Background: Early-onset familial Alzheimer disease (EOFAD) is caused by heterozygous variants in the presenilin 1 (PSEN1), presenilin 2 (PSEN2), and APP genes. Decades after their discovery, the mechanisms by which these genes cause Alzheimer’s disease (AD) or promote AD progression are not fully understood. While it is established that presenilin (PS) enzymatic activity produces amyloid-β (Aβ), PSs also regulate numerous other cellular functions, some of which intersect with known pathogenic drivers of neurodegeneration. Accumulating evidence suggests that microglia, resident innate immune cells in the central nervous system, play a key role in AD neurodegeneration. Objective: Previous work has identified a regulatory role for PS2 in microglia. We hypothesized that PSEN2 variants lead to dysregulated microglia, which could further contribute to disease acceleration. To mimic the genotype of EOFAD patients, we created a transgenic mouse expressing PSEN2 N141I on a mouse background expressing one wildtype PS2 and two PS1 alleles. Results: Microglial expression of PSEN2 N141I resulted in impaired γ-secretase activity as well as exaggerated inflammatory cytokine release, NFκB activity, and Aβ internalization. In vivo, PS2 N141I mice showed enhanced IL-6 and TREM2 expression in brain as well as reduced branch number and length, an indication of “activated” morphology, in the absence of inflammatory stimuli. LPS intraperitoneal injection resulted in higher inflammatory gene expression in PS2 N141I mouse brain relative to controls. Conclusion: Our findings demonstrate that PSEN2 N141I heterozygosity is associated with disrupted innate immune homeostasis, suggesting EOFAD variants may promote disease progression through non-neuronal cells beyond canonical dysregulated Aβ production.
Individual neurons in the brain have complex intrinsic dynamics that are highly diverse. We hypothesize that the complex dynamics produced by networks of complex and heterogeneous neurons may contribute to the brain's ability to process and respond to temporally complex data. To study the role of complex and heterogeneous neuronal dynamics in network computation, we develop a rate-based neuronal model, the generalized-leaky-integrate-and-firing-rate (GLIFR) model, which is a rate-equivalent of the generalized-leaky-integrate-and-fire model. The GLIFR model has multiple dynamical mechanisms which add to the complexity of its activity while maintaining differentiability. We focus on the role of after-spike currents, currents induced or modulated by neuronal spikes, in producing rich temporal dynamics. We use machine learning techniques to learn both synaptic weights and parameters underlying intrinsic dynamics to solve temporal tasks. The GLIFR model allows us to use standard gradient descent techniques rather than surrogate gradient descent, which has been utilized in spiking neural networks. After establishing the ability to optimize parameters using gradient descent in single neurons, we ask how networks of GLIFR neurons learn and perform on temporally challenging tasks, such as sinusoidal pattern generation and sequential MNIST. We find that these networks learn a diversity of parameters, which gives rise to diversity in neuronal dynamics. We also observe that training networks on the sequential MNIST task leads to formation of cell classes based on the clustering of neuronal parameters. GLIFR networks have mixed performance when compared to vanilla recurrent neural networks but appear to be more robust to random silencing. When we explore these performance gains further, we find that both the ability to learn heterogeneity and the presence of after-spike currents contribute. Our work both demonstrates the computational robustness of neuronal complexity and diversity in networks and demonstrates a feasible method of training such models using exact gradients.
Microglia maintain brain health and play important roles in disease and injury. Despite the known ability of microglia to proliferate, the precise nature of the population or populations capable of generating new microglia in the adult brain remains controversial. We identified Prominin-1 (Prom1; also known as CD133) as a putative cell surface marker of committed brain myeloid progenitor cells. We demonstrate that Prom1-expressing cells isolated from mixed cortical cultures will generate new microglia in vitro. To determine whether Prom1-expressing cells generate new microglia in vivo, we used tamoxifen inducible fate mapping in male and female mice. Induction of Cre recombinase activity at 10 weeks in Prom1expressing cells leads to the expression of TdTomato in all Prom1-expressing progenitors and newly generated daughter cells. We observed a population of new TdTomato-expressing microglia at 6 months of age that increased in size at 9 months. When microglia proliferation was induced using a transient ischemia/reperfusion paradigm, little proliferation from the Prom1-expressing progenitors was observed with the majority of new microglia derived from Prom1-negative cells. Together, these findings reveal that Prom1-expressing myeloid progenitor cells contribute to the generation of new microglia both in vitro and in vivo. Furthermore, these findings demonstrate the existence of an undifferentiated myeloid progenitor population in the adult mouse brain that expresses Prom1. We conclude that Prom1-expressing myeloid progenitors contribute to new microglia genesis in the uninjured brain but not in response to ischemia/reperfusion.
Individual neurons in the brain have complex intrinsic dynamics that are highly diverse. We hypothesize that the complex dynamics produced by networks of complex and heterogeneous neurons may contribute to the brain's ability to process and respond to temporally complex data. To study the role of complex and heterogeneous neuronal dynamics in network computation, we develop a rate-based neuronal model, the generalized-leaky-integrate-and-fire-rate (GLIFR) model, which is a rate equivalent of the generalized-leaky-integrate-and-fire model. The GLIFR model has multiple dynamical mechanisms, which add to the complexity of its activity while maintaining differentiability. We focus on the role of after-spike currents, currents induced or modulated by neuronal spikes, in producing rich temporal dynamics. We use machine learning techniques to learn both synaptic weights and parameters underlying intrinsic dynamics to solve temporal tasks. The GLIFR model allows the use of standard gradient descent techniques rather than surrogate gradient descent, which has been used in spiking neural networks. After establishing the ability to optimize parameters using gradient descent in single neurons, we ask how networks of GLIFR neurons learn and perform on temporally challenging tasks, such as sequential MNIST. We find that these networks learn diverse parameters, which gives rise to diversity in neuronal dynamics, as demonstrated by clustering of neuronal parameters. GLIFR networks have mixed performance when compared to vanilla recurrent neural networks, with higher performance in pixel-by-pixel MINST but lower in line-by-line MNIST. However, they appear to be more robust to random silencing. We find that the ability to learn heterogeneity and the presence of after-spike currents contribute to these gains in performance. Our work demonstrates both the computational robustness of neuronal complexity and diversity in networks and a feasible method of training such models using exact gradients.
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