Biology subjects

Zhu, V.

Publications and source records attributed to Zhu, V..

2 recordsLinked to original sources

bioRxiv2024

Translational control of microglial inflammatory and neurodegenerative responses

In Alzheimers Disease (AD), activation of the mechanistic target of rapamycin (mTOR) pathway is essential for microglia neuroprotective roles, but it is unclear which mTOR effectors promote these neuroprotective functions. The mTOR complex 1 (mTORC1) inactivates the translation suppressors eukaryotic translation Initiation Factor 4E (eIF4E)-Binding Proteins (4E-BP) to promote mRNA translation. We show that 4E-BP1 inactivation is impaired in microglia under AD-relevant conditions. Depleting 4E-BPs in microglia increases mitochondrial metabolism, suppresses the pro-inflammatory profile, and mitigates amyloid-induced apoptosis. Furthermore, in the cerebrospinal fluid of patients with amyloid pathology, there was a positive association between microglia activation and neurodegeneration, which increases along 4E-BP1 levels. Thus, we propose the engagement mTORC1-4E-BP1 axis as a neuroprotective mechanism and a therapeutic target or biomarker for microglia modulation in AD.

neuroscience↗

bioRxiv2019

Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance

Balanced excitation and inhibition is widely observed in cortical recordings. How does this balance shape neural computations and stimulus representations? This problem is often studied using computational models of neuronal networks in a dynamically balanced state. However, these balanced network models predict a linear relationship between stimuli and population responses, in contrast to the nonlinearity of cortical computations. We show that every balanced network architecture admits some stimuli that break the balanced state and these breaks in balance push the network into a "semi-balanced state" characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state is unavoidable in networks driven by multiple stimuli, consistent with experimental data, has a direct mathematical relationship to artificial neural networks, and permits nonlinear stimulus representations and nonlinear computations.

neuroscience↗