Biology subjects

Shtutman, M.

Publications and source records attributed to Shtutman, M..

9 recordsLinked to original sources

bioRxiv2026

Spatially Constrained Monte Carlo Permutation Test Reveals Diffusion Changes Near Stress Granules

Intracellular diffusion is inherently heterogeneous, yet single-particle tracking (SPT) analyses are often summarized using cell-wide average parameters that can obscure localized effects. Here, we tracked 40-nm genetically encoded multimeric (GEM) nanoparticles during stress granule (SG) formation and developed SPaCe-MC (Spatially Constrained Monte Carlo permutation test), a statistical framework that generates cytoplasm-specific null models to test whether diffusion associated with a specific cellular structure differs from that expected in the surrounding heterogeneous cytoplasm. Across three SG-inducing conditions, including oxidative stress, DDX3 inhibition, and combined treatment, bulk cytoplasmic analyses revealed distinct responses ranging from increased nanoparticle mobility to increased subdiffusive behavior. In contrast, SPaCe-MC consistently detected a local diffusion constraint in SG-associated regions relative to their treatment-matched cytoplasmic background, revealing a conserved local diffusion effect despite divergent global cytoplasmic responses. Together, our findings establish SPaCe-MC as a framework for identifying compartment-specific diffusion changes in heterogeneous cellular environments.

cell biology↗

bioRxiv2026

NLRP3 inflammasome-related microglial pyroptosis in EcoHIV infected mice

HIV-associated neurocognitive disorders (HAND) have become a major clinical concern, particularly among the aging HIV-1-seropositive population, which is generally characterized by persistent viral reservoirs and a lower level of chronic inflammation. NLRP3 inflammasome activation exhibits its unique role in the progression of many chronic inflammatory diseases. Furthermore, pyroptosis, an inflammatory form of programmed cell death, has been implicated in numerous neurological diseases. However, the mechanisms linking EcoHIV infection, microglial pyroptosis, and NLRP3 inflammasome activation remain incompletely understood. In this study, EcoHIV was retro-orbitally injected into C57BL/6J wild-type mice and analyzed at 14-, 30-, 60-, and 90-days post-infection to establish a NeuroHIV model. Additionally, in vitro, BV2 microglial cell line was infected with EcoHIV and treated with MCC950, an inhibitor of the NLRP3 inflammasome, for three days. Pyroptosis marker GSDMD, NLRP3 inflammasome components, Caspase-1 (a marker of inflammasome activation), HLA-DR (an immune activation marker), Programmed-death 1 (PD-1, an immune checkpoint molecule), and Ki67 (a cellular proliferation marker) were assessed by immunofluorescence staining. Results showed that EcoHIV-infected mice showed a peak in NLRP3 expression at 14 days post-infection, compared with controls, followed by a modest decline at 30 days, while GSDMD expression increased progressively across 14 and 30 days. These findings demonstrate dynamic changes in microglial pyroptosis and NLRP3 inflammasome activation over the course of EcoHIV infection. In vitro, EcoHIV-infected BV2 cells exhibited significantly increased EcoHIV-eGFP fluorescence compared with controls, confirming the utility of BV2 cells as an in vitro model of microglial EcoHIV infection. Expression levels of GSDMD and NLRP3 were elevated following infection, indicating enhanced pyroptosis and neuroinflammation. Treatment with MCC950 significantly reduced the expression of GSDMD, NLRP3, HLA-DR, PD-1, and Ki67, suggesting that inhibition of NLRP3 inflammasome activity suppresses both pyroptosis and microglial activation and proliferation. Together, elucidating the interplay between microglial pyroptosis and NLRP3 inflammasome activation may provide new insights into the pathogenesis and potential therapeutic strategies for NeuroHIV in the aging HIV-1-seropositive population.

neuroscience↗

bioRxiv2025

Biomedical Hypothesis Explainability with Graph-Based Context Retrieval

We introduce an explainability method for biomedical hypothesis generation systems, built on top of the novel Hypothesis Generation Context Retriever framework. Our approach combines semantic graph-based retrieval and relevant data-restrictive training to simulate real-world discovery constraints. Integrated with large language models (LLMs) via retrieval-augmented generation, the system explains hypotheses with contextual evidence using published scientific literature. We also propose a novel feedback loop approach, which iteratively identifies and corrects flawed parts of LLM-generated explanations, refining both the evidence paths and supporting context. We demonstrate the performance of our method with multiple large language models and evaluate the explanation and context retrieval quality through both expert-curated assessment and large-scale automated analysis. Our code is available at: https://github.com/IlyaTyagin/HGCR.

bioinformatics↗

bioRxiv2024

Single Particle Tracking of Genetically Encoded Nanoparticles: Optimizing Expression for Cytoplasmic Diffusion Studies

Single particle tracking (SPT) is a powerful technique for probing the diverse physical properties of the cytoplasm. Genetically encoded nanoparticles provide an especially convenient tool for such investigations, as they can be expressed and tracked in cells via fluorescence. Among these, 40-nm GEMs provide a unique opportunity to explore the cytoplasm. Their size corresponds to that of ribosomes and big protein complexes, allowing us to investigate the effects of the cytoplasm on the diffusivity of these objects while excluding the influence of chemical interactions during stressful events and pathological conditions. However, it has been shown that cytoplasmic viscosity is tightly regulated and plays a crucial role in maintaining homeostasis during protein synthesis and degradation. Despite this, the effects of GEM expression levels on diffusivity remain largely uncharacterized in mammalian cells. To optimize the GEMs tracking and estimate GEMs-expression effects we constructed dox-inducible GEM expression system and compare with a previously reported constitutive expression system. The optimized level of GEMs expression increases the measured diffusivity from 0.29 {+/-} 0.02 m2/sec in GEMs-overexpressed cells to 0.35 {+/-} 0.02 m2/sec; improve homogeneity throughout the cell population; and facilitates particle tracking. We also improved the analyses of GEM diffusivity by applying effective diffusion coefficient while considering the type of motion and assessing the heterogeneity in the type of motion by calculating the standard deviations of particle displacements. Statement of significanceDescribing cytoplasmic properties, such as environmental viscosity and protein complex motion, is essential for understanding molecular-level changes in cell function and pathology. A recently developed approach uses self-assembling fluorescent protein probes, expressed in cells, to investigate cytoplasmic properties through single-particle tracking (SPT). One such system employs genetically encoded multimeric (GEM) nanoparticles-- scaffold protein structures similar in size to ribosomes. This study addresses a key limitation in SPT of GEMs by examining how varying GEM expression levels affect measured diffusivity and tracking quality in mammalian cells. Our findings demonstrate that controlled GEM expression reduces particle overcrowding, increases measured diffusivity, and enhances track detection. This work contributes valuable insights into optimizing GEM nanoparticle applications for studying cytoplasmic viscosity and motion dynamics.

biophysics↗

bioRxiv2024

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia

Neurodegenerative pathologies such as Alzheimers disease, Parkinsons disease, Huntingtons disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing. Author SummaryThis manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.

bioinformatics↗

bioRxiv2023

Suppression of HIV and cocaine-induced neurotoxicity and inflammation by cell penetrable itaconate esters

HIV-associated neurological disorder (HAND) is a serious complication of HIV infection, marked by neurotoxicity induced by viral proteins like Tat. Substance abuse exacerbates neurocognitive impairment in people living with HIV. There is an urgent need for effective therapeutic strategies to combat HAND comorbid with Cocaine Use Disorder (CUD). Our analysis of the HIV and cocaine-induced transcriptomes in primary cortical cultures revealed a significant overexpression of the macrophage-specific gene, aconitate decarboxylase 1 (Acod1), caused by the combined insults of HIV and cocaine. ACOD1 protein converts the tricarboxylic acid intermediate cis-aconitate into itaconate during the activation of inflammation. The itaconate produced facilitates cytokine production and subsequently activates anti-inflammatory transcription factors, shielding macrophages from infection-induced cell death. While the role of itaconate in limiting inflammation has been studied in peripheral macrophages, its immunometabolic function remains unexplored in HIV and cocaine-exposed microglia. We assessed in this model system the potential of 4-octyl-itaconate (4OI), a cell-penetrable esterified form of itaconate known for its potent anti-inflammatory properties and potential therapeutic applications. We administered 4OI to primary cortical cultures exposed to Tat and cocaine. 4OI treatment increased the number of microglial cells in both untreated and Tat{+/-}Cocaine-treated cultures and also reversed the morphological altercations induced by Tat and cocaine. In the presence of 4OI, microglial cells also appeared more ramified, resembling the quiescent microglia. Consistent with these results, 4OI treatment inhibited the secretion of the proinflammatory cytokines IL-1, IL-1{beta}, IL-6, and MIP1- induced by Tat and cocaine. Transcriptome profiling further determined that Nrf2 target genes such as NAD(P)H quinone oxidoreductase 1 (Nqo1), Glutathione S-transferase Pi (Gstp1), and glutamate cysteine ligase catalytic (Gclc), were most significantly activated in Tat-4OI treated cultures, relative to Tat alone. Further, genes associated with cytoskeleton dynamics in inflammatory microglia were downregulated by 4OI treatment. Together, the results strongly suggest 4-octyl-itaconate holds promise as a potential candidate for therapeutic development aimed at addressing HAND coupled with CUD comorbidities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/559154v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@bfffa8org.highwire.dtl.DTLVardef@129ad8dorg.highwire.dtl.DTLVardef@1412ea2org.highwire.dtl.DTLVardef@5cb968_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Model of 4OI-mediated neuroprotection against Tat-Cocaine toxicity Tat and Tat-Cocaine treatment induce neuronal damage, which is mitigated by 4OI through microglia cells. This cartoon shows the reduction of harmful effects such as proinflammatory cytokine release, upregulation of P2R, PDE, and Acod1 by the presence of 4OI. This ester modified itaconate triggers anti-inflammatory responses and activates antioxidant pathways. C_FIG

neuroscience↗

bioRxiv2023

Heparan sulfate modifications of betaglycan promote TIMP3-dependent ectodomain shedding to fine-tune TGF-β signaling.

In pathologies such as cancer, aberrant Transforming Growth Factor-{beta} (TGF-{beta}) signaling exerts profound tumor intrinsic and extrinsic consequences. Intense clinical endeavors are underway to target this pivotal pathway. Central to the success of these interventions is pinpointing factors that decisively modulate the TGF-{beta} responses. Betaglycan/type III TGF-{beta} receptor (T{beta}RIII), is an established co-receptor for the TGF-{beta} superfamily known to bind directly to TGF-{beta}s 1-3 and inhibin A/B. While betaglycan can be membrane-bound, it can also undergo ectodomain cleavage to produce soluble-betaglycan that can sequester its ligands. The extracellular domain of betaglycan undergoes heparan sulfate and chondroitin sulfate glycosaminoglycan modifications, transforming betaglycan into a proteoglycan. Here we report the unexpected discovery that the heparan sulfate modifications are critical for the ectodomain shedding of betaglycan. In the absence of such modifications, betaglycan is not shed. Such shedding is indispensable for the ability of betaglycan to suppress TGF-{beta} signaling and the cells responses to exogenous TGF-{beta} ligands. Using unbiased transcriptomics, we identified TIMP3 as a key regulator of betaglycan shedding and thereby TGF-{beta} signaling. Our results bear significant clinical relevance as modified betaglycan is present in the ascites of patients with ovarian cancer and can serve as a marker for predicting patient outcomes and TGF-{beta} signaling responses. These studies are the first to demonstrate a unique reliance on the glycosaminoglycan modifications of betaglycan for shedding and influence on TGF-{beta} signaling responses. Dysregulated shedding of TGF-{beta} receptors plays a vital role in determining the response and availability of TGF-{beta}s, which is crucial for prognostic predictions and understanding of TGF-{beta} signaling dynamics.

biochemistry↗

bioRxiv2021

Accelerating COVID-19 research with graph mining and transformer-based learning

In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on graph-mining and the transformer model. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone. ReproducibilityAll code, details, and pre-trained models are available at https://github.com/IlyaTyagin/AGATHA-C-GP CCS CONCEPTS* Applied computing [->] Bioinformatics; Document management and text processing; * Computing methodologies [->] Learning latent representations; Neural networks; Information extraction; Semantic networks.

bioinformatics↗

bioRxiv2019

Inhibition of the DDX3 prevents HIV-1 Tat and cocaine-induced neurotoxicity by targeting microglia activation

HIV-1 Associated Neurocognitive Disorder (HAND) is commonly seen in HIV-infected patients. Viral proteins including Tat cause neuronal toxicity and is worsened by drugs of abuse. To uncover potential targets for anti-HAND therapy, we employed a literature mining system, MOLIERE. Here, we validated Dead Box RNA Helicase 3 (DDX3) as a target to treat HAND via a selective DDX3 inhibitor, RK-33. The combined neurotoxicity of Tat protein and cocaine was blocked by RK-33 in rat and mouse cortical cultures. Transcriptome analysis showed that Tat-activated transcripts include makers and regulators of microglial activation, and RK-33 blocked Tat-induced activation of these mRNAs. Elevated production of proinflammatory cytokines was also inhibited by RK-33. These findings show that DDX3 contributes to microglial activation triggered by Tat and cocaine, and DDX3 inhibition shows promise as a therapy for HAND. Moreover, DDX3 may contribute to the pathology of other neurodegenerative diseases with pathological activation of microglia.

neuroscience↗