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The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

Qualitative Dynamical Modelling Can Formally Explain Mesoderm Specification and Predict Novel Developmental Phenotypes

Mbodj, Abibatou, Gustafson, E. Hilary, Ciglar, Lucia, Junion, Guillaume, Gonzalez, Aitor, Girardot, Charles, Perrin, Laurent, Furlong, Eileen E. M., Thieffry, Denis·2016·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1005073PMC PMC5012701
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Abstract

Given the complexity of developmental networks, it is often difficult to predict the effect of genetic perturbations, even within coding genes. Regulatory factors generally have pleiotropic effects, exhibit partially redundant roles, and regulate highly interconnected pathways with ample cross-talk. Here, we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development, thereby providing a formal integration of all available genetic information from the literature. The four main tissues derived from mesoderm correspond to alternative stable states. We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new, often non-intuitive, loss- and gain-of-function mutations, and combinations thereof. We further validated several novel predictions experimentally, thereby demonstrating the robustness of model. Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions.

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Figures

Fig 1 Early stages of drosophila mesoderm specification. A-C: Schematic description of the establishment of mesoderm anterior-posterior and dorsal-ventral patterning. At stage 8, the presumptive mesoderm is largely homogeneous. At stage 9, ectodermal signals outline a characteristic pattern, with stripes of Hh, Eve and En alternating with stripes of Wg and Slp, which delimit anterior/posterior segmental borders, respectively. Dpp signalling further delimits dorsal versus ventral mesoderm domains. Mesoderm specification is achieved at stage 10, when Wg/Slp domains give rise to heart precursors (H, in red, dorsally located) and somatic muscles (SM, orange, ventrally located), whereas En/Eve/Hh domains give rise to visceral mesoderm (VM, blue, dorsal) and fat body (FB, green, ventral). D: Schematic representation of the four main tissues originating from the mesoderm in each segment, with key associated markers (e.g. Srp expression for FB).
Fig 1 Early stages of drosophila mesoderm specification. A-C: Schematic description of the establishment of mesoderm anterior-posterior and dorsal-ventral patterning. At stage 8, the presumptive mesoderm is largely homogeneous. At stage 9, ectodermal signals outline a characteristic pattern, with stripes of Hh, Eve and En alternating with stripes of Wg and Slp, which delimit anterior/posterior segmental borders, respectively. Dpp signalling further delimits dorsal versus ventral mesoderm domains. Mesoderm specification is achieved at stage 10, when Wg/Slp domains give rise to heart precursors (H, in red, dorsally located) and somatic muscles (SM, orange, ventrally located), whereas En/Eve/Hh domains give rise to visceral mesoderm (VM, blue, dorsal) and fat body (FB, green, ventral). D: Schematic representation of the four main tissues originating from the mesoderm in each segment, with key associated markers (e.g. Srp expression for FB).CC BY · PubMed Central
Fig 2 Key signalling pathways and markers genes involved in mesoderm specification. A, B: In situ hybridizations for Tin and Bin during mesoderm specification at stages 8 and 9–10. Tin is implicated in the formation of VM and H, while Bin participates only in the development of VM. Initially, the expression of Tin is mainly due to Twist activation. Later, Tin expression needs the presence of Dpp, Tin itself, in combination with Pan. C: Graphical representations of the main pathways activated by signals coming from the ectoderm, encompassing target transcription factors and cross-regulations underlying the specification of VM, H, FB and SM. In the absence of these factors, these tissues do not form or are severely reduced. Black and light grey arcs denote active and inactive regulations, depending on stage or tissue. Normal and blunt end arrows denote activations and inhibitions, respectively.
Fig 2 Key signalling pathways and markers genes involved in mesoderm specification. A, B: In situ hybridizations for Tin and Bin during mesoderm specification at stages 8 and 9–10. Tin is implicated in the formation of VM and H, while Bin participates only in the development of VM. Initially, the expression of Tin is mainly due to Twist activation. Later, Tin expression needs the presence of Dpp, Tin itself, in combination with Pan. C: Graphical representations of the main pathways activated by signals coming from the ectoderm, encompassing target transcription factors and cross-regulations underlying the specification of VM, H, FB and SM. In the absence of these factors, these tissues do not form or are severely reduced. Black and light grey arcs denote active and inactive regulations, depending on stage or tissue. Normal and blunt end arrows denote activations and inhibitions, respectively.CC BY · PubMed Central
Fig 3 Regulatory graph for the signalling/transcriptional network controlling drosophila mesoderm specification. Built with the software GINsim, this regulatory graph encompasses the main regulatory factors and interactions involved in mesoderm specification (stages 8–10), as documented by published (molecular) genetic and functional genomic data. Ellipses denote Boolean nodes, whereas rectangles denote multilevel nodes. Light green filling denotes input nodes, most corresponding to factors expressed in and acting from the ectoderm. Yellow filling denotes output factors, mostly effector genes and tissue markers. Blue or grey filling denotes internal nodes expressed in the mesoderm. Green arrows and red blunt arrows denote activations and inhibitions, respectively. Logical rules are further associated with each node to define its behaviour depending on regulatory inputs (cf. S1 Table). To ease the dynamical analysis of this regulatory graph, we performed a reduction of this regulatory graph (cf. Material and methods), making implicit the twelve grey components. This logical model is provided as supporting data, including comprehensive annotations and bibliographical references.
Fig 3 Regulatory graph for the signalling/transcriptional network controlling drosophila mesoderm specification. Built with the software GINsim, this regulatory graph encompasses the main regulatory factors and interactions involved in mesoderm specification (stages 8–10), as documented by published (molecular) genetic and functional genomic data. Ellipses denote Boolean nodes, whereas rectangles denote multilevel nodes. Light green filling denotes input nodes, most corresponding to factors expressed in and acting from the ectoderm. Yellow filling denotes output factors, mostly effector genes and tissue markers. Blue or grey filling denotes internal nodes expressed in the mesoderm. Green arrows and red blunt arrows denote activations and inhibitions, respectively. Logical rules are further associated with each node to define its behaviour depending on regulatory inputs (cf. S1 Table). To ease the dynamical analysis of this regulatory graph, we performed a reduction of this regulatory graph (cf. Material and methods), making implicit the twelve grey components. This logical model is provided as supporting data, including comprehensive annotations and bibliographical references.CC BY · PubMed Central
Fig 4 Simulations of known genetic perturbations. The results of selected simulations of loss-of-function (lof), gain-of-function (gof) mutations, and of combination thereof are shown in the form of coloured square vignettes, along with references to articles presenting matching data. The first vignette (top left) correspond to the wild type situation, with VM, H, FB and SM presumptive territories coloured in blue, red, green and orange, respectively. In the following vignettes, the coloration of the four presumptive territories are modified to reflect the absence or important markers, or the combination of markers associated with different tissues. Wg lof leads to the loss of Wg/Slp domain, resulting in an expansion of the En/Hh domain; consequently, the model correctly predicts the loss of H along with a potential perturbation of SM (yellow domains). Dpp lof leads to the loss of dorsal derivatives (VM and H), along with an expansion of FB. Dpp gof leads to an expansion of VM at the expense of FB, along with a perturbation of SM. Tin lof shows a loss of dorsal tissues, while Bap lof exhibits only the loss of VM. Finally, the combination of Wg gof and Hh lof leads to a dorsal expansion of H, along with a loss of FB, while the combination of Dpp gof, Hh gof and Wg lof leads to an expansion of VM in the whole mesoderm.
Fig 4 Simulations of known genetic perturbations. The results of selected simulations of loss-of-function (lof), gain-of-function (gof) mutations, and of combination thereof are shown in the form of coloured square vignettes, along with references to articles presenting matching data. The first vignette (top left) correspond to the wild type situation, with VM, H, FB and SM presumptive territories coloured in blue, red, green and orange, respectively. In the following vignettes, the coloration of the four presumptive territories are modified to reflect the absence or important markers, or the combination of markers associated with different tissues. Wg lof leads to the loss of Wg/Slp domain, resulting in an expansion of the En/Hh domain; consequently, the model correctly predicts the loss of H along with a potential perturbation of SM (yellow domains). Dpp lof leads to the loss of dorsal derivatives (VM and H), along with an expansion of FB. Dpp gof leads to an expansion of VM at the expense of FB, along with a perturbation of SM. Tin lof shows a loss of dorsal tissues, while Bap lof exhibits only the loss of VM. Finally, the combination of Wg gof and Hh lof leads to a dorsal expansion of H, along with a loss of FB, while the combination of Dpp gof, Hh gof and Wg lof leads to an expansion of VM in the whole mesoderm.CC BY · PubMed Central
Fig 5 Systematic simulations of double mutants. This matrix displays the results of systematic perturbations. Loss- and gain-of-function mutations (rows and column) were simulated iteratively using a set of Python scripts, along with pairwise combinations (cf. Material and methods). The results of the simulations of single mutants are displayed on the diagonal of the matrix. The predicted phenotype for each double mutant is shown at the intersection of the corresponding column and row. Note that the cells corresponding to the crossing of a lof and a gof for the same gene are left empty. Simulation results are graphically depicted using vignettes as in Fig 4, with specific colours denoting situations with miss-expressed genes (cf. colour key top right). This presentation eases the comparison of the results of multiple mutant simulations and enables the identification of dominant or synergic effects. This matrix encompasses numerous predictions, along with a few dozens of documented phenotypes. The web version of the matrix (S2 File) further provides access to detailed information regarding the predicted patterns of expression for each mutant in each region. We have selected six perturbations (four single and two double ones, surrounded by tick squares in the matrix) for experimental validation (see Fig 6).
Fig 5 Systematic simulations of double mutants. This matrix displays the results of systematic perturbations. Loss- and gain-of-function mutations (rows and column) were simulated iteratively using a set of Python scripts, along with pairwise combinations (cf. Material and methods). The results of the simulations of single mutants are displayed on the diagonal of the matrix. The predicted phenotype for each double mutant is shown at the intersection of the corresponding column and row. Note that the cells corresponding to the crossing of a lof and a gof for the same gene are left empty. Simulation results are graphically depicted using vignettes as in Fig 4, with specific colours denoting situations with miss-expressed genes (cf. colour key top right). This presentation eases the comparison of the results of multiple mutant simulations and enables the identification of dominant or synergic effects. This matrix encompasses numerous predictions, along with a few dozens of documented phenotypes. The web version of the matrix (S2 File) further provides access to detailed information regarding the predicted patterns of expression for each mutant in each region. We have selected six perturbations (four single and two double ones, surrounded by tick squares in the matrix) for experimental validation (see Fig 6).CC BY · PubMed Central
Fig 6 In situ RNA staining for two sets of single and double mutants. A: Slp lof and Med lof each results in a perturbation of H. The double mutant displays an even stronger disruption of H, along with a clear expansion of Srp expression. These experimental results are largely consistent with our model predictions and further provide interpretational clues regarding mixed expression patterns. B: Slp gof exhibits a loss of VM, while FB appears perturbed in both Doc gof and Slp gof mutants. The combination of these perturbations leads to stronger losses of FB and VM, while H and SM are barely affected. These results qualitatively agree with model predictions.
Fig 6 In situ RNA staining for two sets of single and double mutants. A: Slp lof and Med lof each results in a perturbation of H. The double mutant displays an even stronger disruption of H, along with a clear expansion of Srp expression. These experimental results are largely consistent with our model predictions and further provide interpretational clues regarding mixed expression patterns. B: Slp gof exhibits a loss of VM, while FB appears perturbed in both Doc gof and Slp gof mutants. The combination of these perturbations leads to stronger losses of FB and VM, while H and SM are barely affected. These results qualitatively agree with model predictions.CC BY · PubMed Central

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Metadata

Authors
Mbodj, A., Gustafson, E. H., Ciglar, L., Junion, G., Gonzalez, A., Girardot, C., Perrin, L., Furlong, E. E. M., & Thieffry, D.
ORCID
Mbodj, Abibatou
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2016-09-06
Volume
12
Issue
9
Pages
e1005073
ISSN
1553-7358
DOI
10.1371/journal.pcbi.1005073
PMC
PMC5012701
Cited by
12
References
54
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • DFG (FU 750/1-2)
  • EraSysBio (ModHeart)

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