Biology subjects

Ivanov, V. A.

Publications and source records attributed to Ivanov, V. A..

3 recordsLinked to original sources

bioRxiv2025

Mycoplasma gallisepticum FtsZ demonstrates properties that distinguish it from other known homologs

In bacteria, cell division usually occurs through binary fission, with the participation of genes from the dcw cluster. In mollicutes, this cluster is significantly reduced -- often only the ftsZ, ftsA, mraZ, and mraW genes remain, and sometimes ftsZ is completely absent. FtsZ is a key division protein that forms the Z-ring, but its role in mollicutes is questionable due to the absence of many cell division proteins and the cell wall. In the current study, we investigated the FtsZ protein of Mycoplasma gallisepticum, a bacterium with a reduced set of putative cell division genes (ftsZ, ftsA, ftsK). The results show that, unlike in well-studied bacteria, FtsZ in M. gallisepticum often exhibits polar rather than mid-cell localization. Overexpression of fluorescently labeled FtsZ enhances this polar localization and may lead to minicell formation. The FtsZ concentration was measured and, together with in vitro data, confirmed its ability to polymerize, similar to its homologs. Protein-protein interactions were also analyzed and confirmed the link of FtsZ to cell division. Overall, the results support the role of FtsZ in cell division, though its properties differ significantly from other known homologs.

molecular biology↗

bioRxiv2025

Decoding the Microbiome-Disease Axis with Interpretable Graph Neural Networks

The human gut microbiome is a complex ecosystem whose disruption is implicated in a wide spectrum of diseases, yet translating microbiome research into actionable therapeutics is hindered by a critical trade-off: existing models either prioritize predictive accuracy at the expense of interpretability or sacrifice performance for mechanistic insight, limiting their ability to pinpoint specific disease-driving microbial interactions and taxa. To address this, we introduce Graph neural network for Interpretable Microbiome (GIM), a graph neural network framework that integrates minimally processed taxonomic metadata as sparse node embeddings within an unweighted complete graph, enabling direct modeling of high-order microbial interactions through message passing. GIM achieves state-of-the-art classification performance on microbiome-disease prediction tasks (e.g., healthy vs. allergic states) while generating fine-grained, experimentally validated attributions at the level of taxonomic ranks, driver microbes, and putative microbe-to-microbe interactions. By bridging the gap between predictive accuracy and biological interpretability, GIM overcomes a key limitation in current approaches, offering a unified framework to both predict dysbiosis-associated disease states and identify actionable microbial targets for therapeutic intervention. This dual capability represents a critical advance toward precision microbiome engineering and scalable hypothesis generation in translational microbiome research.

systems biology↗

bioRxiv2024

Bacteroides vesicles promote functional alterations in gut microbiota composition

Inflammatory bowel diseases are characterized by chronic intestinal inflammation and alterations of gut microbiota composition. Bacteroides fragilis, which secretes outer membrane vesicles (OMVs) with polysaccharide A (PSA), can moderate inflammatory response and possibly alter microbiota composition. In this research, we created a murine model of chronic DSS- induced intestinal colitis and treated it with Bacteroides fragilis OMVs. We monitored the efficiency of OMVs therapy by determining the disease activity index (DAI) and histological examination (HE) of the intestine before and after vesicles exposure. We also analyzed the microbiota composition using 16S rRNA gene sequencing. Finally, we evaluated the volatile compounds composition in animals stools by HS-GC/MS to assess the functional activity of the microbiota. As a result, we observed a more effective intestine repair after OMVs treatment according to DAI and HE. The metabolomic study also indicated the microbiota functional activity change, showing a predominance of phenol and pentanoic acid in the control group compared to the group treated with DSS (DSS) and the group treated with OMVs (DSS OMV). We also observed a positive correlation of these metabolites with Saccharibacteria and Hungatei Clostridium in the control group, whereas in the DSS group there was a negative correlation of phenol and pentanoic acid with Lactococcus and Romboutsia. According to metabolome and sequencing data, the microbiota composition of the DSS OMV group was intermediate between the control and DSS groups. It can be concluded that OMVs not only have an anti-inflammatory effect, but also contribute to the recovery of the microbiota composition.

microbiology↗