Biology subjects

Sergushichev, A.

Publications and source records attributed to Sergushichev, A..

7 recordsLinked to original sources

bioRxiv2026

Immune Checkpoint Therapy Drives Maturation of a Cellular Neighborhood Nucleated by T Cell-APC Triads Enabling Spatially Compartmentalized Tumor Immunity

Spatially organized immune hubs of T cells and antigen-presenting cells (APCs) have been linked to immune checkpoint therapy (ICT) efficacy, yet the mechanisms underlying their function remain unclear. Using CODEX multiplex imaging, we longitudinally characterized the dynamic evolution of intratumoral cellular neighborhoods (CN) defined by "triad" interactions of CD4 and CD8 T cells with two distinct myeloid APC populations: cDC1s and IFN{gamma}-activated macrophages. We termed this CN the immunity-promoting CN (IP-CN) and tracked its progressive development during tumor rejection induced by -CTLA-4/-PD-1 therapy. A coordinated IFN{gamma} and TNF signaling signature accompanied the IP-CN assembly. Over time, the IP-CN underwent functional maturation, forming specialized sub-neighborhoods that compartmentalized proliferating T cells at the tumor periphery versus cytotoxic T effector cells interacting with tumor cell targets. Our findings reveal a spatiotemporal mechanism by which the IP-CN sustains and amplifies cytotoxic T cell responses, demonstrating how T cell-APC neighborhoods orchestrate tumor immunity.

immunology↗

bioRxiv2023

Understanding Complex Trait Susceptibilities and Ethnical Diversity in a Sample of 4,145 Russians Through Analysis of Clinical and Genetic Data

The population of Russia consists of more than 150 local ethnicities. The ethnic diversity and geographic origins, which extend from eastern Europe to Asia, make the population uniquely positioned to investigate the shared properties of inherited disease risks between European and Asian ethnicities. We present the analysis of genetic and phenotypic data from a cohort of 4,145 individuals collected in three metro areas in western Russia. We show the presence of multiple admixed ancestry clusters spanning from primarily European to Asian and high identity-by-descent sharing with the Finnish population. As a result, there was notable enrichment of Finnish-specific variants in Russia. We illustrate the utility of Russian-descent cohorts for discovery of novel population-specific genetic associations, as well as replication of previously identified associations that were thought to be population-specific in other cohorts. Finally, we provide access to a database of GWAS results for 465 unique phenotypes and allele frequencies.

genetics↗

bioRxiv2022

Phantasus: web-application for visual and interactive gene expression analysis

Transcriptomic profiling became a standard approach to quantify a cell state, which led to accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires a significant technical expertise. Here we present Phantasus - a user-friendly web-application for interactive gene expression analysis which provide a streamlined access to more than 84000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows to go all the way from loading, normalizing and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed on-line at https://ctlab.itmo.ru/phantasus or https://artyomovlab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under MIT licence.

bioinformatics↗

bioRxiv2022

GCDPipe: risk gene, cell type, and drug ranking for complex traits

We introduce a user-friendly machine learning tool for risk gene, cell type, and drug ranking for complex traits - GCDPipe. It uses gene-level GWAS-derived data and publicly available expression data to train a model for prediction of disease risk genes and relevant cell types. Gene-ranking information is then coupled with known drug targets data to prioritize drugs based on their estimated functional effects associated with identified risk genes. The pipeline was tested in two case studies: inflammatory bowel disease (IBD) and schizophrenia, then it was applied to Alzheimers disease to investigate potential options for drug repurposing. The results show that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets.

bioinformatics↗

bioRxiv2021

A non-canonical role for glutamate decarboxylase 1 in cancer cell amino acid homeostasis, independent of the GABA shunt.

Glutamate decarboxylase 1 (GAD1) is best known for its role in producing the neurotransmitter {gamma}-amino butyric acid (GABA) as part of the "GABA shunt" metabolic pathway, an alternative mechanism of glutamine anaplerosis for TCA cycle metabolism (Yogeeswari et al., 2005). However, understanding of the metabolic function of GAD1 in non-neuronal tissues has remained limited. Here, we show that GAD1 supports cancer cell proliferation independent of the GABA shunt. Despite its elevated expression in lung cancer tissue, GAD1 is not engaged in the GABA shunt in proliferating non-small cell lung cancer (NSCLC) cells, but rather is required for regulating amino acid homeostasis. Silencing GAD1 promotes a broad deficiency in amino acid uptake, leading to reduced glutamine-dependent TCA cycle metabolism and defects in serum- and amino acid-stimulated mTORC1 activation. Mechanistically, GAD1 regulates amino acid uptake through ATF4-dependent amino acid transporter expression including SLC7A5 (LAT1), an amino acid transporter required for branched chain amino acid (BCAA) uptake. Overexpression of LAT1 rescues the proliferative and mTORC1 signalling defects of GAD1-deficient tumor cells. Our results, therefore, define a non-canonical role for GAD1, independent of its characterised role in GABA metabolism, whereby GAD1 regulates amino acid homeostasis to maintain tumor cell proliferation.

cancer biology↗

bioRxiv2020

Open Source ImmGen: network perspective on metabolic diversity among mononuclear phagocytes

We dissect metabolic variability of mononuclear phagocyte (MNP) subpopulations across different tissues through integrative analysis of three large scale datasets. Specifically, we introduce ImmGen MNP Open Source dataset that profiled 337 samples and extended previous ImmGen effort which included 202 samples of mononuclear phagocytes and their progenitors. Next, we analysed Tabula Muris Senis dataset to extract data for 51,364 myeloid cells from 18 tissues. Taken together, a compendium of data assembled in this work covers phagocytic populations found across 38 different tissues. To analyse common metabolic features, we developed novel network-based computational approach for unbiased identification of key metabolic subnetworks based on cellular transcriptional profiles in large-scale datasets. Using ImmGen MNP Open Source dataset as baseline, we define 9 metabolic subnetworks that encapsulate the metabolic differences within mononuclear phagocytes, and demonstrate that these features are robustly found across all three datasets, including lipid metabolism, cholesterol biosynthesis, glycolysis, and a set of fatty acid related metabolic pathways, as well as nucleotide and folate metabolism. We systematically define major features specific to macrophage and dendritic cell subpopulations. Among other things, we find that cholesterol synthesis appears particularly active within the migratory dendritic cells. We demonstrate that interference with this pathway through statins administration diminishes migratory capacity of the dendritic cells in vivo. This result demonstrates the power of our approach and highlights importance of metabolic diversity among mononuclear phagocytes.

immunology↗

bioRxiv2018

The Soft Vertex Classification for Active Module Identification Problem

MotivationIntegrative network methods are commonly used for interpretation of high-throughput experimental biological data: transcriptomics, proteomics, metabolomics and others. One of the common approaches consists in finding a connected subnetwork of a global interaction network that best encompasses significant individual changes in the data and represents a so-called active module. Usually methods implementing this approach find a single subnetwork and thus solve a hard classification problem for vertices. This subnetwork inherently contains erroneous vertices, while no instrument is provided to estimate the confidence level of any particular vertex inclusion. To address this issue, in the current study we consider the active module problem as a soft classification problem. We propose a method to estimate probabilities of each vertex to belong to the active module based on Markov chain Monte Carlo subnetwork sampling.\n\nResultsThe proposed method allows to estimate the probability that an individual vertex belongs to the active module as well as the false discovery rate (FDR) for a given set of vertices. Given the estimated probabilities, it becomes possible to provide a connected subgraph in a consistent manner for any given FDR level: no vertex can disappear when the FDR level is relaxed. We show on simulated dataset that the proposed method has good computational performance and high classification accuracy. As an example of the performance of our method on real data, we run it on a protein-protein interaction network together with a gene expression DLBCL dataset. The results are consistent with the previous studies while, at the same time, the proposed approach is more flexible. Source code is available at https://github.com/ctlab/mcmcRanking under MIT licence.

systems biology↗