Biology subjects

Ataman, M.

Publications and source records attributed to Ataman, M..

9 recordsLinked to original sources

bioRxiv2026

Ribosome profiling reveals extensive translational alterations in steatohepatitis-associated hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one the most lethal cancer types. While infections with hepatitis B or hepatitis C viruses remain the primary risk factors for HCC development worldwide, metabolic disease-associated steatohepatitis has become the fastest growing etiology of HCC, particularly in the West. Our study characterizes, for the first time, the remodeling of the translation landscape in steatohepatitis-associated HCC using ribosome profiling and mass spectrometry techniques. In striking contrast with the transcriptional alterations, which do not show strong dependence on associated co-morbidity, translational alterations are broader in the steatohepatitis compared to the viral background. These alterations affect hundreds of genes, whose products are often involved in subcellular protein localization. By quantifying the relationship between the ribosome occupancy of upstream or downstream open reading frames (uORFs or dORFs) and coding regions (CDSs), we provide evidence of uORFs attenuating the translation efficiency of CDSs in HCC. We also identify numerous novel translated regions, including from RNAs currently annotated as non-coding, some yielding peptides that can be reproducibly identified in mass spectrometry datasets. Our study thus provides novel molecular data on HCC with a focus on the steatohepatitis etiology, reveals unexpectedly extensive translational control in this co-morbidity, and gives multiple hints and candidate targets for follow-up experiments.

systems biology↗

bioRxiv2025

in silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity

Understanding how metabolic capabilities diverge across microbial species is fundamental for deciphering community function, ecological interactions, and for guiding synthetic microbiome design. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity poses significant challenges to direct comparison. Here, we introduce NIS, a computational approach that uses the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce GEMs to interpretable modules. NIS enables direct comparison of fueling pathways, biosynthetic routes, and environmental exchange processes across organisms, while preserving key metabolic information. We demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, revealing both conserved and divergent patterns in central metabolism, biomass biosynthesis, and substrate utilization. We further apply NIS to members of the core honeybee gut microbiome, uncovering distinct metabolic traits and complementarity that explain coexistence and interaction potential. Our framework offers a robust and scalable method to dissect microbial metabolic networks and supports the rational design and ecological understanding of microbial communities.

systems biology↗

bioRxiv2025

Identification of new interactors of eIF3f by endogenous proximity-dependent biotin labelling in human muscle cells.

Regulation of protein synthesis is central to maintaining skeletal muscle integrity and its understanding is important for the treatment of muscular and neuromuscular pathologies. The eIF3f subunit of the translation initiation factor eIF3 has a key role, as it stands at the crossroad between protein-synthesis-associated hypertrophy and MAFbx/atrogin-1-dependent. To decipher the molecular mechanisms underpinning the role of eIF3f in regulating muscle mass, we established a cellular model that enables interrogation of eIF3f functionality via identification of proximal interactors. Using CRISPR-Cas9 molecular scissors, we generated single cell clones of immortalised human muscle cells expressing eIF3f fused to the BirA biotin ligase (eIF3f-BioID1 chimera) from the endogenous EIF3F locus. Biotinylated proteins, representing interactors of eIF3f in nanometer range distance, were identified by streptavidin pull-downs and mass spectrometry. In both proliferating and differentiated muscle cells, the eIF3f-BioID1 chimera co-sedimented with ribosomal complexes in polysome profiles and interacted mainly with components of the eIF3 complex, and with the eIF4E, eIF4G, and eIF5 initiation factors. Surprisingly, we identified several nucleus-localised interactors of eIF3f, and the immunofluorescence analyses revealed a previously unknown nuclear localization of eIF3f in both myoblasts and myotubes. We also identified novel cytoplasmic partners of eIF3f, responsible for the maintenance of skeletal muscle ultrastructure (sarcomeric/Z-disc (SYNPO2) bound proteins) and proteins of the lysosomal compartment (LAMP1). The established tagging system should be useful to further advance studies of eIF3f function in hypertrophic and atrophic conditions in skeletal muscle.

molecular biology↗

bioRxiv2023

Ribosomal protein RPL39L is an efficiency factor in the cotranslational folding ofproteins with alpha helical domains

Increasingly many studies reveal how ribosome composition can be tuned to optimally translate the transcriptome of individual cell types. In this study, we investigated the expression pattern, structure within the ribosome and effect on protein synthesis of the ribosomal protein paralog 39L (RPL39L). With a novel mass spectrometric approach we have quantified the expression of RPL39L in human pluripotent cells, cancer cell lines and tissue samples, and in mouse germ cells. We generated RPL39L knock-out mouse embryonic stem cell (mESC) lines and demonstrated that RPL39L impacts the dynamics of translation, to support the pluripotency and differentiation, spontaneous and along the germ cell lineage. Most differences in protein abundance between WT and RPL39L KO lines were explained by widespread proteasomal activity. By CryoEM analysis of purified RPL39 and RPL39L-containing ribosomes we found that, unlike RPL39, RPL39L has two distinct conformations in the exposed segment of the nascent peptide exit tunnel, creating a distinct hydrophobic patch that has been predicted to support the efficient co-translational folding of alpha helices. Our study shows that ribosomal protein paralogs provide switchable modular components that can tune translation to the protein production needs of individual cell types.

molecular biology↗

bioRxiv2022

NICEgame: A workflow for annotating the knowledge gaps in metabolic reconstructions, using known and hypothetical reactions

Advances in medicine and biotechnology rely on the further understanding of biological processes. Despite the increasingly available types and amounts of omics data, significant biochemical knowledge gaps remain uncharacterized. Several approaches have been developed during the past years to identify missing metabolic annotations in genome-scale. However, these approaches suggest missing metabolic reactions within a limited set of already characterized metabolic capabilities. In this study, we introduce NICEgame (Network Integrated Computational Explorer for Gap Annotation of Metabolism), a workflow to characterize missing metabolic capabilities in genome-scale metabolic models using the ATLAS of Biochemistry. NICEgame suggests alternative sets of known and hypothetical reactions to resolve gaps in metabolic networks, assesses their thermodynamic feasibility, and suggests candidate genes and proteins to catalyze the introduced reactions. We use gene essentiality data use to identify metabolic gaps in the latest genome-scale model of Escherichia coli, iML1515. We apply our gap-filling approach and further enhance its genome annotation, by suggesting reactions and putative genes to resolve 46 % of the false negative gene essentiality predictions.

systems biology↗

bioRxiv2021

ZARP: An automated workflow for processing of RNA-seq data

RNA sequencing (RNA-seq) is a crucial technique for many scientific studies and multiple models, and software packages have been developed for the processing and analysis of such data. Given the plethora of available tools, choosing the most appropriate ones is a time-consuming process that requires an in-depth understanding of the data, as well as of the principles and parameters of each tool. In addition, packages designed for individual tasks are developed in different programming languages and have dependencies of various degrees of complexity, which renders their installation and execution challenging for users with limited computational expertise. The use of workflow languages and execution engines with support for virtualization and encapsulation options such as containers and Conda environments facilitates these tasks considerably. Computational workflows defined in those languages can be reliably shared with the scientific community, enhancing reusability, while improving reproducibility of results by making individual analysis steps more transparent. Here we present ZARP, a general purpose RNA-seq analysis workflow which builds on state-of-the-art software in the field to facilitate the analysis of RNA-seq data sets. ZARP is developed in the Snakemake workflow language using best software development practices. It can run locally or in a cluster environment, generating extensive reports not only of the data but also of the options utilized. It is built using modern technologies with the ultimate goal to reduce the hands-on time for bioinformaticians and non-expert users. ZARP is available under a permissive Open Source license and open to contributions by the scientific community. Contactmihaela.zavolan@unibas.ch, alexander.kanitz@unibas.ch

bioinformatics↗

bioRxiv2020

Constraint-based metabolic control analysis for rational strain engineering

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolic concentrations, it fails to account for the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework based on MCA, Network Response Analysis (NRA), for the rational genetic strain design that incorporates biologically relevant constraints, as well as genome editing restrictions. The NRA core constraints being similar to the ones of Flux Balance Analysis, allow it to be used for a wide range of optimization criteria and with various physiological constraints. We show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

systems biology↗

bioRxiv2018

Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

Large-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grown Escherichia coli into a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of the E. coli aerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grown E. coli.

systems biology↗

bioRxiv2017

Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia towards sustainable production of the 2nd generation of biofuels, with Methyl Ethyl Ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methyl-butanenitrile for further study. We reconstructed 3679610 novel biosynthetic pathways towards these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18622 were found to be most biologically feasible ones based on thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC=\"FIGDIR/small/209569_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (27K):\norg.highwire.dtl.DTLVardef@15bb5corg.highwire.dtl.DTLVardef@bfc12borg.highwire.dtl.DTLVardef@1230500org.highwire.dtl.DTLVardef@1d74d2_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

synthetic biology↗