Quantitative system drift compensates for altered maternal inputs to the gap gene network of the scuttle fly Megaselia abdita

Wotton, Karl R.; Jiménez-Guri, Eva; Crombach, Anton; Janssens, Hilde; Alcaine-Colet, Anna; Lemke, Steffen; Schmidt‐Ott, Urs; Jaeger, Johannes

doi:10.7554/elife.04785

Cited by 77 publications

(183 citation statements)

References 138 publications

(252 reference statements)

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“…Interestingly, the spatio-temporal dynamics of the segmentation gene expression patterns are highly conserved between species, suggesting that the co-evolution of modular transcription binding sites compensate for each other to keep the patterning outcomes unchanged [53][54][55]. Such an inter-species canalization phenomenon is also observed among more distally related species within the sub-taxon Cyclorrhapha, which involved more dramatic rewiring of the regulatory network [56,57]. If the breaking point of patterning processes under decanalized conditions truly depends on the system parameters of the underlying network [58], we expect to see different susceptible points in different network structures.…”

Section: Discussionmentioning

confidence: 93%

Embryonic geometry underlies phenotypic variation in decanalized conditions

Huang

Saunders

2019

Preprint

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During development, many mutations cause increased variation in phenotypic outcomes, a phenomenon termed decanalization. Such variations can often be attributed to genetic and environmental perturbations. However, phenotypic discordance remains even in isogenic model organisms raised in homogeneous environments. To understand the mechanisms underlying phenotypic variation, we used as a model the highly precise anterior-posterior (AP) patterning of the early Drosophila embryo. We decanalized the system by depleting the maternal bcd product and found that in contrast to the highly scaled patterning in the wild-type, the segmentation gene boundaries shift away from the scaled positions according to the total embryonic length. Embryonic geometry is hence a key factor predetermining patterning outcomes in such decanalized conditions. Embryonic geometry was also found to predict individual patterning outcomes under bcd overexpression, another decanalizing condition. Further analysis of the gene regulatory network acting downstream of the morphogen identified vulnerable points in the networks due to limitations in the available physical space.

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Section: Discussionmentioning

confidence: 93%

Embryonic geometry underlies phenotypic variation in decanalized conditions

Huang

Saunders

2019

Preprint

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“…The differences in the co‐expression patterns of hairy , atonal , and their upstream regulators hedgehog and Delta/Notch may represent the early stages of developmental systems drift, where pathway components can evolve different relationships while maintaining the same phenotypic output (True & Haag, ). This type of “quantitative developmental systems drift” has been proposed to be a pervasive feature of evolving developmental systems (Crombach et al, ; Wotton et al, ). It may also be that the overall relationship between the genes has not evolved, but that variation between the network components is beneath some critical threshold that would results in differences in downstream regulation—that is that co‐expression patterns may show evolution and variation, but the basic inputs and outputs of the network are conserved.…”

Section: Discussionmentioning

confidence: 99%

Novel approach to quantitative spatial gene expression uncovers genetic stochasticity in the developing Drosophila eye

Ali

Signor

Kozlov

et al. 2019

Evolution and Development

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Robustness in development allows for the accumulation of genetically based variation in expression. However, this variation is usually examined in response to large perturbations, and examination of this variation has been limited to being spatial, or quantitative, but because of technical restrictions not both. Here we bridge these gaps by investigating replicated quantitative spatial gene expression using rigorous statistical models, in different genotypes, sexes, and species (Drosophila melanogaster and D. simulans). Using this type of quantitative approach with molecular developmental data allows for comparison among conditions, such as different genetic backgrounds. We apply this approach to the morphogenetic furrow, a wave of differentiation that patterns the developing eye disc. Within the morphogenetic furrow, we focus on four genes, hairy, atonal, hedgehog, and Delta. Hybridization chain reaction quantitatively measures spatial gene expression, co‐staining for all four genes simultaneously. We find considerable variation in the spatial expression pattern of these genes in the eye between species, genotypes, and sexes. We also find that there has been evolution of the regulatory relationship between these genes, and that their spatial interrelationships have evolved between species. This variation has no phenotypic effect, and could be buffered by network thresholds or compensation from other genes. Both of these mechanisms could potentially be contributing to long term developmental systems drift.

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“…In particular, the mutual inhibition between alternating domains (Hba, Kni, Hbp; Gta, Kr, Gtp) and the one-side inhibition between adjacent domains from posterior to anterior (Hbp to Gtp to Kni to Kr to Hba) are both present in our model and the "real" networks. This regulation structure makes much sense biologically, as it not only is the core motif for Drosophila gap-gene network (23), but also seems to play a pivotal role in long-germband insect evolution (23,(34)(35)(36). This core topology is captured by DNN, perhaps because it is implicated as the anterior shift of the abdomen domains in wild type gap-gene dynamics (37).…”

Section: Revealing the Underlying Regulation Networkmentioning

confidence: 99%

“…This core topology is captured by DNN, perhaps because it is implicated as the anterior shift of the abdomen domains in wild type gap-gene dynamics (37). Such shift is not an artifact or coincidence, but wide spread among different species (23,34,35).…”

Section: Revealing the Underlying Regulation Networkmentioning

confidence: 99%

Deciphering gene regulation from gene expression dynamics using deep neural network

Shen

Petkova²,

Tu³

et al. 2018

Preprint

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Complex biological functions are carried out by the interaction of genes and proteins.Uncovering the gene regulation network behind a function is one of the central themes in biology.Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation -the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN "black box". Our interpretable DNN approach should have broad applications in genotype-phenotype mapping. SignificanceComplex biological functions are carried out by gene regulation networks. The mapping between gene network and function is a central theme in biology. The task usually involves extensive experiments with perturbations to the system (e.g. gene deletion). Here, we demonstrate that machine learning, or deep neural network (DNN), can help reveal the underlying gene regulation for a given function or phenotype with minimal perturbation data. Specifically, after training with wild-type gene expression dynamics data and a few mutant snapshots, the DNN learns to behave like an accurate simulator for the genetic system, which can be used to predict other mutants' behaviors. Furthermore, our DNN approach is biochemically interpretable, which helps 3 uncover possible gene regulatory mechanisms underlying the observed phenotypic behaviors. IntroductionComplex biological functions are carried out by gene regulation networks. Uncovering the gene regulation network behind a function is one of the central themes in biology. Traditionally, this task usually involves extensive genetic and biochemical experiments with perturbations to the biological system. For example, in the classical gene knockout experiments, by observing the expression of gene a increasing (decreasing) when deleting gene b, one may infer that gene a is repressed (activated) by gene b. More recently, statistical and bioinformatical methods have been used to help mapping out genetic and protein interactions. These methods can be very powerful especially in analyzing high throughput experimental data and extracting information about correlations among the genes and proteins (1, 2). Another computational approach is...

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Quantitative system drift compensates for altered maternal inputs to the gap gene network of the scuttle fly Megaselia abdita

Cited by 77 publications

References 138 publications

Embryonic geometry underlies phenotypic variation in decanalized conditions

Embryonic geometry underlies phenotypic variation in decanalized conditions

Novel approach to quantitative spatial gene expression uncovers genetic stochasticity in the developing Drosophila eye

Deciphering gene regulation from gene expression dynamics using deep neural network

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