Biology subjects

Sikirzhytskaya, A.

Publications and source records attributed to Sikirzhytskaya, A..

3 recordsLinked to original sources

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

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↗