Periodic Reporting for period 1 - SocBehGenoPheno (Genetic basis of emergent social behaviour from genotype-phenotype mapping)
Período documentado: 2019-07-01 hasta 2021-06-30
Mutations can affect many behavioural traits and, although improving one, might worsen many others. One hypothesis suggests that this is a particular issue in animal behaviour if based on many degrees of freedom: diverse environmental effects can lead to different phenotypes for the same genotype, i.e. many-to-many genotype-phenotype mapping. An alternative hypothesis suggests that the phenotypic behavioural space is rather low dimensional and hence tolerant to genetic changes: different genotypes can give rise to the same phenotype, i.e. many-to-few genotype-phenotype mapping – a result of neutral mutations, which are neither beneficial nor detrimental to the organism’s fitness. Until now, these conflicting hypotheses have not been tested rigorously in species other than bacteria, viruses and plants. Moreover, they have never been tested in a social context due to challenges of connecting changes in genes with specific social behavioural phenotypes . This was mainly because the animals whose collective behaviour has received the most attention, such as swarming ants, schooling fish, and flocking birds, are not particularly amenable to genetic manipulation. Furthermore, extracting a behavioural phenotype requires an accurate representation of the fine-scale dynamics of individual motion and interactions, not always approachable even with modern tracking techniques. Judging from these points, the nematode worm Caenorhabditis elegans is a perfect system for genotype-phenotype mapping: it displays rich social dynamics during feeding (so-called collective feeding), it is genetically tractable, and owing to recent advances in robotic imaging, completely trackable, even in large groups. For the first time, all these advantages will be combined in a single project, allowing to test the hypotheses mentioned above in a rigorous and structured way using quantitative phenotyping and computational modelling.
The aim of this project is to quantify the genotype-phenotype mapping in C. elegans social behaviour using quantitative collective modelling based on high-throughput tracking data. The specific goals describe a three-stage procedure:
Goal 1: Develop a dictionary of worm behavioural states. This goal will be accomplished in two steps: I will first identify various behavioural states based on worm postures and tracking data and then quantify transitions between the different worm states within this behavioural map.
Goal 2: Build an agent-based model (ABM) and refine it to match the experimental summary statistics. This work will be also done in two steps: I will build an ABM that captures multiple behavioural worm states and subsequently parameterise and refine it to match the experimental summary statistics.
Goal 3: Derive and assess genotype-phenotype mapping by extracting a low-dimensional representation of the model. This is the most ambitious and risky goal. To achieve it, I will extract phenotypes and test their similarity among almost 200 C. elegans strains.
The aim of this project is to quantify the genotype-phenotype mapping in C. elegans social behaviour using quantitative collective modelling based on high-throughput tracking data. The specific goals describe a three-stage procedure:
Goal 1: Develop a dictionary of worm behavioural states. This goal will be accomplished in two steps: I will first identify various behavioural states based on worm postures and tracking data and then quantify transitions between the different worm states within this behavioural map.
Goal 2: Build an agent-based model (ABM) and refine it to match the experimental summary statistics. This work will be also done in two steps: I will build an ABM that captures multiple behavioural worm states and subsequently parameterise and refine it to match the experimental summary statistics.
Goal 3: Derive and assess genotype-phenotype mapping by extracting a low-dimensional representation of the model. This is the most ambitious and risky goal. To achieve it, I will extract phenotypes and test their similarity among almost 200 C. elegans strains.
In order to study the genetic basis of collective behaviour I focused on the wave-like propagation of worms on a food lawn of bacteria, with the social (npr-1) strain showing a well-defined wave front and more dispersed worms in its wake. In contrast, the asocial N2 lab reference mutant shows more network-like protrusions at the wave front, i.e. less cohesion.
A major achievement so far toward Goal 2 has been to design summary statistics of the wave-front characteristics using a roughness index. Remarkably, this shows fascinating scaling behaviour, different for the mutants. Another major achievement was a new method to measure the uptake of bacteria by the worms, based on bioluminescent bacteria. This allowed to understand the differential of uptake depending on mutant and location in the wave. This was published in:
Ding SS, Romenskyy M, Sarkisyan KS, Brown AEX. Measuring Caenorhabditis elegans spatial foraging and food intake using bioluminescent bacteria. Genetics 214(3): 577-587 (2020); doi: 10.1534/genetics.119.302804.
Ongoing work is on Goals 1, 2, and 3: Regarding Goal 1, a principal-component-based analysis method was developed, capturing subtle differences in posture according to worm density and mutants. Regarding Goal 2, an agent-based model is currently refined to capture observed network structures of collective worm behaviour off food and wave propagation with different scaling behaviour on food. Key ingredients are local worm-worm alignment, oxygen sensing, and food uptake. Regarding Goal 3, the low-dimensional representation will be the wave (and network) formation. Key challenges will be separation of subtle changes in collective behaviour in different mutants, while extracting the main robust features.
Due to Covid-19 lockdowns, dissemination occurred in Year 1 mainly through seminar talks, presentation at the Quantitative Biology of C. elegans Behavior meeting in Amsterdam and lecturing on the MSc in Applied Biology and Biotechnology. The results have been also disseminated through social media, in particular using twitter account of the fellow and the project webpage.
A major achievement so far toward Goal 2 has been to design summary statistics of the wave-front characteristics using a roughness index. Remarkably, this shows fascinating scaling behaviour, different for the mutants. Another major achievement was a new method to measure the uptake of bacteria by the worms, based on bioluminescent bacteria. This allowed to understand the differential of uptake depending on mutant and location in the wave. This was published in:
Ding SS, Romenskyy M, Sarkisyan KS, Brown AEX. Measuring Caenorhabditis elegans spatial foraging and food intake using bioluminescent bacteria. Genetics 214(3): 577-587 (2020); doi: 10.1534/genetics.119.302804.
Ongoing work is on Goals 1, 2, and 3: Regarding Goal 1, a principal-component-based analysis method was developed, capturing subtle differences in posture according to worm density and mutants. Regarding Goal 2, an agent-based model is currently refined to capture observed network structures of collective worm behaviour off food and wave propagation with different scaling behaviour on food. Key ingredients are local worm-worm alignment, oxygen sensing, and food uptake. Regarding Goal 3, the low-dimensional representation will be the wave (and network) formation. Key challenges will be separation of subtle changes in collective behaviour in different mutants, while extracting the main robust features.
Due to Covid-19 lockdowns, dissemination occurred in Year 1 mainly through seminar talks, presentation at the Quantitative Biology of C. elegans Behavior meeting in Amsterdam and lecturing on the MSc in Applied Biology and Biotechnology. The results have been also disseminated through social media, in particular using twitter account of the fellow and the project webpage.
Since Fisher’s 1930’s paper on the geometric model of adaptation, the evolving relationship between genotype and phenotype remains an open question in evolutionary biology. This lack of understanding is largely due to difficulties in analysing a large number of sequenced genetic variants of an animal. These technical problems are now lifted in C. elegans nematodes due to the establishment of libraries of genetic variants and high-throughput robotic imaging. In combination with state-of-the-art image analysis and modelling, this project is timely, and would provide completely new insights into the emergence of social behaviour. Besides allowing the answering of fundamental biological questions, the results of this project will be of value to a broad range of academic and industry players. C. elegans is also a model for human diseases such as Alzheimer’s disease, juvenile Parkinson’s disease, spinal muscular atrophy, hereditary non-polyposis colon cancer, as well as aging. Further significance comes from the fact that nematodes are a severe plant pest, destroying 12% crop every year worldwide. New approaches to nematode motion statistics analysis, quantitative phenotyping and modelling will be of great value for various academic and industry peers. In summary, this multidisciplinary project would provide significant amounts of training in modern quantitative biology, including data analysis and modelling, and in combination with complementary training, will be an ideal stepping stone to an independent scientific career. So far a major output for the community has been a new method for measuring the C. elegans spatial foraging and food intake using bioluminescent bacteria. These results allow to quantify feeding in both the asocial and hypersocial strains and generate insights into the spatial pattern of food consumption, which will be of great interest to the scientific community working on collective animal behaviour. Since parasitic nematodes are known to cause huge economic losses, we envision that the obtained results will also be useful for the wide agricultural community.