Biology subjects

Zimmermann-Kogadeeva, M.

Publications and source records attributed to Zimmermann-Kogadeeva, M..

8 recordsLinked to original sources

bioRxiv2026

13C flux ratio analysis with FRAPPPE reveals differences in metabolic fluxes between gut Bacteroidota and Escherichia coli

Gut bacteria shape the metabolism of their host and play an important role in human health. However, systems biology approaches to study their intracellular metabolic fluxes are largely underdeveloped. We present an experimental and computational workflow to quantify metabolic flux ratios in gut bacteria using 13C-labeled nutrient supplementation and a newly developed machine learning-based Flux Ratio Prediction Python PackagE (FRAPPPE). We apply FRAPPPE to investigate central carbon metabolism in two prevalent gut Bacteroidota, Bacteroides uniformis and Phocaeicola vulgatus, in comparison to Escherichia coli. FRAPPPE revealed altered tricarboxylic acid cycle bifurcation in Bacteroidota compared to E. coli under anaerobic conditions. Further, we used FRAPPPE to investigate co-metabolism of nucleosides and carbohydrates by B. uniformis and P. vulgatus. We found distinct species-specific patterns in how nucleosides affected growth and were utilized depending on the co-fed compound. We quantified co-metabolism and showed that the tested nucleosides were mainly contributing to anabolic metabolism closely related to the specific co-fed nucleoside. Together, these findings provide insights into central and nucleoside metabolism of two gut Bacteroidota, and showcase FRAPPPE as a generalizable workflow to investigate metabolic fluxes in gut bacteria.

microbiology↗

bioRxiv2025

In-cell discovery and characterization of a non-canonical bacterial protein translocation-folding complex

Cryo-electron tomography has emerged as powerful technology for in-cell structural biology, and in combination with breakthroughs in protein structure prediction, offers a unique opportunity for illuminating functions of previously uncharacterized macromolecular complexes. Here, we used the genome-reduced bacterium Mycoplasma pneumoniae as a minimal cell model, and determined in-cell maps of an unknown complex located at the cell surface. By combining proteomics, structure prediction, and bioinformatics, we identified the complex to include the conserved Sec-translocon and an extracellular dome-like structure largely formed by three uncharacterized proteins (Mdps). The Mdps show structural homology to a periplasmic ATP-independent foldase, PrsA, with Mdp444 retaining key catalytic residues. Our study provides the first sub-nanometer resolution maps of the complete bacterial Sec-translocation machinery, depicting several conformational states that allow us to suggest a model for translocation initiation, and revealing its coordination with a novel, extracellular, protein-folding system.

molecular biology↗

bioRxiv2025

GEMsembler: cross-tool structural comparison and ensemble modeling improve metabolic model performance

Genome-scale metabolic models (GEMs) are widely used in systems biology to investigate metabolism and predict perturbation responses. Automatic GEM reconstruction tools generate GEMs with different properties and predictive capacities for the same organism. Since different models can excel at different tasks, combining them can increase metabolic network certainty and enhance model performance. Here, we introduce GEMsembler, a Python package designed to compare cross-tool GEMs, track the origin of model features, and build ensemble models containing any subset of the input models. GEMsembler provides comprehensive analysis functionality, including identification and visualisation of biosynthesis pathways, growth assessment and an agreement-based curation workflow. GEMsembler-curated ensemble models built from four Lactiplantibacillus plantarum and Escherichia coli automatically reconstructed models outperform the gold-standard models in auxotrophy and gene essentiality predictions. Optimising Gene-Protein-Reaction (GPR) combinations from ensemble models improves gene essentiality predictions, even in the manually curated gold-standard models. GEMsembler explains model performance by highlighting relevant metabolic pathways and GPRs alternatives, informing experiments to resolve model uncertainty. Thus, GEMsembler facilitates building more accurate and biologically informed metabolic models for systems biology applications.

systems biology↗

bioRxiv2025

Multi-omics profiling of cross-resistance between ceftazidime-avibactam and meropenem identifies common and strain-specific mechanisms in Pseudomonas aeruginosa clinical isolates

Pseudomonas aeruginosa is a highly versatile and resilient pathogen that can infect different tissues and rapidly develop resistance to multiple drugs. Ceftazidime-avibactam (CZA) is an antibiotic often used to treat multidrug resistant infections, however, the knowledge on the CZA resistance mechanisms in P. aeruginosa is limited. Here we performed laboratory evolution of eight clinical isolates of P. aeruginosa exposed to either CZA or meropenem (MEM) in sub-inhibitory concentrations, and used multi-omics profiling to investigate emerging resistance mechanisms. The majority of strains exposed to MEM developed high resistance (83%, 20/24 strains from eight clinical isolates), with only 17% (4/24) acquiring cross-resistance to CZA. The rate of resistance evolution to CZA was substantially lower (21%, 5/24), while 38% (9/24) acquired cross-resistance to MEM. Whole-genome sequencing revealed strain heterogeneity and different evolutionary paths, with three genes mutated in three or more strains: dacB in CZA-treated strains and oprD and ftsI in MEM-treated strains. Transcriptomic and proteomic analysis underlined heterogeneous strain response to antibiotic treatment with few commonly regulated genes and proteins. To identify genes potentially associated with antibiotic resistance, we built a machine learning model that could separate CZA- and MEM-resistant from sensitive strains based on gene expression and protein abundances. To test some of the identified associations, we performed CRISPR/Cas9 genome editing that demonstrated that mutations in dacB, ampD, and to a lesser extent in mexR directly affected CZA resistance. Overall, this study provides novel insights into the strain-specific molecular mechanisms regulating CZA resistance in Pseudomonas aeruginosa. ImportancePseudomonas aeruginosa is one of the most difficult-to-treat pathogens in the hospital, which often acquires resistance to multiple antibiotics. Ceftazidime-avibactam (CZA) is an essential antibiotic used to treat multidrug resistant infections, but its resistance mechanisms are not well understood. Here we investigated the evolution of resistance to CZA and meropenem (MEM) in eight clinical bacterial isolates from patients blood, urine, and sputum. While the rate of resistance evolution to MEM was higher than to CZA, MEM-resistant strains rarely acquired cross-resistance towards CZA. To identify changes at the genome, transcriptome and proteome levels during antibiotic exposure, we performed multi-omics profiling of the evolved strains, and confirmed the effect of several genes on antibiotic resistance with genetic engineering. Altogether, our study provides insights into the molecular response of P. aeruginosa to CZA and MEM and informs therapeutic interventions, suggesting that CZA could still be effective for patients infected with MEM-resistant pathogens.

microbiology↗

bioRxiv2024

Pervasive sublethal effects of agrochemicals as contributing factors to insect decline

Insect biomass is declining across the globe at an alarming rate. Climate change and the widespread use of pesticides have been hypothesized as two underlying drivers. However, the lack of systematic experimental studies across chemicals and species limits our causal understanding of this problem. Here, we employed a chemical library encompassing 1024 different molecules--including insecticides, herbicides, fungicides, and plant growth inhibitors --to investigate how insect populations are affected by varying concentrations of pesticides, focusing on sublethal doses. Using a controlled laboratory pipeline for Drosophila melanogaster, we found that 57% of these chemicals affect the behavior of larvae at sublethal concentrations, and an even higher proportion compromises long-term survivability after acute exposure. Consistent with these results, we observed that exposure to chemicals at doses orders of magnitude below lethality induced widespread phosphorylation changes across the larval proteome. The effects of agrochemicals were amplified when the ambient temperature was increased by four degrees. We also tested the synergistic effects of multiple chemicals at doses found widely in nature and observed fitness-reducing changes in larval developmental time, behavior, and reproduction. Finally, we expanded our investigation to additional fly species, mosquitos, and butterflies and detected similar behavioral alterations triggered by pesticides at sublethal concentrations. Our results provide experimental evidence that strongly suggests sublethal doses of agrochemicals coupled with changes in environmental temperatures are contributing to the global decline in insect populations. We anticipate that our assays can contribute to improving chemical safety assessment, better protect the environment, secure food supplies, and safeguard animal and human health, as well as understand our rapidly changing world.

ecology↗

bioRxiv2023

Consistency across multi-omics layers in a drug-perturbed gut microbial community

Multi-omics analyses are increasingly employed in microbiome studies to obtain a holistic view of molecular changes occurring within microbial communities exposed to different conditions. However, it is not always clear to what extent each omics data type contributes to our understanding of the community dynamics and whether they are concordant with each other. Here we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers, namely 16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics, and metabolomics. Using this controlled setting, we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions. Furthermore, different omics methods complement each other in their ability to capture functional changes in response to the drug perturbations. For example, while nearly all omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control and metabolomics revealed a decrease in polysaccharide uptake, likely caused by Bacteroidota depletion. Taken together, our study provides insights into how multi-omics datasets can be utilised to reveal complex molecular responses to external perturbations in microbial communities.

microbiology↗

bioRxiv2022

Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome

Dietary nutrients, host metabolism, and gut microbiota activity each influence the hosts metabolic phenotype; however, the interplay between these factors remains poorly understood. We employed tissue-resolved metabolomics in gnotobiotic mice carrying a synthetic human gut microbiota and germfree mice in two dietary conditions to develop an intestinal flux model that quantifies diet, host, and bacterial contributions to the levels of 2,700 intestinal metabolites. While diet was the main factor affecting metabolite profiles, we identified 1,117 potential microbial substrates and products in the gut. By integrating metagenomics and metatranscriptomics data into genome-scale enzymatic networks, we linked 202 potential substrate-product pairs by a single enzymatic reaction. We further identified bacterial species and enzymes that can explain the differential abundance of 13% of the identified microbial products between the mouse groups. This quantitative modelling approach paves the way for controlling an individuals metabolic phenotype by modulating their gut microbiome composition and diet.

systems biology↗

bioRxiv2021

Visualizing translation dynamics at atomic detail inside a bacterial cell

Translation is the fundamental process of protein synthesis and is catalysed by the ribosome in all living cells. Here, we use cryo-electron tomography and sub-tomogram analysis to visualize the dynamics of translation inside the prokaryote Mycoplasma pneumoniae. We first obtain an in-cell atomic model for the M. pneumoniae ribosome that reveals distinct extensions of ribosomal proteins. Classification then resolves thirteen ribosome states that differ in conformation and composition and reflect intermediates during translation. Based on these states, we animate translation elongation and demonstrate how antibiotics reshape the translation landscape inside cells. During translation elongation, ribosomes often arrange in a defined manner to form polysomes. By mapping the intracellular three-dimensional organization of translating ribosomes, we show that their association into polysomes exerts a local coordination mechanism that is mediated by the ribosomal protein L9. Our work demonstrates the feasibility of visualizing molecular processes at atomic detail inside cells.

biochemistry↗