skip to main content

Explore Research Products in the NSF-PAR

Title: Idiosyncratic learning performance in flies
Individuals vary in their innate behaviours, even when they have the same genome and have been reared in the same environment. The extent of individuality in plastic behaviours, like learning, is less well characterized. Also unknown is the extent to which intragenotypic differences in learning generalize: if an individual performs well in one assay, will it perform well in other assays? We investigated this using the fruit fly Drosophila melanogaster , an organism long-used to study the mechanistic basis of learning and memory. We found that isogenic flies, reared in identical laboratory conditions, and subject to classical conditioning that associated odorants with electric shock, exhibit clear individuality in their learning responses. Flies that performed well when an odour was paired with shock tended to perform well when the odour was paired with bitter taste or when other odours were paired with shock. Thus, individuality in learning performance appears to be prominent in isogenic animals reared identically, and individual differences in learning performance generalize across some aversive sensory modalities. Establishing these results in flies opens up the possibility of studying the genetic and neural circuit basis of individual differences in learning in a highly suitable model organism.  more » « less
Award ID(s):
1764269
NSF-PAR ID:
10328937
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Biology Letters
Volume:
18
Issue:
2
ISSN:
1744-957X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ( , Genes, Brain and Behavior)
    Abstract

    Learning and memory are critical functions for all animals, giving individuals the ability to respond to changes in their environment. Within populations, individuals vary, however the mechanisms underlying this variation in performance are largely unknown. Thus, it remains to be determined what genetic factors cause an individual to have high learning ability and what factors determine how well an individual will remember what they have learned. To genetically dissect learning and memory performance, we used theDrosophilasynthetic population resource (DSPR), a multiparent mapping resource in the model systemDrosophila melanogaster, consisting of a large set of recombinant inbred lines (RILs) that naturally vary in these and other traits. Fruit flies can be trained in a “heat box” to learn to remain on one side of a chamber (place learning) and can remember this (place memory) over short timescales. Using this paradigm, we measured place learning and memory for ~49 000 individual flies from over 700 DSPR RILs. We identified 16 different loci across the genome that significantly affect place learning and/or memory performance, with 5 of these loci affecting both traits. To identify transcriptomic differences associated with performance, we performed RNA‐Seq on pooled samples of seven high performing and seven low performing RILs for both learning and memory and identified hundreds of genes with differences in expression in the two sets. Integrating our transcriptomic results with the mapping results allowed us to identify nine promising candidate genes, advancing our understanding of the genetic basis underlying natural variation in learning and memory performance.

     
    more » « less
  2. ; ; ; ( , eLife)
    Many behaviors that we perform everyday, including something as familiar as making a peanut-butter sandwich, consist of a sequence of recognizable acts. These acts may include, for example, holding a knife and opening a jar. Yet often neither the sequence nor the individual acts are always performed in the exact same way. For example, there are many ways to hold a knife and there are many ways to open a jar, meaning neither of these actions could be called “stereotyped”. A lack of stereotypy makes it difficult for a computer to automatically recognize the individual acts in a sequence. This same problem would apply to other common behaviors, such as walking around somewhere you have not visited before. While we easily recognize it when we see it, walking is not a stereotyped behavior. It consists of a series of movements that differ between individuals, and even in the same individual at different times. So how can someone automatically recognize the individual acts in a non-stereotyped behavior like walking? To begin to find out, Tao et al. developed a mathematical model that can recognize the walking behavior of a fruit fly. Existing recordings of fruit flies walking were analyzed using a type of mathematical model called a Hierarchical Hidden Markov Model (often shortened to HHMM). Such models assume that there are hidden states that influence the behaviors we can see. For example, someone’s chances of going skiing (an observable behavior) depend on whether or not it is winter (a hidden state). The HHMM revealed that the seemingly random wanderings of a fly consist of ten types of movement. These include the “meander”, the “stop-and-walk”, as well as right turns and left turns. Exposing the flies to a pleasant odor – in this case, apple cider vinegar – altered how the flies walked by changing the time they spent performing each of the different types of movement. All flies in the dataset used the same ten movements, but in different proportions. This means that each fly showed an individual pattern of movement. In fact, the differences between flies are so great that Tao et al. argue that there is no such thing as an average walk for a fruit fly. The model represents a complete description of how fruit flies walk. It thus provides clues to the processes that transform an animal’s sensory experiences into behavior. But it also has potential clinical applications. Similar models for human behaviors could help reveal behaviors that are abnormal because of disease. Normal behaviors also show variability, and some diseases increase or decrease this variability. By making it easier to detect these changes, mathematical models could support earlier diagnosis of medical conditions. 
    more » « less
  3. ; ; ; ; ; ( , eLife)
    null (Ed.)
    Cells in the brain, liver and skin, as well as many other organs, all contain the same DNA, yet behave in very different ways. This is because before a gene can produce its corresponding protein, it must first be transcribed into messenger RNA. As an organism grows, the transcription of certain genes is switched on or off by regulatory molecules called transcription factors, which guide cells towards a specific ‘fate’. These molecules bind to specific locations within the regulatory regions of DNA, and for decades biologist have tried to use the arrangement of these sites to predict which proteins a cell will make. Theoretical models known as thermodynamic models have been able to successfully predict transcription in bacteria. However, this has proved more challenging to do in eukaryotes, such as yeast, fruit flies and humans. One of the key differences is that DNA in eukaryotes is typically tightly wound into bundles called nucleosomes, which must be disentangled in order for transcription factors to access the DNA. Previous thermodynamic models have suggested that DNA in eukaryotes randomly switches between being in a wound and unwound state. The models assume that once unwound, regulatory proteins stabilize the DNA in this form, making it easier for other transcription factors to bind to the DNA. Now, Eck, Liu et al. have tested some of these models by studying the transcription of a gene involved in the development of fruit flies. The experiments showed that no thermodynamic model could accurately mimic how this gene is regulated in the embryos of fruit flies. This led Eck, Liu et al. to identify a model that is better at predicting the activation pattern of this developmental gene. In this model, instead of just ‘locking’ DNA into an unwound shape, transcription factors can also actively speed up the unwinding of DNA. This improved understanding builds towards the goal of predicting gene regulation, where DNA sequences can be used to tell where and when cell decisions will be made. In the future, this could allow the development of new types of therapies that can regulate transcription in different diseases. 
    more » « less
  4. ; ; ; ; ; ; ( , Ecological Entomology)

    1.Draculaorchids mimic mushrooms in appearance and scent, and are pollinated by mushroom flies. This study examined the guilds of insect visitors toDraculaspecies and to co‐occurring mushrooms in an Ecuadorean cloud forest. Significant visitor overlap was documented (mostly in the generaZygothricaandHirtodrosophila; Drosophilidae). To test the brood‐site mimicry hypothesis, behavioural observations were also performed and rearing success was examined.

    2. Many fly species that visitDraculaare also found on mushrooms. Furthermore, the presence of pollinia enabled us to document the fact that particular individuals visiting mushrooms also visitDracula. Roughly two‐thirds of the visitors to these unusual flowers are fungal‐associated, and the rest appear to beDraculaspecialists.

    3. A variety of behaviours common on both host groups were observed, including courtship semaphoring, feeding, sheltering, defending territory, and mating. Given this suite of shared reproductive behaviour, it was hypothesised that flies may also oviposit in both substrates. Flies were reared from mushrooms,Draculaspp., and other flowers (controls) to determine which substrate led to the highest fitness for the flies. Very few flies emerged from theDraculaflowers and there was no species overlap between the emergent insects and the known pollinators.

    4. Despite the fact that there is evidence for brood‐site mimicry, theDraculasituation is complex. All the flies could derive many of the same fitness benefits from the flowers as they do from mushrooms (shelter, food, mating), but they are clearly not gaining the same fitness benefit, in terms of progeny, on flowers compared with mushrooms.

     
    more » « less
  5. ; ( , Philosophical Transactions of the Royal Society B: Biological Sciences)
    The Price equation embodies the ‘conditions approach’ to evolution in which the Darwinian conditions of heritable variation in fitness are represented in equation form. The equation can be applied recursively, leading to a partition of selection at the group and individual levels. After reviewing the well-known issues with the Price partition, as well as issues with a partition based on contextual analysis, we summarize a partition of group and individual selection based on counterfactual fitness, the fitness that grouped cells would have were they solitary. To understand ‘group selection’ in multi-level selection models, we assume that only group selection can make cells suboptimal when they are removed from the group. Our analyses suggest that there are at least three kinds of selection that can be occurring at the same time: group-specific selection along with two kinds of individual selection, within-group selection and global individual selection. Analyses based on counterfactual fitness allow us to specify how close a group is to being a pseudo-group, and this can be a basis for quantifying progression through an evolutionary transition in individuality (ETI). During an ETI, fitnesses at the two levels, group and individual, become decoupled, in the sense that fitness in a group may be quite high, even as counterfactual fitness goes to zero. This article is part of the theme issue ‘Fifty years of the Price equation’. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email