe validated a screen for feeding behaviour in Drosophila by comparing results after disrupting the expression of centrally expressed genes that influence energy balance in flies to those of 10 control genes. We then used this screen to explore the effects of disrupted expression of genes either a) implicated in energy homeostasis through human genome-wide association studies (GWAS) or b) expressed and nutritionally responsive in specific populations of hypothalamic neurons with a known role in feeding/fasting.ResultsUsing data from the validation study to classify responses, we studied 53 Drosophila orthologues of genes implicated by human GWAS in body mass index and found that 15 significantly influenced feeding behaviour or energy homeostasis in the Drosophila screen. We then studied 50 Drosophila homologues of 47 murine genes reciprocally nutritionally regulated in POMC and agouti-related peptide neurons. Seven of these 50 genes were found by our screen to influence feeding behaviour in flies.ConclusionWe demonstrated the utility of Drosophila as a tractable model organism in a high-throughput genetic screen for food intake phenotypes. This simple, cost-efficient strategy is ideal for high-throughput interrogation of genes implicated in feeding behaviour and obesity in mammals and will facilitate the process of reaching a functional understanding of obesity pathogenesis.

Dataset Information

Automated monitoring and quantitative analysis of feeding behaviour in Drosophila.

Publications

Automated monitoring and quantitative analysis of feeding behaviour in Drosophila.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets