Biology subjects

Dmitrieva, M.

Publications and source records attributed to Dmitrieva, M..

5 recordsLinked to original sources

bioRxiv2025

Biological effects of truffle extracts on bacteria, fungi, and microalgae

One of the current challenges on a global scale is the problem of antibiotic resistance. Excessive and inappropriate use of drugs in healthcare and agriculture has accelerated the development of diseases caused by antibiotic-resistant bacteria. In an effort to combat such resistance, it is extremely important to explore alternative sources of new molecules with biological activities. Truffles have become a promising source of new compounds because they contain a wide range of biologically active ingredients. In addition, they are a consortium of symbiotic microorganisms that produce natural products. The aim of this study was to study the antibiotic and growth stimulating effects of methanolic extracts of truffle fruiting bodies collected in Russia in 2020-2022. The extracts demonstrated inhibitory activity against St. carnosus, E. persicina, and kanamycin-resistant E. coli. Also, for the first time, we have demonstrated that truffle extracts have biological activity in terms of stimulating the growth of C. vulgaris. We have observed the effects of short-term and long-term growth stimulation. Thus, truffles can become a promising source of new active ingredients in medicine, agronomy, and different life sciences.

ecology↗

bioRxiv2025

Scalable automated segmentation quantifies mitochondrial proteins and morphology at the nanoscale

Ultrastructural changes of mitochondria are closely associated with metabolic dysfunction, leading to a variety of human disorders. These changes can be visualized by pan-Expansion Microscopy (pan-ExM), a 3D microscopy technique requiring only standard fluorescence microscopes, at a throughput exceeding that of the current gold standard, 3D electron microscopy, by orders of magnitude. However, a lack of tools that enable the characterization and quantification of the observed ultrastructural features in the acquired 3D datasets at a comparable throughput has hindered the widespread adoption of pan-ExM for quantitative imaging. Here, we present an automated deep-learning based segmentation approach that utilizes pan-ExMs power to acquire multi-channel images and uses specific labeling as the annotation for the training of the segmentation network. This molecular annotation reduces the required manual annotation effort for mitochondria substructures to just a few hours when setting up the experiment and thereby provides access to 3D suborganellar morphology of mitochondria at an unprecedented throughput. Our approach, which we term MAPS (Mitochondrial Automated Pan-ExM Segmentation), enables for the first time to quantify mitochondrial ultrastructural morphology at scale. We demonstrate this power by characterizing the 3D mitochondrial morphology at the organelle and sub-organelle level in tens of HeLa cells under different treatments and localizing mitochondrial proteins in the sub-organellar context. To demonstrate our technology in tissue, we compare the ultrastructural morphology of mitochondria in proximal tubules of kidneys of mice exhibiting acute kidney injury (AKI) with those of untreated mice, revealing striking differences in their cristae structure. MAPS can easily be adapted to different cell and tissue types, allowing the analysis of tens of samples per day, and therefore provides a versatile tool for a comprehensive understanding of mitochondrial ultrastructural changes in many disease contexts. Requiring only standard fluorescence microscopes and computer infrastructure, MAPS is readily adoptable by any lab. HighlightsO_LIMAPS utilizes specific labeling to train a segmentation model for 3D super-resolution pan-ExM images C_LIO_LISuborganellar mitochondrial features and their changes in disease models are quantified at high throughput C_LIO_LI3D protein distributions are correlated to ultrastructural features in mitochondria C_LIO_LIRequiring only standard lab infrastructure, MAPS is readily adoptable C_LI

cell biology↗

bioRxiv2024

The first report on the effect of white and black truffle extracts on human semen parameters in vitro

Our experiment aimed to evaluate the influence of extracts of white and black truffle mushrooms on human spermatozoa. The study utilized 28 samples of wild truffle fruiting bodies. In the experiment, we used ejaculate from male volunteers of active reproductive age (N=10, 25-35 years old). During the experiment, we assessed sixteen physiological parameters. Research has shown that extracts from black and white truffles related to Tuber sp. have a stimulating effect on spermatozoa. The average path sperm velocity, curvilinear velocity and beat cross-frequency were increased by 56%, 48% and 50% respectively. Linearity showed a significant increase by 56% and straightness by 48%. This could be useful in the development of drugs to enhance sperm activity and lifespan. Additionally, extracts from black truffles have been found to have negative effects on spermatozoa, which could be relevant for developing new contraceptive drugs. Our study demonstrated, for the first time, the influence of methanol extracts of Tuber sp. mushrooms on male gametes in vitro.

developmental biology↗

bioRxiv2024

Tracking exocytic vesicle movements with MSP-tracker/viewer reveals the spatial control of secretion in epithelial cells.

Understanding how specific secretory cargoes are targeted to distinct domains of the plasma membrane in epithelial cells requires analysing the trafficking of post-Golgi vesicles to their sites of secretion. We used the RUSH (retention using selective hooks) system to synchronously release an apical cargo, Cadherin 99C (Cad99C), and a basolateral cargo, the ECM protein Nidogen, from the endoplasmic reticulum and follow their movements to the plasma membrane. We also developed an interactive vesicle tracking framework, MSP-tracker and viewer, that exploits developments in computer vision and deep learning to determine vesicle trajectories in a noisy environment without the need for extensive training data. MSP-tracker outperformed other tracking software in detecting and tracking post-Golgi vesicles, revealing that Cad99c vesicles predominantly move apically with a mean speed of 1.1{micro}m/sec. This is reduced to 0.85 {micro}m/sec by a dominant slow dynein mutant, demonstrating that dynein transports Cad99C vesicles to the apical cortex. Furthermore, both the dynein mutant and microtubule depolymerisation cause lateral Cad99C secretion. Thus, microtubule organisation plays a central role in targeting apical secretion, suggesting that Drosophila does not have distinct apical versus basolateral vesicle fusion machinery. Nidogen vesicles undergo planar-polarised transport to the leading edge of follicle cells as they migrate over the ECM, whereas most Collagen is secreted at trailing edges. The follicle cells therefore bias secretion of different ECM components to opposite sides of the cell, revealing that the secretory pathway is more spatially organised than previously thought.

cell biology↗

bioRxiv2021

TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data

Single Particle Tracking (SPT) is one of the most widespread techniques to evaluate particle mobility in a variety of situations, such as in cellular and model membrane dynamics. The proposed TRAIT2D Python library is developed to provide object tracking, trajectory analysis and produce simulated datasets with graphical user interface. The tool allows advanced users to customise the analysis to their requirements. Availability and implementation: the software has been coded in Python, and can be accessed from: https://github.com/Eggeling-Lab-Microscope-Software/TRAIT2D, or the pypi and condaforge repositories. A comprehensive user guide is provided at https://eggeling-lab-microscope-software.github.io/TRAIT2D/.

bioinformatics↗