Biology subjects

Ivanov, S.

Publications and source records attributed to Ivanov, S..

12 recordsLinked to original sources

bioRxiv2026

An Aphid Protein Interferes with Plant endomembrane Trafficking Machinery to Promote Aphid Survival and Plant Growth

Aphids deplete their host plants of amino acids and other essential nutrients by feeding on phloem sap. Autophagy, a conserved process across eukaryotes, maintains cellular homeostasis during starvation and environmental stress by recycling damaged or excess cellular components. Here, we identified and characterized a previously uninvestigated aphid salivary protein, termed Aphid Autophagy Related Factor 1 (AAF1), that is secreted into plant cells and manipulates plant physiology in a manner that benefits the aphid. We show that AAF1 interacts with four known or putative host autophagy related proteins: RABD1 and RABD2a (plant homologs of Ypt1/RAB1), ATG8, a ubiquitin like protein required for autophagosome formation, and PI3PT, a previously uncharacterized ER localized putative lipid binding or lipid transfer protein annotated as a phosphoinositide 3 phosphate transfer protein. We further demonstrate that the RAB binding domain of AAF1 and its interaction with RABD2a may be specifically required for AAF1 function. In addition, we identified unique, highly mobile compartments containing interacting AAF1 and RABD2a that are associated with the endoplasmic reticulum (ER) and dynamically traffic with autophagosomes. We propose that these compartments may be derived from endosomes or endosome to ER contact sites (EERCSs) and serve as platforms for autophagosome trafficking and/or biogenesis. We further propose that AAF1, RABD2a, and PI3PT function together to regulate the formation or function of these compartments, thereby supporting autophagosome formation and trafficking. Finally, we show that stable expression of AAF1 in plants enhances growth and biomass in an autophagy dependent manner.

plant biology↗

bioRxiv2026

Adrenal Gland Macrophage-derived TGF-β Governs Vascular Permeability to Drive Monocyte Recruitment during Stress

The adrenal glands are central regulators of systemic stress responses through tightly controlled glucocorticoid production. Yet, the contribution of local immune-vascular interactions to adrenal stress adaptation remains poorly understood. Here, we investigated the role of adrenal gland macrophages in coordinating stress-induced immune remodeling and vascular function. By integrating single-cell RNA sequencing datasets across four distinct stress models, including acute cold exposure, chronic social defeat, chronic inflammation, and systemic Candida albicans infection, we identified a conserved increase in monocyte recruitment to the adrenal gland, accompanied by dynamic macrophage transitions. Comparative transcriptomic and ligand-receptor analyses identified transforming growth factor-{beta} (TGF{beta}) as a dominant macrophage-derived signal targeting adrenal endothelial cells across all stress conditions. Pharmacological blockade of TGF{beta} receptor signaling reduced endothelial activation, vascular permeability, and monocyte infiltration into the adrenal gland following stress, without directly altering resident macrophage numbers. Using genetic fate-mapping and conditional knockout models, we demonstrate that macrophage-derived, but not endothelial-derived, TGF{beta} is required to promote enhanced endothelial adhesion molecule expression, vascular fenestration, permeability, and efficient monocyte recruitment. Loss of macrophage TGF{beta} production also led to exacerbated systemic stress hormone levels. Together, these findings uncover a previously unrecognized macrophage-endothelial axis in the adrenal gland, whereby macrophage-derived TGF{beta} regulates vascular properties to support immune cell recruitment and stress adaptation. This immune-vascular crosstalk provides new mechanistic insights into adrenal homeostasis and suggests potential therapeutic avenues for disorders associated with dysregulated chronic stress.

immunology↗

bioRxiv2026

The limits of Bayesian estimates of divergence times in measurably evolving populations

Bayesian inference of divergence times for extant species using molecular data is an unconventional statistical problem: Divergence times and molecular rates are confounded, and only their product, the molecular branch length, is statistically identifiable. This means we must use priors on times and rates to break the identifiability problem. As a consequence, there is a lower bound in the uncertainty that can be attained under infinite data for estimates of evolutionary timescales using the molecular clock. With infinite data (i.e., an infinite number of sites and loci in the alignment) uncertainty in ages of nodes in phylogenies increases proportionally with their mean age, such that older nodes have higher uncertainty than younger nodes. On the other hand, if extinct taxa are present in the phylogeny, and if their sampling times are known (i.e., heterochronous data), then times and rates are identifiable and uncertainties of inferred times and rates go to zero with infinite data. However, in real heterochronous datasets (such as viruses and bacteria), alignments tend to be small and how much uncertainty is present and how it can be reduced as a function of data size are questions that have not been explored. This is clearly important for our understanding of the tempo and mode of microbial evolution using the molecular clock. Here we conducted extensive simulation experiments and analyses of empirical data to develop the infinite-sites theory for heterochronous data. Contrary to expectations, we find that uncertainty in ages of internal nodes scales positively with the distance to their closest tip with known age (i.e., calibration age), not their absolute age. Our results also demonstrate that estimation uncertainty decreases with calibration age more slowly in datasets with more, rather than fewer site patterns, although overall uncertainty is lower in the former. Our statistical framework establishes the minimum uncertainty that can be attained with perfect calibrations and sequence data that are effectively infinitely informative. Finally, we discuss the implications for viral sequence datasets. In a vast majority of cases viral data from outbreaks is not sufficiently informative to display infinite-sites behaviour and thus all estimates of evolutionary timescales will be associated with a degree of uncertainty that will depend on the size of the dataset, its information content, and the complexity of the model. We anticipate that our framework is useful to determine such theoretical limits in empirical analyses of microbial outbreaks. Significance statementGenetic sequences are routinely used to date the origins of outbreaks and the divergence of viral and bacterial lineages, but every such date carries uncertainty, and it has been unclear how much of that uncertainty more sequence data can remove. We show that for pathogens sampled repeatedly over time, precision depends not on how old an event is but on how close it lies to the nearest sample of known date, and that even large influenza and hepatitis B virus datasets fall far short of the precision that is theoretically attainable. This provides a simple diagnostic for judging how much confidence a molecular date deserves, and whether sequencing more genomes would improve its precision.

bioinformatics↗

bioRxiv2025

Mutation of M. truncatula SOBIR1 affects rhizobial specificity and arbuscular mycorrhizal colonization

Legume plants form symbiotic interactions with bacteria (rhizobia sp.) to obtain fixed nitrogen and arbuscular mycorrhizal fungi to obtain phosphorus and other nutrients. These interactions require the plant to distinguish beneficial from pathogenic organisms and trigger the plants innate immune system. We identified mutants in the Medicago truncatula SOBIR1 gene, known to be involved in innate immune response in multiple plants. We examined nodulation with multiple strains and species of Sinorhizobium and examined mycorrhizal interactions with the arbuscular mycorrhizal fungus Glomus versiforme (recently renamed Diversispora epigaea). Plants containing mutations in SOBIR1 exhibit normal nodulation with strains of S. meliloti, but fewer nodules when inoculated with S. medicae strains. The S. medicae nodules show evidence of accumulation of polyphenolic compounds, and abnormal arrangement of the rhizobia within the nodules. In contrast, when inoculated with Glomus versiforme. M. truncatula sobir1 mutant plants have increased mycorrhizal colonization and arbuscule number compared to the wild type. We localized a tagged SOBIR1 protein to the periarbuscular membrane interface. Together the data suggest the immune kinase SOBIR1 is involved in both nodulation and mycorrhizal interactions, but the effects of mutation differ.

plant biology↗

bioRxiv2025

Yeast-2-Hybrid-Seq and Bifluorescence Complementation Resources for assessing Protein:Protein Interactions in Arbuscular Mycorrhizal Roots: CKL2 as a Case Study

Reverse genetics, facilitated by CRISPR technologies and comprehensive sequence-indexed insertion mutant collections, has advanced the identification of plants genes essential for arbuscular mycorrhizal (AM) symbiosis. However, a mutant phenotype alone is generally insufficient to reveal the specific role of the protein in AM symbiosis and in many cases, identifying interacting partner proteins is useful. To enable identification of protein:protein interactions during AM symbiosis, we established a Medicago truncatula -Diversispora epigaea yeast-two-hybrid (Y2H) library which, through Y2H-seq screening, can provide a rank-ordered list of candidate interactors of a protein of interest. We also developed a vector system to facilitate bimolecular fluorescence complementation assays (BIFC) in mycorrhizal roots so that protein interactions can be assessed in their native cell types and sub-cellular locations. We demonstrate the utility of a Y2H-seq screen coupled with BIFC in mycorrhizal roots, with a search for proteins that interact with CYCLIN DEPENDENT LIKE KINASE 2 (CKL2), a kinase essential for AM symbiosis. The Y2H-seq screen identified three 14-3-3 proteins as the highest ranked CKL2 interacting proteins. BIFC assays in mycorrhizal roots provided evidence for a CKL2:14-3-3 interaction at the periarbuscular membrane (PAM) in colonized root cells. Down-regulation of 14-3-3 by RNA interference provides initial evidence for a function in AM symbiosis. Thus, CKL2 may utilize 14-3-3 proteins to direct signaling from the PAM. The Y2H and BIFC resources will accelerate understanding of protein functions during AM symbiosis.

plant biology↗

bioRxiv2025

CSF1R regulates monocyte subset differentiation and intracellular metabolism.

Monocytes are key circulating effectors of vascular homeostasis, innate immunity and inflammation. Following their generation in mouse bone marrow, classical (Ly6Chigh) monocytes are mobilized into the blood circulation where they mature into non-classical (Ly6Clow) patrolling monocytes or are recruited into peripheral tissues where they differentiate into tissue resident or inflammatory macrophages. Monocytes and macrophages express CSF1R (CD115), the receptor for lineage-specific growth factors CSF1 and IL34. Here, we report that acute CSF1R blockade or genetic deletion negatively interferes with monocyte intracellular metabolism and reduces blood Ly6Clow monocytes in part by blunting differentiation of Ly6Chigh monocytes. Based upon lineage-specific deletion of GFPT1 (Glutamine-Fructose-6-Phosphate Transaminase 1), the hexosamine biosynthetic pathway (HBP) is identified as a novel regulator of CSF1R expression and monocyte subset diversity. Our findings provide new insights into the link between CSF1R signaling, metabolic regulation, and monocyte survival and differentiation.

immunology↗

bioRxiv2024

Identification of conserved and tissue-restricted transcriptional profiles for lipid associated macrophages (LAMs)

Macrophages are essential immune cells present in all tissues, and are vital for maintaining tissue homeostasis, immune surveillance, and immune responses. Considerable efforts have identified shared and tissue-specific gene programs for macrophages across organs during homeostasis. This information has dramatically enhanced our understanding of tissue-restricted macrophage programming and function. However, few studies have addressed the overlapping and tissue-specific responses of macrophage subsets following inflammatory responses. One subset of macrophages that has been observed across several studies, lipid-associated macrophages (LAMs), have gained interest due to their unique role in lipid metabolism and potential as a therapeutic target. LAMs have been associated with regulating disease outcomes in metabolically related disorders including atherosclerosis, obesity, and nonalcoholic fatty liver disease (NAFLD). In this study, we utilized single-cell RNA sequencing (scRNAseq) data to profile LAMs across multiple tissues and sterile inflammatory conditions in mice and humans. Integration of data from various disease models revealed that LAMs share a set of conserved transcriptional profiles, including Trem2 and Lpl, but also identified key sets of tissue-specific LAM gene programs. Importantly, the shared LAM markers were highly conserved with human LAM populations that also emerge in chronic inflammatory settings. Overall, this analysis provides a detailed transcriptional landscape of tissue-restricted and shared LAM gene programs and offers insights into their roles in metabolic and chronic inflammatory diseases. These data may help instruct appropriate targets for broad or tissue-restricted therapeutic interventions to modulate LAM populations in disease.

immunology↗

bioRxiv2024

MACanalyzeR: scRNA-seq Analysis Tool Reveals PPARγHI Lipid-Associated Macrophages Facilitate Thermogenic Expansion in BAT

Macrophages in brown adipose tissue (BAT) play a complex role in regulating its activity. However, the role of macrophages in regulating BAT activation/deactivation has not yet been comprehensively characterized. To elucidate this, we developed MACanalyzeR, a scRNAseq-based tool specifically designed to explore the macrophage features at molecular and metabolic level. MACanalyzeR was applied in scRNA-seq datasets obtained from BAT with thermogenic loss (db/db mice) and activation (High Fat Diet, HFD). Our computational approach revealed that macrophages accumulating in BAT upon these conditions resemble lipid-associated macrophages (LAMs) with foaming-like features. BAT LAMs also show a significant enrichment of genes associated with mitochondria and lysosomes. Interestingly, LAMs identified in BAT from HFD mice positively correlate with thermogenic genes and exhibit an enrichment in PPAR{gamma} signaling pathway, with an activated mitochondrial metabolism. Cell dynamic strategy, revealed that LAM with high Pparg expression levels (PpargHIGH) progressively accumulate during skeletal muscle regeneration, suggesting a potential role for this LAM subcluster in maintaining tissue homeostasis. Our findings suggest PpargHIGH LAMs as a subclass of macrophages potentially contributing in preserving tissue homeostasis associated with high energy demand conditions such as thermogenic and regenerative stimuli.

physiology↗

bioRxiv2024

Oncogenic GNAS drives a gastric pylorus program in intraductal papillary mucinous neoplasms of the pancreas

BACKGROUND & AIMSIntraductal Papillary Mucinous Neoplasms (IPMNs) are cystic lesions and bona fide precursors for pancreatic ductal adenocarcinoma (PDAC). Recent studies have shown that pancreatic precancer is characterized by a transcriptomic program similar to gastric metaplasia. The aims of this study were to assay IPMN for pyloric markers, to identify molecular drivers, and to determine a functional role for this program in the pancreas. METHODSPyloric marker expression was evaluated by RNA-seq and multiplex immunostaining in patient samples. Cell lines and organoids expressing KrasG12D +/- GNASR201C underwent RNA sequencing. A PyScenic-based regulon analysis was performed to identify molecular drivers, and candidates were evaluated by RNA-seq, immunostaining, and small interfering RNA knockdown. Glycosylation profiling was performed to identify GNASR201C-driven changes. Glycan abundance was evaluated in patient samples. RESULTSPyloric markers were identified in human IPMN. GNASR201Cdrove expression of this program as well as an indolent phenotype characterized by distinct glycosyltransferase changes. Glycan profiling identified an increase in LacdiNAcs and loss of pro-tumorigenic Lewis antigens. Knockdown of transcription factors Spdef or Creb3l1 or chitinase treatment reduced LacdiNAc deposition and reversed the indolent phenotype. LacdiNAc and 3-sulfoLeA/C abundance discriminated low from high grade patient IPMN. CONCLUSIONGNASR201C drives an indolent phenotype in IPMN by amplifying a differentiated, pyloric phenotype through SPDEF/CREB3L1 which is characterized by distinct glycans. Acting as a glycan rheostat, mutant GNAS elevates LacdiNAcs at the expense of pro-tumorigenic acidic Lewis epitopes, inhibiting cancer cell invasion and disease progression. LacdiNAc and 3-Sulfo-LeA/C are mutually exclusive and may serve as markers of disease progression.

cancer biology↗

bioRxiv2020

Comprehensive analysis of HIV elite controllers and progressors' transcriptional profiles from CD8+ T lymphocytes demonstrates heterogeneity of pathways and master regulators, which may be essential for disease nonprogression

Cytotoxic and noncytotoxic CD8+ T lymphocyte responses are essential for the control of HIV infection. Understanding the mechanisms underlying HIV control in elite controllers (ECs), who do not progress to AIDS for many years without treatment, may facilitate the development of new effective therapeutic strategies. We performed a comprehensive analysis of the transcriptional profiles of CD8+ cells from ECs and treated and untreated progressors using an original pipeline. Cluster analysis enabled the identification of five distinct groups (EC groups 1-5) of ECs based on their transcription profiles. Profiles of EC groups 2-4 were associated with different numbers of differentially expressed genes, but the corresponding genes shared the same cellular processes. These three groups were associated with increased metabolism, survival, proliferation, and the absence of an "exhausted" phenotype of CD8+ lymphocytes, compared to untreated progressors. The transcriptional profiles of EC group 1 were opposite to those of EC groups 2-4 and similar to those of the treated progressors. This group may be associated with residual dysfunction of T lymphocytes. The EC group 5 was indistinguishable from normal. EC groups 1 and 5 can have mechanisms of nonprogression unrelated to CD8+ cells. The transcription changes in CD8+ lymphocytes from ECs may be attributable to the receptors that modulate the functional states of CD8+ cells. We identified 22 receptors, which are potential master regulators and may be responsible for the observed expression changes of CD8+ cells in ECs. These receptors can be considered potential targets of therapeutic intervention, which may decelerate disease progression. IMPORTANCEA small group of HIV-infected individuals, known as elite controllers (ECs), do not develop AIDS for many years, despite being untreated. Understanding the mechanisms governing HIV control in ECs may help develop new effective therapeutic strategies. Since CD8+ T lymphocytes seem to play a significant role in HIV control, we analyzed the large number of transcriptional profiles in total CD8+ cells from ECs, treated, and untreated progressors. We found distinct groups of ECs, which may have different mechanisms governing HIV nonprogression, and we identified related pathways and cellular processes that are dissimilar from those in progressors. We also identified master regulators, key proteins in the signaling network that can be responsible for observed transcriptional changes in CD8+ cells from ECs and may be essential for disease nonprogression. These proteins may represent potential targets for therapeutic interventions.

microbiology↗

bioRxiv2020

Implementation of a Computing Pipeline for Evaluating the Extensibility of Boolean Networks' Structure and Function

Formal interaction networks are well suited for representing complex biological systems and have been used to model signalling pathways, gene regulatory networks, interaction within ecosystems, etc. In this paper, we introduce Sign Boolean Networks (SBNs), which are a uniform variant of Threshold Boolean Networks (TBFs). We continue the study of the complexity of SBNs and build a new framework for evaluating their ability to extend, i.e. the potential to gain new functions by addition of nodes, while also maintaining the original functions. We describe our software implementation of this framework and show some first results. These results seem to confirm the conjecture that networks of moderate complexity are the most able to grow, because they are not too simple, but also not too constrained, like the highly complex ones. Biological Regulation, Biological Networks, Sign Boolean Networks, Complexity, Extensibility, Network Growth

systems biology↗

bioRxiv2019

Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions

Adverse drug effects (ADEs) are one of the leading causes of death in developed countries and are the main reason for drug recalls from the market, whereas the ADEs that are associated with action on the cardiovascular system are the most dangerous and widespread. The treatment of human diseases often requires the intake of several drugs, which can lead to undesirable drug-drug interactions (DDIs), thus causing an increase in the frequency and severity of ADEs. An evaluation of DDI-induced ADEs is a nontrivial task and requires numerous experimental and clinical studies. Therefore, we developed a computational approach to assess the cardiovascular ADEs of DDIs. This approach is based on the combined analysis of spontaneous reports (SRs) and predicted drug-target interactions to estimate the five cardiovascular ADEs that are induced by DDIs, namely, myocardial infarction, ischemic stroke, ventricular tachycardia, cardiac failure, and arterial hypertension. We applied a method based on least absolute shrinkage and selection operator (LASSO) logistic regression to SRs for the identification of interacting pairs of drugs causing corresponding ADEs, as well as noninteracting pairs of drugs. As a result, five datasets containing, on average, 3100 ADE-causing and non-ADE-causing drug pairs were created. The obtained data, along with information on the interaction of drugs with 1553 human targets predicted by PASS Targets software, were used to create five classification models using the Random Forest method. The average area under the ROC curve of the obtained models, sensitivity, specificity and balanced accuracy were 0.838, 0.764, 0.754 and 0.759, respectively. The predicted drug targets were also used to hypothesize the potential mechanisms of DDI-induced ventricular tachycardia for the top-scoring drug pairs. The created five classification models can be used for the identification of drug combinations that are potentially the most or least dangerous for the cardiovascular system. Author summaryAssessment of adverse drug effects as well as the influence of drug-drug interactions on their manifestation is a nontrivial task that requires numerous experimental and clinical studies. We developed a computational approach for the prediction of adverse effects that are induced by drug-drug interactions, which are based on a combined analysis of spontaneous reports and predicted drug-target interactions. Importantly, the approach requires only structural formulas to predict adverse effects, and, therefore, may be applied for new, insufficiently studied drugs. We applied the approach to predict five of the most important cardiovascular adverse effects, because they are the most dangerous and widespread. These effects are myocardial infarction, ischemic stroke, ventricular tachycardia, arterial hypertension and cardiac failure. The accuracies of predictive models were relatively high, in the range of 73-81%; therefore, we performed a prediction of the five cardiovascular adverse effects for the large number of drug pairs and revealed the combinations that are the most dangerous for the cardiovascular system. We consider that the developed approach can be used for the identification of pairwise drug combinations that are potentially the most or least dangerous for the cardiovascular system.

bioinformatics↗