Tracking nutrient decisions in Drosophila melanogaster

Animals integrate external sensory information and current metabolic needs to adapt their behavior in order to survive. Accordingly, many organisms can detect an internal nutritional imbalance and adjust their nutritional choices to restore homeostasis. Detailed quantitative analyses of nutrient-choice behaviors are needed to deepen our understanding of how neural circuits integrate internal state information and drive compensatory behavior when facing metabolic challenges. During this project, we developed an automated video tracking setup to characterize how metabolic and reproductive states interact to shape exploitation and exploration decisions taken by the adult fruit fly Drosophila melanogaster, to achieve nutritional homeostasis. We find that these two states have specific effects on the decisions to stop on and leave proteinaceous food patches. Furthermore, the internal nutrient state defines the exploration-exploitation trade-off: nutrient deprived flies focus on specific patches while satiated flies explore more globally. We provide few examples of how our paradigm could be used in the dissection of the genetic and neuronal pathways underlying nutrient decisions: First, we show that olfaction is not required for the compensatory high yeast feeding after amino acid deprivation, but that it mediates the efficient recognition of yeast as an appropriate food source in mated females. Second, we show that octopamine is required to mediate homeostatic postmating responses without affecting internal nutrient sensing. Third, we show how gustation is required to sustain interest for protein-rich resources upon amino acid deprivation. Our results provide a quantitative description of how the fly changes behavioral decisions to achieve homeostatic nutrient balancing and provide a framework for future detailed mechanistic dissection of such decisions

A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology

An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment

Functional Validation of Candidate Genes Detected by Genomic Feature Models

Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach

The role of food odor in invertebrate foraging

Foraging for food is an integral part of animal survival. In small insects and inverte-brates, multisensory information and optimized locomotion strategies are used toeffectively forage in patchy and complex environments. Here, the importance ofolfactory cues for effective invertebrate foraging is discussed in detail. We reviewhow odors are used by foragers to move toward a likely food source and the recentmodels that describe this sensory-driven behavior. We argue that smell serves a sec-ond function by priming an organism for the efficient exploitation of food. Byappraising food odors, invertebrates can establish preferences and better adapt totheir ecological niches, thereby promoting survival. The smell of food pre-preparesthe gastrointestinal system and primes feeding motor programs for more effectiveingestion as well. Optimizing resource utilization affects longevity and reproductionas a result, leading to drastic changes in survival. We propose that models of foragingbehavior should include odor priming, and illustrate this with a simple toy modelbased on the marginal value theorem. Lastly, we discuss the novel techniques andassays in invertebrate research that could investigate the interactions between odorsensing and food intake. Overall, the sense of smell is indispensable for efficient for-aging and influences not only locomotion, but also organismal physiology, whichshould be reflected in behavioral modeling

On the Convexity of Latent Social Network Inference

In many real-world scenarios, it is nearly impossible to collect explicit social network data. In such cases, whole networks must be inferred from underlying observations. Here, we formulate the problem of inferring latent social networks based on network diffusion or disease propagation data. We consider contagions propagating over the edges of an unobserved social network, where we only observe the times when nodes became infected, but not who infected them. Given such node infection times, we then identify the optimal network that best explains the observed data. We present a maximum likelihood approach based on convex programming with a l1-like penalty term that encourages sparsity. Experiments on real and synthetic data reveal that our method near-perfectly recovers the underlying network structure as well as the parameters of the contagion propagation model. Moreover, our approach scales well as it can infer optimal networks of thousands of nodes in a matter of minutes.Comment: NIPS, 201

Unsupervised behavioral classification with 3D pose data from tethered Drosophila melanogaster

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), Universidade de Lisboa, Faculdade de Ciências, 2020O comportamento animal e guiado por instruções geneticamente codificadas, com contribuições do meio envolvente e experiências antecedentes. O mesmo pode ser considerado como o derradeiro output da atividade neuronal, pelo que o estudo do comportamento animal constitui um meio de compreensão dos mecanismos subjacentes ao funcionamento do cérebro animal. Para desvendar a correspondência entre cérebro e comportamento são necessárias ferramentas que consigam medir um comportamento de forma precisa, apreciável e coerente. O domínio científico responsável pelo estudo dos comportamentos dos animais denomina-se Etologia. No início do seculo XX, os etólogos categorizavam comportamentos animais com recurso as suas próprias intuições e experiência. Consequentemente, as suas avaliações eram subjetivas e desprovidas de comportamentos que os etólogos não considerassem a priori. Com o ressurgimento de novas técnicas de captura e analise de comportamentos, os etólogos transitaram para paradigmas mais objetivos, quantitativos da medição de comportamentos. Tais ferramentas analíticas fomentaram a construção de datasets comportamentais que, por sua vez, promoveram o desenvolvimento de softwares para a quantificação de comportamentos: rastreamento de trajetórias, classificação de ações, analise de padrões comportamentais em grandes escalas consistem nos exemplos mais preeminentes. Este trabalho encontra-se inserido na segunda categoria referida (classificação de ações). Os classificadores de ações dividem-se consoante são supervisionados ou não-supervisionados. A primeira categoria compreende classificadores treinados para reconhecer padrões específicos, definidos por um especialista humano. Esta categoria de classificadores e encontra-se limitada por: 1) necessitar de um processo extenuado de anotação de frames para treino do classificador; 2) subjetividade face ao especialista que classifica os mesmos frames, 3) baixa dimensionalidade, na medida em que a classificação reduz os complexos comportamentos a um só rotulo; 4) assunções erróneas; 5) preconceito humano face aos comportamentos observados. Por sua vez, os classificadores não-supervisionados seguem exaustivamente uma formula: 1) computer vision e empregue para a extração das características posturais do animal; 2) dá-se o pré-processamento dos dados, que inclui um modulo vital que envolve a construção de uma representação dinâmico-postural das ações do animal, de forma a capturar os elementos dinâmicos do comportamento; 3) segue-se um modulo opcional de redução de dimensionalidade, caso o utilizador deseje visualizar diretamente os dados num espaço de reduzidas dimensões; 4) efetua-se a atribuição de um rótulo a cada elemento dos dados, por via de um algoritmo que opera quer diretamente no espaço de alta dimensão, ou no de baixa dimensão, resultante do passo anterior. O objetivo deste trabalho passa por alcançar uma classificação objetiva e reproduzível, de forma não-supervisionada de frames de Drosophila melanogaster suspensas numa bola que flutua no ar, tentando minimizar o número de intuições requeridas para o efeito e, se possível, dissipar a influência dos aspetos morfológicos de cada individuo (garantindo assim uma classificação generalizada dos comportamentos destes insetos). Para alcançar tal classificação, este estudo recorre a uma ferramenta recém desenvolvida que regista a pose tridimensional de Drosophila fixas, o DeepFly3D, para construir um dataset com as coordenadas x-, y- e z-, ao longo do tempo, das posições de referência de um conjunto de três genótipos de Drosophila melanogaster (linhas aDN>CsChrimson, MDN-GAL4/+ e aDNGAL4/+). Sucede-se uma operação inovadora de normalização que recorre ao cálculo de ângulos entre pontos de referência adjacentes, como as articulações, antenas e riscas dorsais das moscas, por via de relações trigonométricas e a definição dos planos anatómicos das moscas, que visa atenuar os pesos das diferenças morfológicas das moscas, ou a sua orientação relativa as camaras do DeepFly3D, para o classificador. O modulo de normalização e sucedido por outro de analise de frequência, focado na extração das frequências relevantes nas series temporais dos ângulos calculados, bem como dos seus pesos relativos. O produto final do pré-processamento consiste numa matriz com a norma dos ditos pesos – a matriz de expressão do espaço dinâmico-postural. Subsequentemente, seguem-se os módulos de redução de dimensionalidade e de atribuição de clusters (pontos 3) e 4) do paragrafo anterior). Para os mesmos, são propostas seis configurações possíveis de algoritmos, submetidas de imediato a uma anélise comparativa, de forma a determinar a mais apta para classificar este tipo de dados. Os algoritmos de redução de dimensionalidade aqui postos a prova são o t-SNE (t-distributed Stochastic Neighbor Embedding) e o PCA (Principal Component Analysis), enquanto que os algoritmos de clustering comparados são o Watershed, GMM-posterior probability assignment e o HDBSCAN (Hierarchical Density Based Spatial Clustering of Applications with Noise). Cada uma das pipelines candidatas e finalmente avaliada mediante a observação dos vídeos inclusos nos clusters produzidos e, dado o vasto numero destes vídeos, bem como a possibilidade de uma validação subjetiva face a observadores distintos, com o auxilio de métricas que expressam determinados critérios abrangentes de qualidade dos clusters: 1) Fly uncompactness, que avalia a eficiência do modulo de normalização com ângulos de referencia da mosca; 2) Homogeneity, que procura garantir que os clusters não refletem a identidade ou o genótipo das moscas; 3) Cluster entropy, que afere a previsibilidade das transições entre os clusters; 4) Mean dwell time, que pondera o tempo que um individuo demora em media a realizar uma Acão. Dois critérios auxiliares extra são ainda considerados: o número de parâmetros que foram estimados pelo utilizador (quanto maior, mais limitada e a reprodutibilidade da pipeline) e o tempo de execução do algoritmo (que deve ser igualmente minimizado). Apesar de manter alguma subjetividade face aquilo a que o utilizador considera um “bom” cluster, a inclusão das métricas aproxima esta abordagem a um cenário ideal de completa autonomia entre a conceção de uma definição de comportamento, e a validação dos resultados que decorrem das suas conjeturas. Os desempenhos das pipelines candidatas divergiram largamente: os espaços resultantes das operações de redução de dimensionalidade demonstram-se heterogéneos e anisotrópicos, com a presença de sequências de pontos que tomam formas vermiformes, ao invés de um antecipado conglomerado de pontos desassociados. Estas trajetórias vermiformes limitam o desempenho dos algoritmos de clustering que operam nos espaços de baixas (duas, neste caso) dimensões. A ausência de um passo intermedio de amostragem do espaço dinâmico-postural explica a génese destas trajetórias vermiformes. Não obstante, as pipelines que praticam redução de dimensionalidade geraram melhores resultados que a pipeline que recorre a clustering com HDBSCAN diretamente sobre a matriz de expressão do espaço dinâmico-postural. A combinação mais fortuita de módulos de redução de dimensionalidade e clustering adveio da pipeline PCA30-t-SNE2-GMM. Embora não sejam absolutamente consistentes, os clusters resultantes desta pipeline incluem um comportamento que se sobressai face aos demais que se encontram inseridos no mesmo cluster (erroneamente). Lacunas destes clusters envolvem sobretudo a ocasional fusão de dois comportamentos distintos no mesmo cluster, ou a presença inoportuna de sequências de comportamentos nas quais a mosca se encontra imóvel (provavelmente o resultado de pequenos erros de deteção produzidos pelo DeepFly3D). Para mais, a pipeline PCA30-t-SNE2-GMM foi capaz de reconhecer diferenças no fenótipo comportamental de moscas, validadas pelas linhas genéticas das mesmas. Apesar dos resultados obtidos manifestarem visíveis melhorias face aqueles produzidos por abordagens semelhantes, sobretudo a nível de vídeos dos clusters, uma vez que só uma das abordagens inclui métricas de sucesso dos clusters, alguns aspetos desta abordagem requerem correções: a inclusão de uma etapa de amostragem, sucedida de um novo algoritmo que fosse capaz de realizar reduções de dimensionalidade consistentes, de forma a reunir todos os pontos no mesmo espaço embutido será possivelmente a característica mais capaz de acrescentar valor a esta abordagem. Futuras abordagens não deverão descurar o contributo de múltiplas representações comportamentais que possam vir a validar-se mutuamente, substituindo a necessidade de métricas de sucesso definidas pelos utilizadores.One of the preeminent challenges of Behavioral Neuroscience is the understanding of how the brain works and how it ultimately commands an animal’s behavior. Solving this brain-behavior linkage requires, on one end, precise, meaningful and coherent techniques for measuring behavior. Rapid technical developments in tools for collecting and analyzing behavioral data, paired with the immaturity of current approaches, motivate an ongoing search for systematic, unbiased behavioral classification techniques. To accomplish such a classification, this study employs a state-of-the-art tool for tracking 3D pose of tethered Drosophila, DeepFly3D, to collect a dataset of x-, y- and z- landmark positions over time, from tethered Drosophila melanogaster moving over an air-suspended ball. This is succeeded by unprecedented normalization across individual flies by computing the angles between adjoining landmarks, followed by standard wavelet analysis. Subsequently, six unsupervised behavior classification techniques are compared - four of which follow proven formulas, while the remaining two are experimental. Lastly, their performances are evaluated via meaningful metric scores along with cluster video assessment, as to ensure a fully unbiased cycle - from the conjecturing of a definition of behavior to the corroboration of the results that stem from its assumptions. Performances from different techniques varied significantly. Techniques that perform clustering in embedded low- (two-) dimensional spaces struggled with their heterogeneous and anisotropic nature. High-dimensional clustering techniques revealed that these properties emerged from the original highdimensional posture-dynamics spaces. Nonetheless, high and low-dimensional spaces disagree on the arrangement of their elements, with embedded data points showing hierarchical organization, which was lacking prior to their embedding. Low-dimensional clustering techniques were globally a better match against these spatial features and yielded more suitable results. Their candidate embedding algorithms alone were capable of revealing dissimilarities in preferred behaviors among contrasting genotypes of Drosophila. Lastly, the top-ranking classification technique produced satisfactory behavioral cluster videos (despite the irregular allocation of rest labels) in a consistent and repeatable manner, while requiring a marginal number of hand tuned parameters

Modulation of sleep and activity in Drosophila: a systems biology approach. Genetics, pharmacology and high-throughput analysis of behaviour

Drosophila melanogaster is a widely used model organism which for the past 20 years has been employed in a variety of contexts to understand aspects of sleep, activity and more complex forms of behaviour. A challenge within the field of behaviour is how to accurately classify and quantify behaviours that arise from an organism when these behaviours are observed in different contexts. Technological advances have increased the availability of quantitative tools which can be used to examine activity and sleep behaviour. With the use of these tools, we can now answer new questions about the underlying mechanisms of behaviour in different conditions. In the thesis herein, I have examined activity and sleep behaviour in two different contexts, utilising some of these new technological tools, including a novel activity monitoring device and statistical classification techniques. In the first part of this thesis, I use this activity monitoring system to elucidate some of the mechanisms involved in homeostatic sleep behaviour. Specifically, I examine the effect of two different sleep deprivation methods on mated and virgin Drosophila females to examine their responses in terms of homeostatic sleep regulation. Using the same methodology and protocol, I then extend this work to exam- ine the role of the neuropeptide, Corazonin, and its receptor, the Corazonin receptor, in these contexts. In the second part of this thesis, I use the same activity monitor- ing system to record the behavioural responses of flies exposed to different insecticide compounds. I then use both a statistical classification technique and behavioural anal- ysis to attempt to classify these compounds based on their mode of action (MoA) and symptomology. Finally, I apply this methodology to answer other biological questions of interest, classifying both rebound sleep and flies with varying genotypes.Open Acces

An automated system for quantitative analysis of Drosophila larval locomotion

Background: Drosophila larvae have been used as a model to study to genetic and cellular circuitries modulating behaviors. One of the challenges in behavioral study is the quantification of complex phenotypes such as locomotive behaviors. Experimental capability can be greatly enhanced by an automatic single-animal tracker that records an animal at a high resolution for an extended period, and analyzes multiple behavioral parameters. Results: Here we present MaggotTracker, a single-animal tracking system for Drosophila larval locomotion analysis. This system controls the motorized microscope stage while taking a video, so that the animal remains in the viewing center. It then reduces the animal to 13 evenly distributed points along the midline, and computes over 20 parameters evaluating the shape, peristalsis movement, stamina, and track of the animal. To demonstrate its utility, we applied MaggotTracker to analyze both wild-type and mutant animals to identify factors affecting locomotive behaviors. Each animal was tracked for four minutes. Our analysis on Canton-S third-instar larvae revealed that the distance an animal travelled was correlated to its striding speed rather than the percentage of time the animal spent striding, and that the striding speed was correlated to both the distance and the duration of one stride. Sexual dimorphism was observed in body length but not in locomotive parameters such as speed. Locomotive parameters were affected by animal developmental stage and the crawling surface. No significant changes in movement speed were detected in mutants of circadian genes such as period (per), timeout, and timeless (tim). The MaggotTracker analysis showed that ether a go-go (eag), Shaker (Sh), slowpoke (slo), and dunce (dnc) mutant larvae had severe phenotypes in multiple locomotive parameters such as stride distance and speed, consistent with their function in neuromuscular junctions. Further, the phenotypic patterns of the K+ channel genes eag, Sh and slo are highly similar. Conclusions: These results showed that MaggotTracker is an efficient tool for automatic phenotyping. The MaggotTracker software as well as the data presented here can be downloaded from our open-access site www.WormLoco.org/Ma

Investigating the Role of ATP-Binding Cassette Transporters in Drosophila melanogaster Testis Stem Cells

Multidrug resistance is among the most pressing obstacles in cancer treatment today. Resistance is thought to arise from the ability of cancer stem cells to efflux therapeutic molecules using a collection of membrane proteins called ATP-binding cassette (ABC) transporters. There is strong interest in targeting ABC transporters to preserve and improve drug efficacy and reduce cancer recurrence. Many studies have been performed in vitro using cultured cell lines, but currently there is a lack of simple models in which to study ABC transporters in vivo. As a solution, I propose to use the fruit fly Drosophila melanogaster for the study of ABC transporters, and specifically the D. melanogaster testis stem cell niche—one of the best-characterized adult stem cell niches. Stem cells have several traits in common with cancer cells, including the ability to divide indefinitely, the ability to give rise to many different kinds of daughter cells, and chemoresistance. In invertebrates there is mounting evidence for a role ABC transporters play in insecticide resistance, but to date there is no peer-reviewed evidence for invertebrate stem cell drug resistance in the literature. Here, I present evidence of cytotoxic drug efflux in the germline stem cells of the D. melanogaster testis. This was accomplished by feeding the chemotherapy drug doxorubicin to wild type flies for two days and measuring fluorescence levels using confocal microscopy. Using enhancer trap lines, I also present ABC transporter expression evidence in both the germline and cyst lineages of the testis. Finally, through RNAi knockdown of several ABC transporters, I present evidence of their contribution to germline stem cell drug efflux. I also report unexpected phenotypes in the male germline resulting from the knockdown of two ABC transporters, CG32901 and CG3164, which points to a role for these proteins in development of the normal testes niche