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Simpson, M.

Publications and source records attributed to Simpson, M..

24 records · Page 2Linked to original sources

KLF6 activates complementary gene modules relevant to axon growth and promotes corticospinal tract regeneration after spinal injury

Members of the KLF family of transcription factors can exert both positive and negative effects on axon regeneration in the central nervous system, but the underlying mechanisms are unclear. KLF6 and -7 share nearly identical DNA binding domains and stand out as the only known growth-promoting family members. Here we confirm that similar to KLF7, expression of KLF6 declines during postnatal cortical development and that forced re-expression of KLF6 in corticospinal tract neurons of adult female mice enhances axon regeneration after cervical spinal injury. Unlike KLF7, however, these effects were achieved with wildtype KLF6, as opposed constitutively active mutants, thus simplifying the interpretation of mechanistic studies. To clarify the molecular basis of growth promotion, RNA sequencing identified 454 genes whose expression changed upon forced KLF6 expression in cortical neurons. Network analysis of these genes revealed sub-networks of downregulated genes that were highly enriched for synaptic functions, and sub-networks of upregulated genes with functions relevant to axon extension including cytoskeleton remodeling, lipid synthesis and transport, and bioenergetics. The promoter regions of KLF6-sensitive genes showed enrichment for the binding sequence of STAT3, a previously identified regeneration-associated gene. Notably, co-expression of constitutively active STAT3 along with KLF6 in cortical neurons produced synergistic increases in neurite length. Finally, genome-wide ATAC-seq footprinting detected frequent co-binding by the two factors in pro-growth gene networks, indicating co-occupancy as an underlying mechanism for the observed synergy. These findings advance understanding of KLF-stimulated axon growth and indicate functional synergy of KLF6 transcriptional effects with those of STAT3.\n\nSIGNIFICANCE STATEMENTThe failure of axon regeneration in the CNS limits recovery from damage and disease. These findings show the transcription factor KLF6 to be a potent promoter of axon growth after spinal injury, and more importantly clarify the underlying transcriptional changes. In addition, bioinformatics analysis predicted a functional interaction between KLF6 and a second transcription factor, STAT3, and genome-wide footprinting confirmed frequent co-occupancy. Co-expression of the two factors yielded synergistic elevation of neurite growth in primary neurons. These data point the way toward novel transcriptional interventions to promote CNS regeneration.

neuroscience↗

Three-dimensional experiments and individual based simulations show that cell proliferation drives melanoma nest formation in human skin tissue

BackgroundMelanoma can be diagnosed by identifying nests of cells on the skin surface. Understanding the processes that drive nest formation is important as these processes could be potential targets for new cancer drugs. Cell proliferation and cell migration are two potential mechanisms that could conceivably drive melanoma nest formation. However, it is unclear which one of these two putative mechanisms plays a dominant role in driving nest formation.\n\nResultsWe use a suite of three-dimensional (3D) experiments in human skin tissue and a parallel series of 3D individual-based simulations to explore whether cell migration or cell proliferation plays a dominant role in nest formation. In the experiments we measure nest formation in populations of irradiated (non-proliferative) and non-irradiated (proliferative) melanoma cells, cultured together with primary keratinocyte and fibroblast cells on a 3D experimental human skin model. Results show that nest size depends on initial cell number and is driven primarily by cell proliferation rather than cell migration.\n\nConclusionsWe find that nest size depends on initial cell number, and is driven primarily by cell proliferation rather than cell migration. All experimental results are consistent with simulation data from a 3D individual based model (IBM) of cell migration and cell proliferation.

cell biology↗

Mathematical models for cell migration with real-time cell cycle dynamics

Fluorescent ubiquitination-based cell cycle indicator, also known as FUCCI, allows the visualisation of the G1 and S/G2/M cell cycle phases of individual cells. FUCCI consists of two fluorescent probes, so that cells in the G1 phase fluoresce red and cells in the S/G2/M phase fluoresce green. FUCCI reveals real-time information about cell cycle dynamics of individual cells, and can be used to explore how the cell cycle relates to the location of individual cells, local cell density, and different cellular microenvironments. In particular, FUCCI is used in experimental studies examining cell migration, such as malignant invasion and wound healing. Here we present new mathematical models which can describe cell migration and cell cycle dynamics as indicated by FUCCI. The fundamental model describes the two cell cycle phases, G1 and S/G2/M, which FUCCI directly labels. The extended model includes a third phase, early S, which FUCCI indirectly labels. We present experimental data from scratch assays using FUCCI-transduced melanoma cells, and show that the predictions of spatial and temporal patterns of cell density in the experiments can be described by the fundamental model. We obtain numerical solutions of both the fundamental and extended models, which can take the form of travelling waves. These solutions are mathematically interesting because they are a combination of moving wavefronts and moving pulses. We derive and confirm a simple analytical expression for the minimum wave speed, as well as exploring how the wave speed depends on the spatial decay rate of the initial condition.

cell biology↗

Extended logistic growth model for heterogeneous populations

Cell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability as heterogeneity. Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.

cell biology↗

Discrete and continuum approximations for collective cell migration in a scratch assay with cell size dynamics

Scratch assays are routinely used to study the collective spreading of cell populations. In general, the rate at which a population of cells spreads is driven by the combined effects of cell migration and proliferation. To examine the effects of cell migration separately from the effects of cell proliferation, scratch assays are often performed after treating the cells with a drug that inhibits proliferation. Mitomycin-C is a drug that is commonly used to suppress cell proliferation in this context. However, in addition to suppressing cell proliferation, Mitomycin-C also causes cells to change size during the experiment, as each cell in the population approximately doubles in size as a result of treatment. Therefore, to describe a scratch assay that incorporates the effects of cell-to-cell crowding, cell-to-cell adhesion, and dynamic changes in cell size, we present a new stochastic model that incorporates these mechanisms. Our agent-based stochastic model takes the form of a system of Langevin equations that is the system of stochastic differential equations governing the evolution of the population of agents. We incorporate a time-dependent interaction force that is used to mimic the dynamic increase in size of the agents. To provide a mathematical description of the average behaviour of the stochastic model we present continuum limit descriptions using both a standard mean-field approximation, and a more sophisticated moment dynamics approximation that accounts for the density of agents and density of pairs of agents in the stochastic model. Comparing the accuracy of the two continuum descriptions for a typical scratch assay geometry shows that the incorporation of agent growth in the system is associated with a decrease in accuracy of the standard mean-field description. In contrast, the moment dynamics description provides a more accurate prediction of the evolution of the scratch assay when the increase in size of individual agents is included in the model.

biophysics↗

HIVs Feedback Circuit Breaks The Fundamental Limit On Noise Suppression To Stabilize Fate

Diverse biological systems utilize gene-expression fluctuations ( noise) to drive lineage-commitment decisions1-5. However, once a commitment is made, noise becomes detrimental to reliable function6,7 and the mechanisms enabling post-commitment noise suppression are unclear. We used time-lapse imaging and mathematical modeling, and found that, after a noise-driven event, human immunodeficiency virus (HIV) strongly attenuated expression noise through a non-transcriptional negative-feedback circuit. Feedback is established by serial generation of RNAs from post-transcriptional splicing, creating a precursor-product relationship where proteins generated from spliced mRNAs auto-deplete their own precursor un-spliced mRNAs. Strikingly, precursor auto-depletion overcomes the theoretical limits on conventional noise suppression--minimizing noise far better than transcriptional auto-repression--and dramatically stabilizes commitment to the active-replication state. This auto-depletion feedback motif may efficiently suppress noise in other systems ranging from detained introns to non-sense mediated decay.

cell biology↗