Biology subjects

Campbell, S. A.

Publications and source records attributed to Campbell, S. A..

3 recordsLinked to original sources

bioRxiv2020

H3K4 trimethylation is required for postnatal pancreatic endocrine cell functional maturation

During pancreas development, endocrine progenitors differentiate into the islet-cell subtypes, which undergo further functional maturation in postnatal islet development. In islet {beta}-cells, genes involved in glucose-stimulated insulin secretion are activated and glucose exposure increases the insulin response as {beta}-cells mature. Here, we investigated the role of H3K4 trimethylation in endocrine cell differentiation and functional maturation by disrupting TrxG complex histone methyltransferase activity in mouse endocrine progenitors. In the embryo, genetic inactivation of TrxG component Dpy30 in NEUROG3+ cells did not affect the number of endocrine progenitors or endocrine cell differentiation. H3K4 trimethylation was progressively lost in postnatal islets and the mice displayed elevated random and fasting glycemia, as well as impaired glucose tolerance by postnatal day 24. Although postnatal endocrine cell proportions were equivalent to controls, islet RNA-sequencing revealed a downregulation of genes involved in glucose-stimulated insulin secretion and an upregulation of immature {beta}-cell genes. Comparison of histone modification enrichment profiles in NEUROG3+ endocrine progenitors and mature islets suggested that genes downregulated by loss of H3K4 trimethylation more frequently acquire active histone modifications during maturation. Taken together, these findings suggest that H3K4 trimethylation is required for the activation of genes involved in the functional maturation of pancreatic islet endocrine cells.

developmental biology↗

bioRxiv2020

Effect of time delay on the synchronization of excitatory-inhibitory neural networks

We study a model for a network of synaptically coupled, excitable neurons to identify the role of coupling delays in generating different network behaviors. The network consists of two distinct populations, each of which contains one excitatory-inhibitory neuron pair. The two pairs are coupled via delayed synaptic coupling between the excitatory neurons, while each inhibitory neuron is connected only to the corresponding excitatory neuron in the same population. We show that multiple equilibria can exist depending on the strength of the excitatory coupling between the populations. We conduct linear stability analysis of the equilibria and derive necessary conditions for delay-induced Hopf bifurcation. We show that these can induce two qualitatively different phase-locked behaviors, with the type of behavior determined by the sizes of the coupling delays. Numerical bifurcation analysis and simulations supplement and confirm our analytical results. Our work shows that the resting equilibrium point is unaffected by the coupling, thus the network exhibits bistability between a rest state and an oscillatory state. This may help understand how rhythms spontaneously arise neuronal networks.

neuroscience↗

bioRxiv2020

Spatially localized cluster solutions in inhibitory neural networks

Neurons in the inhibitory network of the striatum display cell assembly firing patterns which recent results suggest may consist of spatially compact neural clusters. Previous computational modeling of striatal neural networks has indicated that non-monotonic, distance-dependent coupling may promote spatially localized cluster firing. Here, we identify conditions for the existence and stability of cluster firing solutions in which clusters consist of spatially adjacent neurons in inhibitory neural networks. We consider simple non-monotonic, distance-dependent connectivity schemes in weakly coupled 1-D networks where cells make strong connections with their kth nearest neighbors on each side. Using the phase model reduction of the network system, we prove the existence of cluster solutions where neurons that are spatially close together are also synchronized in the same cluster, and find stability conditions for these solutions. Our analysis predicts the long-term behavior for networks of neurons, and we confirm our results by numerical simulations of biophysical neuron network models. Additionally, we add weaker coupling between closer neighbors as a perturbation to our network connectivity. We analyze the existence and stability of cluster solutions of the perturbed network and validate our results with numerical simulations. Our results demonstrate that an inhibitory network with non-monotonic, distance-dependent connectivity can exhibit cluster solutions where adjacent cells fire together.

neuroscience↗