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Sadeghi, M.

Publications and source records attributed to Sadeghi, M..

At least 37 records · Page 2Linked to original sources

Microglia ferroptosis is prevalent in neurodegenerative disease and regulated by SEC24B

Iron dysregulation has been implicated in multiple neurodegenerative diseases, including Parkinsons Disease (PD), Amyotrophic Lateral Sclerosis (ALS), and Multiple Sclerosis (MS). One prominent feature of affected brain regions are iron-loaded microglia, but how iron overload influences microglia physiology and disease response is poorly understood. Here we show that microglia are highly susceptible to ferroptosis, an iron-dependent form of cell death. In a tri-culture of human iPSC-derived neurons, astrocytes, and microglia, under ferroptosis-inducing conditions, microglia undergo a drastic shift in cell state, with increased ferritin levels, disrupted glutathione homeostasis, and altered cytokine signaling. Similar ferroptosis-associated signature (FAS) microglia were uncovered in PD, and the signature was also found in a large cohort of PD patient blood samples, raising the possibility that ferroptosis can be identified clinically. We performed a genome-wide CRISPR screen which revealed a novel regulator of ferroptosis, the vesicle trafficking gene SEC24B. A small molecule screen also nominated several candidates which blocked ferroptosis, some of which are already in clinical use. These data suggest that ferroptosis sits at the interface of cell death and inflammation, and inhibition of this process in microglia and other brain cells may provide new ways for treating neurodegenerative disease.

neuroscience↗

Reactive/Non-cooperative individuals advance Population's Synchronization: Modeling of Dictyostelium discoideum Concerted Signaling during Aggregation Phase

Orchestrated chemical signaling of single cells sounds to be a linchpin of emerging organization and multicellular life form. The social amoeba Dictiostelium discoiudium is a well-studied model organism to explore overall pictures of grouped behavior in developmental biology. The chemical waves secreted by aggregating Dictiostelium is a superb example of pattern formation. The waves are either circular or spiral in shape, according to the incremental population density of a self-aggregating community of individuals. Here, we revisit the spatiotemporal patterns that appear in an excitable medium due to synchronization of randomly firing individuals, but with a more parsimonies attitude. According to our model, a fraction of these individuals is refusal to amplify external stimulants. Our simulations indicate that the cells enhance the systems asymmetry and as a result, nucleate early sustainable spiral territory zones, provided that their relative population does not exceed a tolerable threshold.

biophysics↗

IL-31 uncouples skin inflammation from itch sensation in allergic dermatitis

Despite a robust literature associating IL-31 with pruritic inflammatory skin diseases, its influence on cutaneous inflammation and on the interplay between inflammatory and neurosensory pathways remain unmapped. Here, we examined the effects of IL-31 and its receptor IL31RA on both inflammation and pruritus in mouse models of dermatitis, including chronic topical house dust mite (HDM) exposure. Unexpectedly, Il31 deficiency increased cutaneous adaptive type 2 cytokine-producing cells and serum IgE. In addition, M2-like macrophages capable of fueling feedforward pro-inflammatory loops were selectively enriched in Il31ra-deficient skin. Thus, IL-31 is not strictly a pro-inflammatory cytokine, but rather an immunoregulatory factor that limits the magnitude of allergic skin inflammation. In contrast, Il31-deficient mice displayed a deficit in HDM-induced scratching. Itch reduction occurred despite intact - and in some cases increased - responsiveness of sensory neurons to other pruritogens released during HDM challenge, highlighting the non-redundant contribution of IL-31-receptive sensory afferents to pruritus in environmental allergen-induced dermatitis. When present, therefore, IL-31 uncouples circuits driven by sensory neurons and immune cells that converge in inflamed skin.

immunology↗

Quantitative analysis of membrane-mediated interactions and aggregation of flexible peripheral proteins orchestrating large-scale membrane remodeling

Biomembrane remodeling is essential for cellular trafficking, with membrane-binding peripheral proteins playing a key role in it. Significant membrane remodeling as in endo- and exocytosis is often due to aggregates of many proteins with direct or membrane-mediated interactions. Understanding this process via computer simulations is extremely challenging: protein-membrane systems involve time- and lengthscales that make atomistic simulations impractical, while most coarse-grained models fall short in resolving dynamics and physical effects of protein and membrane flexibility. Here, we develop a coarse-grained model of the bilayer membrane bestrewed with rotationally-symmetric flexible proteins, parametrized to reflect local curvatures and lateral dynamics of proteins. We investigate the kinetics, equilibrium distributions, and the free energy landscape governing the formation and break-up of protein clusters on the surface of the membrane. We demonstrate how the flexibility of the proteins as well as their surface concentration play deciding roles in highly selective macroscopic aggregation behavior.

biophysics↗

Universal features of epidemic models under social distancing guidelines

Social distancing as a form of nonpharmaceutical intervention has been enacted in many countries as a form of mitigating the spread of COVID-19. There has been a large interest in mathematical modeling to aid in the prediction of both the total infected population and virus-related deaths, as well as to aid government agencies in decision making. As the virus continues to spread, there are both economic and sociological incentives to minimize time spent with strict distancing mandates enforced, and/or to adopt periodically relaxed distancing protocols, which allow for scheduled economic activity. The main objective of this study is to reduce the disease burden in a population, here measured as the peak of the infected population, while simultaneously minimizing the length of time the population is socially distanced, utilizing both a single period of social distancing as well as periodic relaxation. We derive a linear relationship among the optimal start time and duration of a single interval of social distancing from an approximation of the classic epidemic SIR model. Furthermore, we see a sharp phase transition region in start times for a single pulse of distancing, where the peak of the infected population changes rapidly; notably, this transition occurs well before one would intuitively expect. By numerical investigation of more sophisticated epidemiological models designed specifically to describe the COVID-19 pandemic, we see that all share remarkably similar dynamic characteristics when contact rates are subject to periodic or one-shot changes, and hence lead us to conclude that these features are universal in epidemic models. On the other hand, the nonlinearity of epidemic models leads to non-monotone behavior of the peak of infected population under periodic relaxation of social distancing policies. This observation led us to hypothesize that an additional single interval social distancing at a proper time can significantly decrease the infected peak of periodic policies, and we verified this improvement numerically. While synchronous quarantine and social distancing mandates across populations effectively minimize the spread of an epidemic over the world, relaxation decisions should not be enacted at the same time for different populations.

systems biology↗

Network Analysis and Human Single Cell Brain Transcriptomics Reveal Novel Aspects of Alpha-Synuclein (SNCA) Biology

Alpha-synuclein (SNCA) aggregates are pathological hallmarks of synucleinopathies, neurodegenerative disorders including Parkinsons Disease (PD) and Lewy Body Dementia (LBD). Functional networks are not yet well-characterized for SNCA by CNS cell type. We investigated cell-specific differences in SNCA expression using Allen Brain Database single-nucleus RNA-seq data from human Middle Temporal Gyrus (MTG, 15,928 nuclei) and Anterior Cingulate Cortex (ACC, 7,258 nuclei). Weighted gene co-expression analysis (WGCNA) and hierarchical clustering identified a conserved SNCA co-expression module. Module genes were highly conserved (p < 10-10) and most highly expressed in excitatory neurons versus inhibitory neurons and other glial cells. SNCA co-expression module genes from ACC and MTG regions were then used to construct a protein-protein interaction (PPI) network, with SNCA empirically top hub. Genes in the SNCA PPI network were compared with genes nearest single nucleotide polymorphisms linked with PD risk in genome-wide association studies. 16 genes in our PPI network are nearest genes to PD risk loci (p < 0.0006) and 55 genes map within 100kb. Selected SNCA PPI network genes nearest PD risk loci were disrupted by CRISPR knock out gene editing for validation of network functional significance; disruption of STK39, GBA, and MBNL2 resulted in significantly elevated intracellular SNCA expression.

neuroscience↗

A Computational Model of Stem Cell Molecular Mechanism to Maintain Tissue Homeostasis

Stem cells, with their capacity to self-renew and to differentiate to more specialized cell types, play a key role to maintain homeostasis in adult tissues. To investigate how, in the dynamic stochastic environment of a tissue, non-genetic diversity and the precise balance between proliferation and differentiation are achieved, it is necessary to understand the molecular mechanisms of the stem cells in decision making process. By focusing on the impact of stochasticity, we proposed a computational model describing the regulatory circuitry as a tri-stable dynamical system to reveal the mechanism which orchestrate this balance. Our model explains how the distribution of noise in genes, linked to the cell regulatory networks, controls cell decision-making to maintain homeostatic state. The noise control over tissue homeostasis is achieved by regulating the probability of differentiation and self-renewal through symmetric and/or asymmetric cell divisions. Our model reveals, when mutations due to the replication of DNA in stem cell division, are inevitable, how mutations contribute to either aging gradually or the development of cancer in a short period of time. Furthermore, our model sheds some light on the impact of more complex regulatory networks on the system robustness against perturbations.

bioinformatics↗

Distinct midbrain response statistics are associated with sound recognition and discrimination during the construction of natural sound textures

The perception of sound textures, a class of natural sounds defined by statistical sound structure such as fire wind, and rain, has been proposed to arise through the integration of time-averaged summary statistics. Where and how the auditory system might encode these summary statistics to create internal representations of these stationary sounds, however, is unknown. Here, using natural textures and synthetic variants with reduced statistics, we show that summary statistics modulate the correlations between frequency organized neuron ensembles in the awake rabbit inferior colliculus. These neural ensemble correlation statistics capture high-order sound structure and allow for accurate neural decoding in a single trial recognition task with evidence accumulation times approaching 1 s. In contrast, the average activity across the neural ensemble (neural spectrum) provides a fast (tens of ms) and salient signal that contributes primarily to texture discrimination. Intriguingly, perceptual studies in human listeners reveals analogous trends: the sound spectrum is integrated quickly and serves as salient discrimination cue while high-order sound statistics are integrated slowly and contribute substantially more towards recognition. The findings suggest statistical sound cues such as the sound spectrum and correlation structure are represented by distinct response statistics in auditory midbrain ensembles, and that these neural response statistics may have dissociable roles and time scales for the recognition and discrimination of natural sounds. SIGNIFICANCE STATEMENTBeing able to recognize and discriminate natural sounds, such as from a running stream, a crowd clapping, or ruffling leaves is a critical task of the normal functioning auditory system. Humans can easily perform such tasks, yet they can be particularly difficult for the hearing impaired and they challenge our most sophisticated computer algorithms. This difficulty is attributed to the complex physical structure of such natural sounds and the fact they are not unique: they vary randomly in a statistically defined manner from one excerpt to the other. Here we provide the first evidence, to our knowledge, that the central auditory system is able to encode and utilize statistical sound cues for natural sound recognition and discrimination behaviors.

neuroscience↗

Large-scale simulation of biomembranes: bringing realistic kinetics to coarse-grained models

Biomembranes are two-dimensional assemblies of phospholipids that are only a few nanometres thick, but form micrometer-sized structures vital to cellular function. Explicit modelling of biologically relevant membrane systems is computationally expensive, especially when the large number of solvent particles and slow membrane kinetics are taken into account. While highly coarse-grained solvent-free models are available to study equilibrium behaviour of membranes, their efficiency comes at the cost of sacrificing realistic kinetics, and thereby the ability to predict pathways and mechanisms of membrane processes. Here, we present a framework for integrating coarse-grained membrane models with anisotropic stochastic dynamics and continuum-based hydrodynamics, allowing us to simulate large biomembrane systems with realistic kinetics at low computational cost. This paves the way for whole-cell simulations that still include nanometer/nanosecond spatiotemporal resolutions. As a demonstration, we obtain and verify fluctuation spectrum of a full-sized human red blood cell in a 150-milliseconds-long single trajectory. We show how the kinetic effects of different cytoplasmic viscosities can be studied with such a simulation, with predictions that agree with single-cell experimental observations.

biophysics↗

Optimal Multichannel Artifact Prediction and Removal for Brain Machine Interfaces and Neural Prosthetics

Neural implants that electrically stimulate neural tissue such as deep brain stimulators, cochlear implants (CI), and vagal nerve stimulators are becoming the routine treatment options for various diseases. Optimizing electrical stimulation paradigms requires closed-loop stimulation using simultaneous recordings of evoked neural activity in real time. Stimulus-evoked artifacts at the recording site are generally orders of magnitude larger than the neural signals, which challenge the interpretation of evoked neural activity. We developed a generalized artifact removal algorithm that can be applied in a variety of neural recording modalities. The procedure leverages known electrical stimulation currents to derive optimal filters that are used to predict and remove artifacts. We validated the procedure using paired recordings and electrical stimulation from sciatic nerve axons, high-rate bilateral CI stimulation, and concurrent multichannel stimulation in auditory midbrain and recordings in auditory cortex. We demonstrate a vast enhancement in the quality of recording even for high-throughput multi-site stimulation with typical improvements in the signal-to-noise ratio between 20-40 dB. The algorithm is efficient, can be scaled to arbitrary number of sites, and is applicable in range of recording modalities. It has numerous benefits over existing approaches and thus should be valuable for emerging neural recording and stimulation technologies.

neuroscience↗

The lottery of spermatogenesis

The causes of male infertility are yet to be fully understood. Here, we propose that male infertility can be partly attributed to the insufficiency of cytoplasmically-inherited proteins in sperms. Using a simple stochastic model of spermatogenesis, we show that in a range of parameters, the proportion of viable sperms can be reduced simply due to stochastic transmission of proteins from spermatogonia to spermatids.

developmental biology↗

A self-administered, artificial intelligence (AI) platform for cognitive assessment in multiple sclerosis (MS)

BackgroundCognitive impairment is common in patients with MS. Accurate and repeatable measures of cognition have the potential to be used as a marker of disease activity. We developed a 5-minute computerized test to measure cognitive dysfunction in patients with MS. The proposed test -named Integrated Cognitive Assessment (ICA)- is self-administered and language-independent.\n\nObjectiveTo determine ICAs validity as a digital biomarker for assessing cognitive performance in MS.\n\nMethods91 MS patients and 83 healthy controls (HC) took part in substudy 1, in which each participant took the ICA test and the Brief International Cognitive Assessment for MS (BICAMS). We assessed ICAs test-retest reliability, its correlation with BICAMS, its sensitivity to discriminate patients with MS from the HC group, and its accuracy in detecting cognitive dysfunction. In substudy 2, we recruited 48 MS patients, and examined the association between the level of serum neurofilament light (NfL) in these patients and their ICA scores.\n\nResultsICA demonstrated excellent test-retest reliability (r=0.94), with no learning bias (i.e. no significant practice effect); and had high level of convergent validity with BICAMS. ICA was sensitive in discriminating the MS patients from the HC group, and demonstrated a high accuracy (AUC = 95%) in discriminating cognitively normal from cognitively impaired participants. Additionally, we found a strong association (r=-0.79) between ICA score and the level of NfL in MS patients.\n\nConclusionsICA can be used as a digital biomarker for assessment and monitoring of cognitive performance in MS patients. In comparison to standard cognitive tools for MS (e.g. BICAMS), ICA is shorter in duration, does not show a learning bias, is independent of language, and takes advantage of artificial intelligence (AI) to identify cognitive status of patients more accurately. Being a digital test, it further has the potential for easier electronic health record or research database integration.

neuroscience↗

A hierarchical approach to model decision making: a study in chemotactic behavior of Escherichia coli

Reducing the complex behavior of living entities to its underlying physical and chemical processes is a formidable task in biology. Complex behaviors can be characterized as decision making: the ability to process the incoming information via an intracellular network and act upon this information to choose appropriate strategies. Motility is one such behavior that has been the focus many modeling efforts in the past. Our aim is to reduce the chemotactic behavior in E. coli to its molecular constituents in order to paint a comprehensive and end-to-end picture of this intricate behavior. We utilize a hierarchical approach, consisting of three layers, to achieve this goal: at the first level, chemical reactions involved in chemotaxis are simulated. In the second level, the chemical reactions give rise to the mechanical movement of six independent flagella. At the last layer, the two lower layers are combined to allow a digital bacterium to receive information from its environment and swim through it with verve. Our results are in concert with the experimental studies concerning the motility of E. coli cells. In addition, we show that our detailed model of chemotaxis is reducible to a non-homogeneous Markov process.

systems biology↗

Making ATP fast and slow: do yeasts play a mixed strategy to metabolise glucose?

The ability of some microorganisms to switch from respiration to fermentation in the presence of oxygen-the so-called Crabtree effect-has been a fascinating subject of study at the theoretical and experimental fronts. Game-theoretical approaches have been routinely used to examine and explain the way a microorganism, such as yeast, would switch between the two ATP-producing pathways, i.e., respiration and fermentation. Here we attempt to explain the switch between respiration and fermentation in yeast by constructing a simple metabolic switch. We then utilise an individual-based model, in which each individual is equipped with all the relevant chemical reactions, to see how cells equipped with such metabolic switch would behave in different conditions. We further investigate our proposed metabolic switch using the game-theoretical approach. Based on this model, we postulate that individuals play a mixed game of glucose metabolism in the population. This approach not only sheds some light in the varieties of metabolic regulations that can be utilised by the individual in the population in competition with others for a common resource, it would also allow a better understanding of the causes of the Warburg effect and similar phenomena observed in nature.

systems biology↗

On the Periodic Gain of the Ribosome Flow Model

1. Introduction 1. Introduction 2. Problem Formulation 3. Optimal Control and... 4. Optimality of the... References The Ribosome Flow Model (RFM) model is a deterministic model of translation with n sites [1, 2], It is a mean-field approximation of a standard model for translation elogation Totally Asymmetric Simple Exclusion Process (TASEP) [3, 4]. The model can be written as follows: [Formula 1](1)where x(t) is the occupancy vector x(t) = [x1(t); x2(t)), ..., xn(t))], and where all the{lambda} ...

systems biology↗

A highly divergent picornavirus infecting the gut epithelia of zebrafish (Danio rerio) in research institutions world-wide

Zebrafish have been extensively used as a model system for research in vertebrate development and pathogen-host interactions. We describe the complete genome of a novel picornavirus identified during a viral metagenomics analysis of zebrafish gut tissue. The closest relatives of this virus showed identity of [&le;]19.8% in their P1 capsids and [&le;]35.4% in their RdRp qualifying zebrafish picornavirus 1 (ZfPV1) as member of a novel genus with a proposed name of Cyprivirus. RT-PCR testing of zebrafish from 41 institutions from North America, Europe, and Asia showed ZfPV1 to be highly prevalent world-wide. In situ hybridization of whole zebrafish showed viral RNA was restricted to a subset of enterocytes and cells in the subjacent lamina propria of the intestine and the intestinal mucosa. This naturally occurring and apparently asymptomatic infection (in wild type zebrafish lineage AB) provides a natural infection system to study picornavirus-host interactions in an advanced vertebrate model organism. Whether ZfPV1 infection affects any immunological, developmental or other biological processes in wild type or mutant zebrafish lineages remains to be determined.

microbiology↗

Integrated Cognitive Assessment: Speed and Accuracy of Visual Processing as a Reliable Proxy to Cognitive Performance

Various mental disorders are accompanied by some degree of cognitive impairment. Particularly in neurodegenerative disorders, cognitive impairment is the phenotypical hallmark of the disease. Effective, accurate and timely cognitive assessment is key to early diagnosis of this family of mental disorders. Current standard-of-care techniques for cognitive assessment are primarily paper-based, and need to be administered by a healthcare professional; they are additionally language and education-dependent and typically suffer from a learning bias. These tests are thus not ideal for large-scale pro-active cognitive screening and disease progression monitoring. We developed the Integrated Cognitive Assessment (ICA), a 5-minute computerized cognitive assessment tool based on a rapid visual categorization task, in which a series of carefully selected natural images of varied difficulty are presented to participants. Overall 448 participants, across a wide age-range with different levels of education took the ICA test. We compared participants ICA test results with a variety of standard pen-and-paper tests that are routinely used to assess cognitive performance. ICA had excellent test-retest reliability, and was significantly correlated with all the reference cognitive tests used here, demonstrating ICAs ability as one unified test that can assess various cognitive domains.

neuroscience↗

A Neural Ensemble Correlation Code for Sound Category Identification

Humans and other animals effortlessly identify sounds and categorize them into behaviorally relevant categories. Yet, the acoustic features and neural transformations that enable the formation of perceptual categories are largely unknown. Here we demonstrate that correlation statistics between frequency-organized cochlear sound channels are reflected in the neural ensemble activity of the auditory midbrain and that such activity, in turn, can contribute to discrimination of perceptual categories. Using multi-channel neural recordings in the auditory midbrain of unanesthetized rabbits, we first demonstrate that neuron ensemble correlations are highly structured in both time and frequency and can be decoded to distinguish sounds. Next, we develop a probabilistic framework for measuring the nonstationary spectro-temporal correlation statistics between frequency organized channels in an auditory model. In a 13-category sound identification task, classification accuracy is consistently high (>80%), improving with sound duration and plateauing at ~ 1-3 seconds, mirroring human performance trends. Nonstationary short-term correlation statistics are more informative about the sound category than the time-average correlation statistics (84% vs. 73% accuracy). When tested independently, the spectral and temporal correlations between the model outputs achieved a similar level of performance and appear to contribute equally. These results outline a plausible neural code in which correlation statistics between neuron ensembles of different frequencies can be read-out to identify and distinguish acoustic categories.

neuroscience↗