Dataset Information

Simulation of melanoblast displacements reveals new features of developmental migration.

ABSTRACT: To distribute and establish the melanocyte lineage throughout the skin and other developing organs, melanoblasts undergo several rounds of proliferation, accompanied by migration through complex environments and differentiation. Melanoblast migration requires interaction with extracellular matrix of the epidermal basement membrane and with surrounding keratinocytes in the developing skin. Migration has been characterized by measuring speed, trajectory and directionality of movement, but there are many unanswered questions about what motivates and defines melanoblast migration. Here, we have established a general mathematical model to simulate the movement of melanoblasts in the epidermis based on biological data, assumptions and hypotheses. Comparisons between experimental data and computer simulations reinforce some biological assumptions, and suggest new ideas for how melanoblasts and keratinocytes might influence each other during development. For example, it appears that melanoblasts instruct each other to allow a homogeneous distribution in the tissue and that keratinocytes may attract melanoblasts until one is stably attached to them. Our model reveals new features of how melanoblasts move and, in particular, suggest that melanoblasts leave a repulsive trail behind them as they move through the skin.

SUBMITTER: Laurent-Gengoux P

PROVIDER: S-EPMC6031402 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Simulation of melanoblast displacements reveals new features of developmental migration.

Laurent-Gengoux Pascal P Petit Valérie V Aktary Zackie Z Gallagher Stuart S Tweedy Luke L Machesky Laura L Larue Lionel L

Development (Cambridge, England) 20180611 12

To distribute and establish the melanocyte lineage throughout the skin and other developing organs, melanoblasts undergo several rounds of proliferation, accompanied by migration through complex environments and differentiation. Melanoblast migration requires interaction with extracellular matrix of the epidermal basement membrane and with surrounding keratinocytes in the developing skin. Migration has been characterized by measuring speed, trajectory and directionality of movement, but there ar ...[more]

PMID: 29769218

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Project description:Activation of several subtypes of glutamate receptors contributes to changes in postsynaptic calcium concentration at hippocampal synapses, resulting in various types of changes in synaptic strength. Thus, while activation of NMDA receptors has been shown to be critical for long-term potentiation (LTP) and long term depression (LTD) of synaptic transmission, activation of metabotropic glutamate receptors (mGluRs) has been linked to either LTP or LTD. While it is generally admitted that dynamic changes in postsynaptic calcium concentration represent the critical elements to determine the direction and amplitude of the changes in synaptic strength, it has been difficult to quantitatively estimate the relative contribution of the different types of glutamate receptors to these changes under different experimental conditions. Here we present a detailed model of a postsynaptic glutamatergic synapse that incorporates ionotropic and mGluR type I receptors, and we use this model to determine the role of the different receptors to the dynamics of postsynaptic calcium with different patterns of presynaptic activation. Our modeling framework includes glutamate vesicular release and diffusion in the cleft and a glutamate transporter that modulates extracellular glutamate concentration. Our results indicate that the contribution of mGluRs to changes in postsynaptic calcium concentration is minimal under basal stimulation conditions and becomes apparent only at high frequency of stimulation. Furthermore, the location of mGluRs in the postsynaptic membrane is also a critical factor, as activation of distant receptors contributes significantly less to calcium dynamics than more centrally located ones. These results confirm the important role of glutamate transporters and of the localization of mGluRs in postsynaptic sites in their signaling properties, and further strengthen the notion that mGluR activation significantly contributes to postsynaptic calcium dynamics only following high-frequency stimulation. They also provide a new tool to analyze the interactions between metabotropic and ionotropic glutamate receptors.

Project description:Purpose: We hypothesized that embryonic melanocytic pathways would be reinitiated in melanoma metatastasis. To identify nove melanoblast signaling pathways with which to explore our hypothesis we used the inducible Dct-GFP mouse model, in which GFP is expressed in both immature melanoblasts and mature melanocytes. Mouse melanoblasts/melanoytes were extracted using Fluorescence Activated Cell Sorting and transcriptomes were analyzed using RNA-sequencing. Abstract: Cutaneous malignant melanoma is an aggressive cancer of melanocytes with a strong propensity to metastasize. We posit that melanoma cells acquire metastatic capability by adopting an embryonic-like phenotype, and that a lineage approach would uncover novel metastatic melanoma biology. Using a genetically engineered mouse model to generate a rich melanoblast transcriptome dataset, we identify melanoblast-specific genes whose expression contribute to metastatic competence and derive a 43-gene signature that predicts patient survival. We identify a melanoblast gene, KDELR3, whose loss impairs experimental metastasis. In contrast, KDELR1 deficiency enhances metastasis, providing the first example of different disease etiologies within the KDELR-family of retrograde transporters. We show that KDELR3 regulates the metastasis suppressor, KAI1, and report an interaction with the E3 ubiquitin-protein ligase gp78, a regulator of KAI1 degradation. Our work demonstrates that the melanoblast transcriptome can be mined to uncover targetable pathways for melanoma therapy. Method: FVB/N iDct-GFP mice were used and doxycycline was administered to activate GFP expression. Multiple litters were used for each developmental stage, and embryos/pups from each stage were pooled to ensure adequate numbers of GFP+ cells. At E17.5, P1 and P7 stages, most melanocytes have reached the dermis, thus only the skin was collected from these developmental stages. Back skin was immersed in a shallow layer of 1X PBS and subcutaneous fat was scraped off until skin appeared translucent. E15.5 was the youngest age assessed due to the necessity to capture sufficient cells for RNA-sequencing. Embryos of the same developmental age that were heterozygous for the TRE-H2B-GFP gene but lacked the Dct-rtTA gene were used as negative controls. Cell doublets were excluded from the analysis. Cells were sorted based on GFP expression and SSC-A. Based on these reference sorts, gates were set so that background cells represented less than 10% of sorted cells. Cells were lysed in 10-fold TRIzol reagent (w/v), phases were separated by addition of 0.2X volume of chloroform, the aqueous phase was combined with an equal volume of 70% ethanol and applied to a RNeasy Micro column (Qiagen) and processed as per the manufacturer’s instructions. Paired-end sequencing libraries were prepared using 1 μg of purified RNA following the mRNA-Seq Sample Prep Kit according to the manufacturer’s instructions (Illumina). RNA-Seq libraries were sequenced on two lanes each of an Illumina GAIIx Genome Analyser to a minimum depth of 49 million reads. Sequence reads were aligned to the mm9 genome using the TopHat software (https://ccb.jhu.edu/software/tophat/index.shtml). Quantified Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values were generated using the Cufflinks software (http://cole-trapnell-lab.github.io/cufflinks/). The UCSC KnownGenes gene models were used for guided alignment and quantification. Results: DESeq2 analysis identified melanoblast-specific gene expression, which was shown by further analyses using patient data and wet lab techniques to likewise be upregulated in metastatic melanoma and facilitate metastasis.

Dataset Information

Simulation of melanoblast displacements reveals new features of developmental migration.

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Simulation of melanoblast displacements reveals new features of developmental migration.

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