• Our new home

    since summer 2021.

  • Our aim is to advance our understanding of biological systems,

    ranging from single species to multi-species systems and ecosystems,

    based on data from large-scale bioanalytical methods.

  • We develop, improve and apply

    computational methods

    for the interpretation of molecular information in biology.

  • We establish and analyse

    quantitative mathematical models.


Latest publications

Genome rearrangements drive evolution of ANK genes in Wolbachia

Genus Wolbachia comprises endosymbionts infecting many arthropods and nematodes; it is a model for studying symbiosis as its members feature numerous, diverse mutualistic and parasitic adaptations to different hosts. In contrast to nematode-infecting Wolbachia, genomes of arthropod-infecting strains contain a high fraction of repetitive elements creating possibilities for multiple recombination events and causing genome rearrangements. The mechanisms and role of these features are still not fully understood. Transposons cover up to 18% of an arthropod-infecting Wolbachia genome and drive numerous genome rearrangements including inversions and segmental amplifications. ANK (ankyrin-repeat domain family) genes are also often found at the breakpoints of rearrangements, while less than 7% of them were found within locally collinear blocks (LCBs). We observed a strong correlation between the number of ANK genes and the genome size as well as significant overrepresentation of transposons adjacent to these genes. We also revealed numerous cases of integration of transposases to the ANK genes affecting the sequences and putative products of the latter. Our results uncover the role of mobile elements in the amplification and diversification of ANK genes. Evolution of arthropod-infecting Wolbachia was accompanied by diverse genome rearrangements driving the evolution of ANK genes important for bacteria-host interactions. This study demonstrates the effectiveness of our LCB-based approach to the Wolbachia genomics and provides a framework for understanding the impact of genome rearrangements on their rapid host adaptation.

Vostokova EV, Dranenko NO, Gelfand MS, Bochkareva OO
2023 - in press

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis.

Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature.
In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics.
Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

Yurtseven A, Buyanova S, Agrawal AA, Bochkareva OO, Kalinina OV
2023 - BMC Microbiol, 23: 404

A predicted CRISPR-mediated symbiosis between uncultivated archaea.

CRISPR-Cas systems defend prokaryotic cells from invasive DNA of viruses, plasmids and other mobile genetic elements. Here, we show using metagenomics, metatranscriptomics and single-cell genomics that CRISPR systems of widespread, uncultivated archaea can also target chromosomal DNA of archaeal episymbionts of the DPANN superphylum. Using meta-omics datasets from Crystal Geyser and Horonobe Underground Research Laboratory, we find that CRISPR spacers of the hosts Candidatus Altiarchaeum crystalense and Ca. A. horonobense, respectively, match putative essential genes in their episymbionts' genomes of the genus Ca. Huberiarchaeum and that some of these spacers are expressed in situ. Metabolic interaction modelling also reveals complementation between host-episymbiont systems, on the basis of which we propose that episymbionts are either parasitic or mutualistic depending on the genotype of the host. By expanding our analysis to 7,012 archaeal genomes, we suggest that CRISPR-Cas targeting of genomes associated with symbiotic archaea evolved independently in various archaeal lineages.

Esser SP, Rahlff J, Zhao W, Predl M, Plewka J, Sures K, Wimmer F, Lee J, Adam PS, McGonigle J, Turzynski V, Banas I, Schwank K, Krupovic M, Bornemann TLV, Figueroa-Gonzalez PA, Jarett J, Rattei T, Amano Y, Blaby IK, Cheng JF, Brazelton WJ, Beisel CL, Woyke T, Zhang Y, Probst AJ
2023 - Nat Microbiol, 9: 1619-1633