Biology subjects

Chitsaz, D.

Publications and source records attributed to Chitsaz, D..

5 recordsLinked to original sources

bioRxiv2026

Remodeling oligodendrocyte lipid metabolism via liver X receptors overcomes inflammatory blockade of remyelination

Multiple sclerosis is characterized by immune-mediated demyelination and inefficient remyelination, owing to impaired differentiation of oligodendrocyte precursor cells (OPCs) into myelinating oligodendrocytes (OLs). Inflammatory cytokines within multiple sclerosis lesions inhibit OPC maturation and induce an immune-like phenotype with antigen-presenting properties, but the underlying mechanisms remain poorly defined. Here, we show that inflammation reprograms OPC lipid metabolism, linking altered metabolism to remyelination failure. In cultured rodent OPCs, interferon-{gamma} (IFN-{gamma}) induced a switch from lipid synthesis to utilization, leading to reduced intracellular fatty acid levels and increased dependence on fatty acid oxidation. Transcriptional analyses confirmed similar lipid metabolic changes in OL-lineage cells cultured from human surgical specimens or isolated from mouse models of inflammatory demyelination and human multiple sclerosis lesions. Enhancing lipid availability in OPCs through oleic acid supplementation or inhibition of fatty acid oxidation attenuated immune-like functions and increased differentiation. Pharmacologic activation of liver X receptor (LXR) transcription factors rebalanced lipid metabolism, suppressed immune-like functions, and overcame IFN-{gamma}-induced differentiation blockade in both mouse and human-derived OPCs. In an adoptive transfer-cuprizone mouse model in which inflammation directly impairs remyelination, LXR activation increased mature OL generation and augmented myelin repair. Together, these findings identify lipid metabolic remodeling as a key mechanism by which inflammation impairs OPC differentiation and highlight LXR activation as a therapeutic approach to enhance remyelination in multiple sclerosis.

neuroscience↗

bioRxiv2025

Dendritic Polyglycerol Amine Substrate Extends the Viability of Mixed Glial Cultures for Repeated Isolation of Immature Oligodendrocyte Lineage Cells

Primary mixed glial cultures are key tools to isolate and study astrocytes, microglia and oligodendrocytes. Cell-substrate adhesion is critical for neural cell survival and differentiation. Cationic polymers like poly-D-lysine (PDL) are widely used to promote cell adhesion to cell culture substrates, however, PDL is not stable long-term, with cultured cells often detaching (peeling) after 2-3 weeks. Dendritic polyglycerol amine (dPGA) is a synthetic polycationic non-protein polymer biomimetic of poly-lysine that is highly resistant to degradation by cellular proteases. Substrates coated with dPGA promote cell adhesion and improve survival in long-term neuronal cultures. Here we assessed dPGA as a substrate coating to provide long-term support for mixed glial cultures. Oligodendrocyte precursor cells (OPCs) were isolated weekly by differential adhesion from cultures grown in T75 flasks with PDL or dPGA-coated substrates. Following two "shake-off" isolations, the cell layer in most PDL-coated flasks fully detached, rendering these flasks unusable for further culture. In contrast, dPGA-coated flasks consistently yielded cells for six or more sequential isolations over seven weeks in culture. dPGA-coated flasks produced more cells, a greater percentage of O4+ cells, and maintained similar proportions of OPCs and MBP-positive cells as when isolated from a PDL-coated substrate. dPGA is cyto-compatible, functionally superior, easy to use, low cost and a stable alternative to conventional cell substrate coatings. The enhanced long-term stability of mixed glial cultures grown on a dPGA substrate has the capacity to increase cellular yield, reduce animal use, and facilitate studies of oligodendrocyte cell biology.

neuroscience↗

bioRxiv2019

AANAT1 functions in astrocytes to regulate sleep homeostasis.

Characteristic features of sleep are conserved among species [1], and from humans to insects sleep is influenced by neural circuits involving monoamines such as serotonin and dopamine [2]. Glial cells have been increasingly implicated in mechanisms of baseline and homeostatic sleep regulation in mammals and flies [3-11], but it remains unknown whether and how glia might influence monoaminergic control of sleep. Sleep is regulated by circadian rhythms and a homeostatic drive to compensate for prolonged wakefulness, and growing evidence suggests that neural mechanisms controlling homeostatic sleep can be discriminated from those controlling baseline sleep [12-15]. In Drosophila, mutants of arylalkylamine N-acetyltransferase 1 (AANAT1lo) have normal baseline amounts of sleep and motor activity, but increased rebound sleep following deprivation [16]. AANAT1 can acetylate and inactivate monoamines in vitro [17], but the role of AANAT1 in vivo remains poorly understood. We find AANAT1 to be expressed in astrocytes and subsets of neurons in the adult Drosophila brain, with levels in astrocytes declining markedly overnight. In sleep-deprived AANAT1 mutant flies, heightened rebound sleep is accompanied by increased serotonin and dopamine levels in the brain. In neurons, AANAT1 functions to limit the quantity and consolidation of nighttime sleep, but in astrocytes AANAT1 constrains the amount of rebound sleep that flies take in response to sleep deprivation. These findings distinguish sleep-control functions of AANAT1 in neurons and astrocytes, and identify a critical role for astrocytes in the regulation of monoamine bioavailability and calibration of the response to sleep need.\n\nHighlightsO_LIThe monoamine catabolic enzyme arylalkylamine N-acetyltransferase 1 (AANAT1) is expressed by astrocytes and subsets of serotonergic, glutamatergic, GABAergic and cholinergic neurons in the adult brain of Drosophila.\nC_LIO_LIAANAT1 limits accumulation of serotonin and dopamine in the brain upon sleep deprivation.\nC_LIO_LILoss of AANAT1 from astrocytes, but not from neurons, causes flies to increase their daytime rebound sleep in response to overnight sleep deprivation.\nC_LI

neuroscience↗

bioRxiv2018

Spatial memory formation requires netrin-1 expression by neurons in the adult mammalian brain.

Netrin-1 was initially characterized as an axon guidance molecule that is essential for normal embryonic neural development; however, many types of neurons continue to express netrin-1 in the post-natal and adult mammalian brain. Netrin-1 and the netrin receptor DCC are both enriched at synapses. In the adult hippocampus, activity-dependent secretion of netrin-1 by neurons potentiates glutamatergic synapse function, and is critical for long-term potentiation, an experimental cellular model of learning and memory. Here, we assessed the impact of neuronal expression of netrin-1 in the adult brain on behavior using tests of learning and memory. We show that adult mice exhibit impaired spatial memory following conditional deletion of netrin-1 from glutamatergic neurons in the hippocampus and neocortex. Further, we provide evidence that mice with conditional deletion of netrin-1 do not display aberrant anxiety-like phenotypes and show a reduction in self-grooming behaviour. These findings reveal a critical role for netrin-1 expressed by neurons in the regulation of spatial memory formation.

neuroscience↗

bioRxiv2018

Deep learning for high-throughput quantification of oligodendrocyte ensheathment: a UNet architecture to extract multiple morphological parameters from individual cells

AO_SCPLOWBSTRACTC_SCPLOWHigh-throughput quantification of oligodendrocyte (OL) myelination is a significant challenge that, if addressed, would facilitate the development of therapeutics to promote myelin protection and repair. Here, we established a quantitative high-throughput method to asses OL ensheathment in-vitro, combining nanofiber culture devices and automated imaging with a heuristic approach that informed the development of a deep learning analytic algorithm. The heuristic approach was developed by modeling general characteristics of OL ensheathments, while the deep learning neural network employed a UNet architecture with enhanced capacity to associate ensheathed segments with individual OLs. Reliably extracting multiple morphological parameters from individual cells, without heuristic approximations, mimics the high-level decision-making capacity of human researchers and improves the validity of the neural network. Experimental validation demonstrated that the deep learning approach matched the accuracy of expert-human measurements of the length and number of myelin segments per cell. The combined use of automated imaging and analysis reduces tedious manual labor while eliminating variability. The capacity of this technology to perform multi-parametric analyses at the level of individual cells permits the detection of nuanced cellular differences to accelerate the discovery of new insight into OL physiology.

bioinformatics↗