Biology subjects

Stojanovic, O.

Publications and source records attributed to Stojanovic, O..

5 recordsLinked to original sources

bioRxiv2024

Steric Repulsion Counteracts ER-to-Lipid Droplet Protein Movement

Lipid droplets (LDs) are uniquely shaped organelles consisting of a neutral lipid core surrounded by a phospholipid monolayer, continuous with the cytosolic leaflet of the endoplasmic reticulum (ER). The dynamics and function of LDs are closely tied to their proteome composition, which is subject to dynamic remodeling. Key proteins essential for LD biology relocate from the ER to LDs, yet the mechanisms governing their movement and accumulation in LDs remain poorly understood. Here, we developed an innovative ex vivo tool to quantify and classify ER proteins based on their affinity for LDs. We found a broad spectrum of ER-to-LD partitioning affinities. We identified steric hindrance as a key factor in regulating ER-to-LD protein transfer, where proteins with only slightly higher LD affinity can effectively displace those with lower affinity from the LD surface. Consistent with this model, we observed that differentiation of 3T3 pre-adipocytes into adipocytes involves extensive remodeling of ER proteins targeting LDs, with Plin1--a high-affinity LD protein--becoming predominantly recruited and excluding other ER proteins. These findings highlight lateral protein-protein exclusion as a fundamental mechanism in shaping the LD proteome, providing new insights into LD biogenesis and function.

biophysics↗

bioRxiv2023

Perilipin membrane association determines Lipid Droplet heterogeneity in differentiating adipocytes

The storage of fat within lipid droplets (LDs) of adipocytes is critical for whole-body health. Acute fatty acid (FA) uptake by differentiating adipocytes leads to the formation of at least two LD-classes marked by distinct perilipins (PLINs). How this LD-heterogeneity arises is an important yet unresolved cell biological problem. Here, we show that an unconventional integral membrane-segment (iMS) targets the adipocyte specific LD-surface factor PLIN1 to the endoplasmic reticulum (ER) and facilitates high affinity binding to the first LD-class. The other PLINs remain largely excluded from these LDs until FA-influx recruits them to a second LD-population. Preventing ER-targeting turns PLIN1 into a soluble, cytoplasmic LD-protein, reduces its LD-affinity and switches its LD-class specificity. Conversely, moving the iMS to PLIN2 leads to ER-insertion and formation of a separate LD-class. Our results shed light on how differences in organelle targeting and disparities in lipid-affinity of LD-surface factors contribute to formation of LD-heterogeneity. HighlightsO_LIPLIN1 is an integral membrane protein behaving as a class I LD protein. C_LIO_LIAn unconventional integral membrane segment (iMS) mediates ER insertion. C_LIO_LIThe iMS is required for LD-heterogeneity in differentiating adipocytes. C_LIO_LIHigh affinity LD-targeting from the ER is likely a gated process. C_LI eTOC blurbMajchrzak et al. use biochemistry, imaging and in vitro experiments to identify molecular features in PLIN1 that determine ER insertion and high lipid droplet affinity which are both important in the generation of LD-heterogeneity within differentiating adipocytes.

cell biology↗

bioRxiv2021

Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets

Environmental scientists often have to predict a complex phenomenon from a heterogeneous collection of datasets. This is particularly challenging if there are systematic differences between them, as is often the case. Accounting for these differences requires a larger number of parameters and thus increases the risk of overfitting. We investigate how Bayesian hierarchical models can help mitigate this problem by allowing the practitioner to explicitly incorporate information about the dataset structure and general domain knowledge. To this end, we look at a typical application in remote sensing: the estimation of leaf area index (of white winter wheat), an important indicator for agronomical modeling, from measurements of reflectance spectra collected at different locations and growth stages. Since the insights gained from such a model could be used to inform policy or business decisions, the interpretability of the model is a primary concern. We, therefore, focus on models that capture the association between leaf area index and the spectral reflectance at various wavelengths by spline-based kernel functions, which can be visually inspected and analyzed. We compare models with three different levels of hierarchy: a non-hierarchical baseline model, a model with hierarchical bias parameter, and a model in which bias and kernel parameters are hierarchically structured. We analyze them using Markov Chain Monte Carlo sampling diagnostics and an intervention-based measure of feature importance. The improved robustness and interpretability of this approach lead us to recommend Bayesian hierarchical models as a versatile tool for environmental sciences and beyond, particularly in scenarios where the available data sources are heterogeneous.

ecology↗

bioRxiv2020

Cold Exposure Protects from Neuroinflammation Through Immunologic Reprogramming

Autoimmunity is energetically costly, but the impact of a metabolically active state on immunity and immune-mediated diseases is unclear. Ly6Chi monocytes are key effectors in CNS autoimmunity with elusive role in priming naive autoreactive T cells. Here we provide unbiased analysis of the immune changes in various compartments during cold exposure, and show that this energetically costly stimulus markedly ameliorates active experimental autoimmune encephalomyelitis (EAE). Cold exposure decreases MHCII on monocytes at steady-state and in various inflammatory mouse models, and suppresses T cell priming and pathogenicity through the modulation of monocytes. Genetic, or antibody-mediated monocyte depletion, or adoptive transfer of Th1- or Th17-polarized cells for EAE abolish the cold-induced effects on T cells or EAE, respectively. These findings provide a mechanistic link between environmental temperature and neuroinflammation, and suggest competition between cold-induced metabolic adaptations and autoimmunity as energetic trade-off beneficial for the immune-mediated diseases.

immunology↗

bioRxiv2019

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-art hhh4 model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology.\n\nAuthor summaryWhy was this study done?\n\nO_LIStatistical modeling is invaluable to public-health policy as it helps understand and anticipate the dynamics of the spread of infectious diseases. The available training data is often limited and reported with a low spatial and temporal resolution. This poses a challenge and makes it particularly important to incorporate domain knowledge and prior assumptions to guide the modeling process.\nC_LIO_LIIn order to evaluate the trustworthiness and reliability of a models predictions, it is crucial to be able to interpret the model and quantify the model uncertainty.\nC_LIO_LITo address this, we develop an interpretable model that uses Bayesian inference (rather than commonly used maximum likelihood estimation) and provides a probability distribution over inferred parameters.\nC_LI\n\nWhat did the researchers do and find?\n\nO_LIWe develop and test a single probabilistic model that learns to predict the number of weekly case counts for three different diseases (campylobacteriosis, rotaviral enteritis and Lyme borreliosis) at the county level one week ahead of time.\nC_LIO_LIWe employ a Bayesian Monte Carlo regression approach that provides an estimate of the full probability distribution over inferred parameters as well as model predictions.\nC_LIO_LIThe model learns an interpretable spatio-temporal kernel that captures typical interactions between infection cases of the tested diseases.\nC_LIO_LIThe predictive performance of our model compares favorably with a contemporary reference model for all diseases tested.\nC_LI\n\nWhat do these findings mean?\n\nO_LIInterpretable predictive models can be applied to surveillance data to gain insights into the dynamics of infectious diseases.\nC_LIO_LIProbabilistic modeling approaches provide a suitable framework for many challenges of working with epidemiological data.\nC_LI

epidemiology↗