Biology subjects

Panina, Y.

Publications and source records attributed to Panina, Y..

6 recordsLinked to original sources

bioRxiv2022

Biopriming of broad bean seeds with entomopathogenic fungus Metarhizium robertsii does not affect invertebrate communities of the agroecosystem

Biopriming, or treatment of seeds with beneficial microorganisms such as beneficial fungi, can be a promising strategy in agricultural cultivation. However, the effects of such treatment on non-target organisms living in the soil and on plants have not been sufficiently studied, and it is not known very well whether such treatment would alter invertebrate communities (e.g., harm them). Here, we addressed the effect of treating broad bean seeds (Vicia faba L.) with the conidia of entomopathogenic ascomycete Metarhizium robertsii on the diversity and abundance of invertebrate communities in the agroecosystem in the south part of West Siberia in 2019 and 2020. We have analyzed the effect both on the general invertebrate communities as well as on the main pests of beans. In the case of bean pests, we assessed the rate of plant infestation by aphids (Hemiptera: Aphididae) and the degree of leaf damage by leafminer flies Liriomyza bryoniae (Diptera: Agromyzidae). In most cases, the treatment did not lead to significant changes in the total abundance of the soil invertebrates and herbivores or the abundance of predominant taxa (Coleoptera: Carabidae, Staphylinidae, Elateridae, Scarabaeidae, Curculionidae; Hemiptera: Miridae, Cicadellidae, Aphididae; larvae of Diptera). A positive effect of treatment on population density of the soil mesofauna was noted for Diptera larvae in June 2019. Regarding aphids and leafminer flies, no significant effect was observed in terms of the proportion of plants with aphids and the density of aphid colonies on individual plants throughout the season, and no significant influence was found on the proportion of plant leaves damaged by leafminer fly Liriomyza bryoniae larvae. In summary, in Western Siberia, the treatment of broad bean seeds with M. robertsii did not significantly affect non-target arthropods common for bean fields as well as the main pests of beans, namely aphids and miner flies.

ecology↗

bioRxiv2020

Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

High dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods. One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

bioinformatics↗

bioRxiv2020

Integrated Collection of Stem Cell Bank data, a data portal for standardized stem cell information

The last decade has witnessed an extremely rapid increase in the number of newly established stem cell lines. However, due to the lack of a standardized format, data exchange among stem cell line resources has been challenging, and no system can search all stem cell lines across resources worldwide. To solve this problem, we have developed Integrated Collection of Stem Cell Bank data (ICSCB) (http://icscb.stemcellinformatics.org/), the largest database search portal for stem cell line information, based on the standardized data items and terms of the MIACARM framework. Currently, ICSCB can retrieve >16,000 cell lines from four major data resources in Europe, Japan, and the United States. ICSCB is automatically updated to provide the latest cell line information, and its integrative search helps users collect cell line information for over 1,000 diseases including many rare diseases worldwide, which has been a formidable task, thereby distinguishing itself from other database search portals.

bioinformatics↗

bioRxiv2020

Cell Pluripotency and Its Effect on Sensitivity to Chemical Exposure

Pluripotent stem cells (PSCs) possess unique characteristics that distinguish them from other cell types. Human embryonic stem (ES) cells are recently gaining attention as a powerful tool for human toxicity assessment without the use of experimental animals, and an embryonic stem cell test (EST) was introduced for this purpose. However, human PSCs have not been thoroughly investigated in terms of drug resistance or compared with other cell types or cell states, such as naive state, to date. Aiming to close this gap in research knowledge, we assessed and compared several human PSC lines for their resistance to drug exposure. Firstly, we report that RIKEN-2A human induced pluripotent stem (iPS) cells possessed approximately the same sensitivity to selected drugs as KhES-3 human ES cells. Secondly, both ES and iPS cells were several times less resistant to drug exposure than other non-pluripotent cell types. Finally, we showed that iPS cells subjected to naive-state induction procedures exhibited a sharp increase in drug sensitivity. Upon passage of these naive-like cells in non-naive PSC culture medium, their sensitivity to drug exposure decreased. We thus revealed differences in sensitivity to drug exposure among different types or states of PSCs and, importantly, indicated that naive-state induction could increase this sensitivity.

pharmacology and toxicology↗

bioRxiv2020

Following embryonic stem cells, their differentiated progeny, and cell-state changes during iPS reprogramming by Raman spectroscopy

To monitor cell state transition in pluripotent cells is invaluable for application and basic research. In this study, we demonstrate the pertinence of use non-invasive, label-free Raman spectroscopy to monitor and characterize the cell state transition of mouse stem cells undergoing reprogramming. Using an isogenic cell line of mouse stem cells, reprogramming from neuronal cells was performed, and we showcase a comparative analysis of single cell spectral data of the original stem cells, their neuronal progenitors, and reprogrammed cells. Neural network, regression models, and ratiometric analysis were used to discriminate the cell states and extract several important biomarkers specific to differentiation or reprogramming. Our results indicated that the Raman spectrum allowed to build a low dimensional space allowing to monitor and characterize the dynamics of cell state transition at a single cell level, scattered in heterogeneous populations. Ability of monitoring pluripotency by Raman spectroscopy, and distinguish differences between ES and reprogrammed cells is also discussed.

systems biology↗

bioRxiv2018

Pairwise Efficiency: A new mathematical approach to qPCR data analysis increases the precision of the classical calibration curve assay

The real-time quantitative polymerase chain reaction (qPCR) is routinely used for quantification of nucleic acids and is considered the gold standard in the field of relative nucleic acid measurements. The efficiency of the qPCR reaction is one of the most important parameters that needs to be determined, reported, and incorporated into data analysis in any qPCR experiment. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines recognize the calibration curve as the method of choice for estimation of qPCR efficiency. The precision of this method has been reported to be between SD=0.007 (3 replicates) and SD=0.022 (no replicates). In this manuscript we present a novel approach to analysing qPCR data obtained by running a dilution series. Unlike previously developed methods, our method relies on a new formula that describes pairwise relationships between data points on separate amplification curves and thus operates extensive statistics (hundreds of estimations). The comparison of our method with classical calibration curve by Monte Carlo simulation shows that our approach can almost double the precision of efficiency and gene expression ratio estimations on the same dataset.

biochemistry↗