FOXG1 dose tunes cell proliferation dynamics in human forebrain progenitor cells.

Heterozygous loss-of-function mutations in Forkhead box G1 (FOXG1), a uniquely brain-expressed gene, cause microcephaly, seizures, and severe intellectual disability, whereas increased FOXG1 expression is frequently observed in glioblastoma. To investigate the role of FOXG1 in forebrain cell proliferation, we modeled FOXG1 syndrome using cells from three clinically diagnosed cases with two sex-matched healthy parents and one unrelated sex-matched control. Cells with heterozygous FOXG1 loss showed significant reduction in cell proliferation, increased ratio of cells in G0/G1 stage of the cell cycle, and increased frequency of primary cilia. Engineered loss of FOXG1 recapitulated this effect, while isogenic repair of a patient mutation reverted output markers to wild type. An engineered inducible FOXG1 cell line derived from a FOXG1 syndrome case demonstrated that FOXG1 dose-dependently affects all cell proliferation outputs measured. These findings provide strong support for the critical importance of FOXG1 levels in controlling human brain cell growth in health and disease.

A modified Ising model of Barabási-Albert network with gene-type spins.

The central question of systems biology is to understand how individual components of a biological system such as genes or proteins cooperate in emerging phenotypes resulting in the evolution of diseases. As living cells are open systems in quasi-steady state type equilibrium in continuous exchange with their environment, computational techniques that have been successfully applied in statistical thermodynamics to describe phase transitions may provide new insights to the emerging behavior of biological systems. Here we systematically evaluate the translation of computational techniques from solid-state physics to network models that closely resemble biological networks and develop specific translational rules to tackle problems unique to living systems. We focus on logic models exhibiting only two states in each network node. Motivated by the apparent asymmetry between biological states where an entity exhibits boolean states i.e. is active or inactive, we present an adaptation of symmetric Ising model towards an asymmetric one fitting to living systems here referred to as the modified Ising model with gene-type spins. We analyze phase transitions by Monte Carlo simulations and propose a mean-field solution of a modified Ising model of a network type that closely resembles a real-world network, the Barabási-Albert model of scale-free networks. We show that asymmetric Ising models show similarities to symmetric Ising models with the external field and undergoes a discontinuous phase transition of the first-order and exhibits hysteresis. The simulation setup presented herein can be directly used for any biological network connectivity dataset and is also applicable for other networks that exhibit similar states of activity. The method proposed here is a general statistical method to deal with non-linear large scale models arising in the context of biological systems and is scalable to any network size.

Stimulation of L-type calcium channels increases tyrosine hydroxylase and dopamine in ventral midbrain cells induced from somatic cells.

Making high-quality dopamine (DA)-producing cells for basic biological or small molecule screening studies is critical for the development of novel therapeutics for disorders of the ventral midbrain. Currently, many ventral midbrain assays have low signal-to-noise ratio due to low levels of cellular DA and the rate-limiting enzyme of DA synthesis, tyrosine hydroxylase (TH), hampering discovery efforts. Using intensively characterized ventral midbrain cells derived from human skin, which demonstrate calcium pacemaking activity and classical electrophysiological properties, we show that an L-type calcium agonist can significantly increase TH protein levels and DA content and release. Live calcium imaging suggests that it is the immediate influx of calcium occurring simultaneously in all cells that drives this effect. Genome-wide expression profiling suggests that L-type calcium channel stimulation has a significant effect on specific genes related to DA synthesis and affects expression of L-type calcium receptor subunits from the CACNA1 and CACNA2D families. Together, our findings provide an advance in the ability to increase DA and TH levels to improve the accuracy of disease modeling and small molecule screening for disorders of the ventral midbrain, including Parkinson's disease.

Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons.

Spiking neuronal networks are usually simulated with one of three main schemes: the classical time-driven and event-driven schemes, and the more recent hybrid scheme. All three schemes evolve the state of a neuron through a series of checkpoints: equally spaced in the first scheme and determined neuron-wise by spike events in the latter two. The time-driven and the hybrid scheme determine whether the membrane potential of a neuron crosses a threshold at the end of the time interval between consecutive checkpoints. Threshold crossing can, however, occur within the interval even if this test is negative. Spikes can therefore be missed. The present work offers an alternative geometric point of view on neuronal dynamics, and derives, implements, and benchmarks a method for perfect retrospective spike detection. This method can be applied to neuron models with affine or linear subthreshold dynamics. The idea behind the method is to propagate the threshold with a time-inverted dynamics, testing whether the threshold crosses the neuron state to be evolved, rather than vice versa. Algebraically this translates into a set of inequalities necessary and sufficient for threshold crossing. This test is slower than the imperfect one, but can be optimized in several ways. Comparison confirms earlier results that the imperfect tests rarely miss spikes (less than a fraction 1/108 of missed spikes) in biologically relevant settings.