Biology subjects

Grekova, A.

Publications and source records attributed to Grekova, A..

3 recordsLinked to original sources

bioRxiv2023

Genome-scale metabolic model of Staphylococcus epidermidis ATCC 12228 matches in vitro conditions

Staphylococcus epidermidis, a commensal bacterium inhabiting collagen-rich areas, like human skin, has gained significance due to its probiotic potential in the nasal microbiome and as a leading cause of nosocomial infections. While infrequently leading to severe illnesses, S. epidermidis exerts a significant influence, particularly in its close association with implant-related infections and its role as a classic opportunistic biofilm former. Understanding its opportunistic nature is crucial for developing novel therapeutic strategies, addressing both its beneficial and pathogenic aspects, and alleviating the burdens it imposes on patients and healthcare systems. Here, we employ genome-scale metabolic modeling as a powerful tool to elucidate the lifestyle and capabilities of S. epidermidis. We created a comprehensive computational resource for understanding the organisms growth conditions within diverse habitats by reconstructing and analyzing a manually curated and experimentally validated metabolic model. The final network, iSep23, incorporates 1,415 reactions, 1,051 metabolites, and 705 genes, adhering to established community standards and modeling guidelines. Benchmarking with the MEMOTE test suite yields a high score, highlighting the models high semantic quality. Following the FAIR data principles, iSep23 becomes a valuable and publicly accessible asset for subsequent studies. Growth simulations and carbon source utilization predictions align with experimental results, showcasing the models predictive power. This metabolic model advances our understanding of S. epidermidis as a commensal and potential probiotic and enhances insights into its opportunistic pathogenicity against other microorganisms. Author summaryStaphylococcus epidermidis, a bacterium commonly found on human skin, has shown probiotic effects in the nasal microbiome and is a notable causative agent of hospital-acquired infections. While typically causing non-life-threatening diseases, the economic ramifications of S. epidermidis infections are substantial, with annual costs reaching billions of dollars in the United States. To unravel its opportunistic nature, we utilized genome-scale metabolic modeling, creating a detailed mathematical network that elucidates S. epidermidiss lifestyle and capabilities. This model, encompassing over a thousand reactions, metabolites, and genes, adheres rigorously to established standards and guidelines, evident in its commendable benchmarking scores. Adhering to the FAIR data principles (Findable, Accessible, Interoperable, and Reusable), the model stands as a valuable resource for subsequent investigations. Growth simulations and predictions align closely with experimental results, showcasing the models predictive accuracy. This metabolic model not only enhances our understanding of S. epidermidis as a skin commensal and potential probiotic but also sheds light on its opportunistic pathogenicity, particularly in competition with other microorganisms.

systems biology↗

bioRxiv2022

TMvisDB: resource for transmembrane protein annotation and 3D visualization

Since the rise of cellular organisms, transmembrane proteins (TMPs) have been crucial to a variety of cellular processes due to their central role as gates and gatekeepers. Despite their importance, experimental high-resolution structures for TMPs remain underrepresented due to technical limitations. With structure prediction methods coming of age, predictions might fill some of the need. However, identifying the membrane regions and topology in three-dimensional structure files requires additional in silico prediction. Here, we introduce TMvisDB to sieve through millions of predicted structures for TMPs. This resource enables both, to browse through 46 million predicted TMPs and to visualize those along with their topological annotations. The database was created by joining AlphaFold DB structure predictions and transmembrane topology predictions from the protein language model based method TMbed. We show the utility of TMvisDB for individual proteins through two single use cases, namely the B-lymphocyte antigen CD20 (Homo sapiens) and the cellulose synthase (Novosphingobium sp. P6W). To demonstrate the value for large scale analyses, we focus on all TMPs predicted for the human proteome. TMvisDB is freely available at tmvis.predictprotein.org.

bioinformatics↗

bioRxiv2022

Multi-Omics Regulatory Network Inference in the Presence of Missing Data

A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guIded Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent. Juan Henao is a 3rd year PhD candidate at Computational Health Center at Helmholtz Center Munich working on multi-omics and clinical data integration using both, bulk and single-cell data. Michael Lauber is a PhD Candidate at the Chair of Experimental Bioinformatics at the Technical University Munich. Currently, he is working on an approach for inference of reprogramming transcription factors for trans-differentiation. Manuel Azevedo is a Masters student at the Technical University of Munich in Mathematics with a focus on Biomathematics and Biostatistics. Currently, he is working as a Student Assistant at Helmholtz Munich, where he is also doing his masters thesis. Anastasiia Grekova is a Masters student of bioinformatics at the Technical University of Munich and the Ludwig-Maximilians-University Munich, working on multi-omics data integration in Marsico Lab at HMGU. Fabian Theis is the Head of the Institute of Computational Biology and leading the group for Machine Learning at Helmholtz Center Munich. He also holds the chair of Mathematical modelling of biological systems, Department of Mathematics, Technical University of Munich as an Associate Professor. Markus List obtained his PhD at the University of Southern Denmark and worked as a postdoctoral fellow at the Max Planck Institute for Informatics before starting his group Big Data in BioMedicine at the Technical University of Munich. Christoph Ogris holds a PostDoc position in the Marsico Lab at Helmholtz-Center Munich. His research focuses on predicting and exploiting multi-modal biological networks to identify disease-specific cross-omic interactions. Benjamin Schubert obtained his PhD at the University of Tubingen and worked as a postdoctoral fellow at Harvard Medical School and Dana-Farber Cancer Institute USA before starting his group for Translational Immmunomics at the Helmholtz Center Munich.

systems biology↗