Biology subjects

Nabavi, S.

Publications and source records attributed to Nabavi, S..

15 recordsLinked to original sources

bioRxiv2025

Epigenetic inheritance of complex learning abilities in the mammalian brain

For several decades, the question of whether cognitive and learning capacities can be inherited through non-genetic mechanisms has been the subject of ongoing debate. Here, we provide the first evidence of transgenerational inheritance of enhanced ability to learn complex tasks in the mammalian rodent brain. These inherited learning enhancements are not limited to specific stimuli, sensory modalities, or learning paradigms. Using behavioral, cellular biophysical, methylomics, genetics and molecular methods, we find that the inherited epigenetic modifications reflect an enhanced neuronal learning state, driven by increased intrinsic neuronal excitability in most pyramidal neurons in the relevant neuronal networks. This enhancement is mediated by persistent downregulation of the muscarinic M-current and is associated with widespread changes in DNA methylation, notably within coding genes associated with the M-current, the Kv7 pathway, in the hippocampi of trained F0 rats, as well as in non-coding RNAs in their sperm samples. Remarkably, a significant portion of these DNA methylation changes were also observed in the hippocampi of their untrained F1 offspring. These findings suggest that complex learning abilities can be inherited in the mammalian brain, as the offspring of trained rodents are born with the biophysical modifications that enable them to become super-learners, the exact change that occurs in their parents brains only after the rule learning.

neuroscience↗

bioRxiv2024

Semaglutide Administration in Healthy Mice Alters Behaviour Related to Stress and Motivation

Semaglutide, a GLP-1 receptor agonist, is widely studied in metabolic disorders, yet its effects in healthy subjects remain unclear. Here, we show that daily administration of semaglutide (0.1 mg/kg) in healthy mice alters behaviour in various tests related to stress and reward pursuit. Additionally, acute administration of semaglutide changes the oscillatory activity in the nucleus accumbens in the delta, theta and alpha bands. These findings reveal that semaglutide impacts both behaviour and neural dynamics in non-diseased brains, offering a baseline for interpreting its broader therapeutic potential.

neuroscience↗

bioRxiv2024

DiDBiT-TMT: A novel method to quantify changes in the proteomic landscape induced by neural plasticity

Withdrawal StatementThe authors have withdrawn this manuscript because during the peer review process in the journal, it was discovered that one of the processed proteomic data groups (the chemical LTP group) was mistakenly processed from the raw data for the norepinephrine group, resulting in an apparent complete overlap between the two conditions. For a correct analysis, please refer to the currently published peer-reviewed publication through the following DOI: https://doi.org/10.1021/acs.jproteome.4c00180 Therefore, the authors do not wish this preprint to be cited as reference for the project. If you have any questions, please contact the corresponding author.

neuroscience↗

bioRxiv2023

Recombinase-Independent AAV for Anterograde Transsynaptic Tracing

Viral transsynaptic labeling has become indispensable for investigating the functional connectivity of neural circuits. Adeno-associated virus serotype 1 (AAV1) allows for anterograde transneuronal labeling and manipulation of postsynaptic neurons. However, it is limited to delivering an AAV1 expressing a recombinase which relies on using transgenic animals or genetic access to postsynaptic neurons. We reasoned that a strong expression level could overcome this limitation. To this end, we used a self-complementary AAV1 (scAAV1) under a strong promoter (CAG). We demonstrated the anterograde transneuronal efficiency of scAAV1 by delivering a fluorescent marker in mouse retina-superior colliculus and thalamic-amygdala pathways in a recombinase-independent manner. In addition to investigating neuronal connectivity, scAAV1.CAG may be suitable for functional manipulation and imaging.

neuroscience↗

bioRxiv2023

Non-Hebbian plasticity produces long-lasting associative memories

The dominant models of learning and memory, such as Hebbian plasticity, propose that experiences are transformed into memories through input-specific synaptic plasticity at the time of learning. However, synaptic plasticity is neither strictly input specific nor restricted to the time of its induction. The impact of such forms of non-Hebbian plasticity on memory has been difficult to test, hence poorly understood. Here, we demonstrate that synaptic manipulations can deviate from the Hebbian model of learning, yet produce a lasting memory. First, we established a weak associative conditioning protocol in mice, where optogenetic stimulation of sensory thalamic input to the amygdala was paired with a footshock, but no detectable memory was formed. However, when the same input was potentiated minutes before or after, or even 24 hours later, the associative experience was converted to a lasting memory. Importantly, potentiating an independent input to the amygdala minutes but not 24 hours after the pairing produced a lasting memory. Thus, our findings suggest that the process of transformation of a transient experience into a memory is neither restricted to the time of the experience nor to the synapses triggered by it; instead, it can be influenced by past and future events.

neuroscience↗

bioRxiv2023

Distinct representations of innate and learned threats within the thalamic-amygdala pathway

Behavioral flexibility and timely reactions to salient stimuli are essential for survival. The subcortical thalamic-basolateral amygdala (BLA) pathway serves as a shortcut for salient stimuli ensuring rapid processing. Here, we show that BLA neuronal and thalamic axonal activity mirror the defensive behavior evoked by an innate visual threat as well as an auditory learned threat. Importantly, perturbing this pathway compromises defensive responses to both forms of threats, in that animals fail to switch from exploratory to defensive behavior. Despite the shared pathway between the two forms of threat processing, we observed noticeable differences. Blocking beta-adrenergic receptors impair the defensive response to the innate but not the learned threats. This reduced defensive response, surprisingly, is reflected in the suppression of the activity exclusively in the BLA, as the thalamic input response remains intact. Our side-by-side examination highlights the similarities and differences between innate and learned threat-processing, thus providing new fundamental insights.

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bioRxiv2021

SorCS2 dynamically interacts with TrkB and GluN2B to control neurotransmission and Huntington's disease progression

BackgroundHuntingtons disease (HD) is a fatal neurodegenerative disorder characterized by progressive motor dysfunction and loss of medium spiny neurons (MSNs) in dorsal striatum. Brain-derived neurotrophic factor (BDNF) sustains functionality and integrity of MSNs, and thus reduced BDNF signaling is integral to the disease. Mutations in BDNF receptor SorCS2 were recently identified in HD patients. Our study investigates the role of SorCS2 in MSNs biology and in HD progression. MethodsWe derived a double transgenic line by crossbreeding SorCS2 deficient (KO) mice with the HD mouse model R6/1. Subsequently, we characterized the SorCS2 KO; R6/1 line by a set of behavioral and biochemical studies to evaluate phenotypes related to HD. Moreover, in combination with electrophysiology and super resolution microscopy techniques, we addressed the molecular mechanism by which SorCS2 controls synaptic activity in MSNs neurons. ResultsWe show that SorCS2 is expressed in MSNs with reduced levels in R6/1 HD model, and that SorCS2 deficiency exacerbates the disease progression in R6/1 mice. Furthermore, we find that SorCS2 binds TrkB and the NMDA receptor subunit GluN2B, which is required to control neurotransmission in corticostriatal synapses. While BDNF stimulates SorCS2-TrkB complex formation to enable TrkB signaling, it disengages SorCS2 from GluN2B, leading to enrichment of the subunit at postsynaptic densities. Consequently, long-term potentiation (LTP) is abolished in SorCS2 deficient mice, despite increased striatal TrkB and unaltered BDNF expression. However, the addition of exogenous BDNF rescues the phenotype. Finally, GluN2B, but not GluN2A, currents are also severely impaired in the SorCS2 KO mice. ConclusionsWe formulate a novel molecular mechanism by which SorCS2 acts as a molecular switch. SorCS2 targets TrkB and GluN2B into postsynaptic densities to enable BDNF signaling and NMDAR dependent neurotransmission in the dorsal striatum. Remarkably, the binding between SorCS2 and TrkB or GluN2B, respectively, is mutually exclusive and controlled by BDNF. This mechanism provides an explanation why deficient SorCS2 signaling severely aggravates HD progression in mice. Moreover, we provide evidence that this finding might represent a general mechanism of SorCS2 signaling found in other brain areas, thus increasing its relevance for other neurological and psychiatric impairments.

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bioRxiv2021

Enhanced Production of Mesencephalic Dopaminergic Neurons from Lineage-Restricted Human Undifferentiated Stem Cells

The differentiation of human pluripotent stem cells (hPSCs) into mesencephalic dopaminergic (mesDA) neurons requires a precise combination of extrinsic factors that recapitulates the in vivo environment and timing. Current methods are capable of generating authentic mesDA neurons after long-term culture in vitro; however, when mesDA progenitors are transplanted in vivo, the resulting mesDA neurons are only minor components of the graft. This low yield hampers the broad use of these cells in the clinic. In this study, we genetically modified pluripotent stem cells to generate a novel type of stem cells called lineage-restricted undifferentiated stem cells (LR-USCs), which robustly generate mesDA neurons. LR-USCs are prevented from differentiating into a broad range of nondopaminergic cell types by knocking out genes that are critical for the specification of cells of alternate lineages. Specifically, we target transcription factors involved in the production of spinal cord and posterior hindbrain cell types. When LR-USCs are differentiated under caudalizing condition, which normally give rise to hindbrain cell types, a large proportion adopt a midbrain identity and develop into authentic mesDA neurons. We show that the mesDA neurons are electrophysiologically active, and due to their higher purity, are capable of restoring motor behavior eight weeks after transplantation into 6-hydroxydopamine (6-OHDA)-lesioned rats. This novel strategy improves the reliability and scalability of mesDA neuron generation for clinical use.

neuroscience↗

bioRxiv2021

Single-Cell Classification Using Graph Convolutional Networks

BackgroundAnalyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures. ResultsIn this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods. ConclusionsResults indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.

bioinformatics↗

bioRxiv2021

Polo-like kinase 2 inhibition reduces serine-129 phosphorylation of physiological nuclear alpha-synuclein but not of the aggregated alpha-synuclein

Accumulation of aggregated alpha-synuclein (-syn) is believed to play a pivotal role in the pathophysiology of Parkinsons disease (PD) and other synucleinopathies. -Syn is a key constituent protein of Lewy pathology, and -syn phosphorylated at serine-129 (pS129) constitutes more than 90% of -syn in Lewy bodies and hence, it is used extensively as a pathological marker for the aggregated form of -syn. However, the exact role of pS129 remains controversial as well as the kinase(s) responsible for the phosphorylation. In this study, we investigated the effect of Polo-like kinase 2 (PLK2) inhibition on formation of pS129 using ex-vivo organotypic brain slice model of synucleinopathy. Our data demonstrated that PLK2 inhibition has no effect on -syn aggregation, pS129 or inter-neuronal spreading of the aggregated -syn seen in the organotypic slices. Instead, PLK2 inhibition reduced the soluble nuclear pS129 level confined in the nuclei. The same finding was replicated in an in-vivo mouse models of templated -syn aggregation and human dopaminergic neurons, suggesting that PLK2 is more likely to be involved in S129 phosphorylation of soluble non-pathology related fraction of -syn. We also demonstrated that reduction of nuclear pS129 but not the aggregates specific pS129 following PLK2 inhibition for a short time before sample collection improves the signal to noise ratio when quantifying pS129 aggregate pathology.

neuroscience↗

bioRxiv2021

Dual-color optical activation and suppression of neurons with high temporal precision

A well-known phenomenon in the optogenetic toolbox is that all light-gated ion channels, including red-shifted channelrhodopsins (ChRs), are activated by blue light, whereas blue-shifted ChRs are minimally responsive to longer wave-lengths. Here, we took advantage of this feature to create a system which allows high-frequency activation of neurons with pulses of red light, while permitting the suppression of action potentials with millisecond precision by blue light. We achieved this by pairing an ultrafast red-shifted ChR with a blue light-sensitive anion channel of appropriately matching kinetics. This required screening several anion-selective ChRs, followed by a model-based mutagenesis strategy to optimize their kinetics and light spectra. Slice electrophysiology in the hippocampus as well as behavioral inspection of vibrissa movement demonstrate a minimal excitation from blue light. Of significant potential value, in contrast to existing tools, the system we introduce here allows high frequency optogenetic excitation of neurons with red light, while blue light suppression of action potentials is confined within the duration of the light pulse.

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bioRxiv2020

Convolutional neural network for automated mass segmentation in mammography

BackgroundAutomatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features. We trained the proposed model using huge publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and a private database from the University of Connecticut Health Center (UCHC). ResultsWe compared the performance of the proposed model with those of the state-of-the-art DL models including the fully convolutional network (FCN), SegNet, Dilated-Net, original U-Net, and Faster R-CNN models and the conventional region growing (RG) method. The proposed Vanilla U-Net model outperforms the Faster R-CNN model significantly in terms of the runtime and the Intersection over Union metric (IOU). Training with digitized film-based and fully digitized MG images, the proposed Vanilla U-Net model achieves a mean test accuracy of 92.6%. The proposed model achieves a mean Dice coefficient index (DI) of 0.951 and a mean IOU of 0.909 that show how close the output segments are to the corresponding lesions in the ground truth maps. Data augmentation has been very effective in our experiments resulting in an increase in the mean DI and the mean IOU from 0.922 to 0.951 and 0.856 to 0.909, respectively. ConclusionsThe proposed Vanilla U-Net based model can be used for precise segmentation of masses in MG images. This is because the segmentation process incorporates more multi-scale spatial context, and captures more local and global context to predict a precise pixel-wise segmentation map of an input full MG image. These detected maps can help radiologists in differentiating benign and malignant lesions depend on the lesion shapes. We show that using transfer learning, introducing augmentation, and modifying the architecture of the original model results in better performance in terms of the mean accuracy, the mean DI, and the mean IOU in detecting mass lesion compared to the other DL and the conventional models.

bioinformatics↗

bioRxiv2020

TRACE: An unbiased method to permanently tag transiently activated inputs

A fundamental interest in circuit analysis is to parse out the synaptic inputs underlying a behavioral experience. Toward this aim, we have devised an unbiased strategy that specifically labels the afferent inputs that are activated by a defined stimulus in an activity-dependent manner. We validated this strategy in four brain areas receiving known sensory inputs. This strategy, as demonstrated here, accurately identifies these inputs.

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bioRxiv2019

Organotypic slice culture model demonstrates interneuronal spreading of alpha-synuclein aggregates

Here we describe the use of an organotypic hippocampal slice model for studying -synuclein aggregation and inter-neuronal spreading initiated by injection of preformed -synuclein filaments (PFFs). PFF injection at dentate gyrus templates the endogenous -synuclein to form aggregates in axons and cell bodies that spread to CA3 and CA1 regions. Aggregates were insoluble and phosphorylated at serine 129, recapitulating Lewy pathology features found in Parkinsons disease and other synucleinopathies. The spreading of the aggregates were favoring the anterograde direction in the slice model. The model allowed development of slices expressing only serine-129 phosphorylation-deficient human -synuclein (S129G) using adeno-associated viral (AAV) vector in -synuclein knockout slices. Processes of aggregation and spreading of -synuclein were thereby shown to be independent of phosphorylation at serine 129. We provide methods and highlight crucial steps for PFF microinjection and characterization of aggregate formation and spreading. Slices derived from genetically engineered mice or manipulated by using viral vectors allow testing of hypotheses on mechanisms involved in formation of -synuclein aggregates and their prion-like spreading.

neuroscience↗

bioRxiv2018

Chemogenetic ligands for translational neurotheranostics

Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) are a popular chemogenetic technology for manipulation of neuronal activity in uninstrumented awake animals with potential for precision medicine-based clinical theranostics. DREADD ligands developed to date are not appropriate for such translational applications. The prototypical DREADD agonist clozapine N-oxide (CNO) lacks brain entry and converts to clozapine. The second-generation DREADD agonist, Compound 21 (C21), was developed to overcome these limitations. We found that C21 has low brain penetrance, weak affinity, and low in vivo DREADD occupancy. To address these drawbacks, we developed two new DREADD agonists, JHU37152 and JHU37160, and the first dedicated positron emission tomography (PET) DREADD radiotracer, [18F]JHU37107. JHU37152 and JHU37160 exhibit high in vivo DREADD potency. [18F]JHU37107 combined with PET allows for DREADD detection in locally-targeted neurons and at their long-range projections, enabling for the first time, noninvasive and longitudinal neuronal projection mapping and potential for neurotheranostic applications.

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