• Our new home

    from 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

Sulfoquinovose is a select nutrient of prominent bacteria and a source of hydrogen sulfide in the human gut.

Responses of the microbiota to diet are highly personalized but mechanistically not well understood because many metabolic capabilities and interactions of human gut microorganisms are unknown. Here we show that sulfoquinovose (SQ), a sulfonated monosaccharide omnipresent in green vegetables, is a selective yet relevant substrate for few but ubiquitous bacteria in the human gut. In human feces and in defined co-culture, Eubacterium rectale and Bilophila wadsworthia used recently identified pathways to cooperatively catabolize SQ with 2,3-dihydroxypropane-1-sulfonate as a transient intermediate to hydrogen sulfide (HS), a key intestinal metabolite with disparate effects on host health. SQ-degradation capability is encoded in almost half of E. rectale genomes but otherwise sparsely distributed among microbial species in the human intestine. However, re-analysis of fecal metatranscriptome datasets of four human cohorts showed that SQ degradation (mostly from E. rectale and Faecalibacterium prausnitzii) and HS production (mostly from B. wadsworthia) pathways were expressed abundantly across various health states, demonstrating that these microbial functions are core attributes of the human gut. The discovery of green-diet-derived SQ as an exclusive microbial nutrient and an additional source of HS in the human gut highlights the role of individual dietary compounds and organosulfur metabolism on microbial activity and has implications for precision editing of the gut microbiota by dietary and prebiotic interventions.

Hanson BT, Dimitri Kits K, Löffler J, Burrichter AG, Fiedler A, Denger K, Frommeyer B, Herbold CW, Rattei T, Karcher N, Segata N, Schleheck D, Loy A
2021 - ISME J, in press

Regulation of the Mitochondrion-Fatty Acid Axis for the Metabolic Reprogramming of Chlamydia trachomatis during Treatment with β-Lactam Antimicrobials.

Infection with the obligate intracellular bacterium is the most common bacterial sexually transmitted disease worldwide. Since no vaccine is available to date, antimicrobial therapy is the only alternative in infection. However, changes in chlamydial replicative activity and the occurrence of chlamydial persistence caused by diverse stimuli have been proven to impair treatment effectiveness. Here, we report the mechanism for regulating host signaling processes and mitochondrial function, which can be used for chlamydial metabolic reprogramming during treatment with β-lactam antimicrobials. Activation of signal transducer and activator of transcription 3 (STAT3) is a well-known host response in various bacterial and viral infections. In infection, inactivation of STAT3 by host protein tyrosine phosphatases increased mitochondrial respiration in both the absence and presence of β-lactam antimicrobials. However, during treatment with β-lactam antimicrobials, increased the production of citrate as well as the activity of host ATP-citrate lyase involved in fatty acid synthesis. Concomitantly, chlamydial metabolism switched from the tricarboxylic acid cycle to fatty acid synthesis. This metabolic switch was a unique response in treatment with β-lactam antimicrobials and was not observed in gamma interferon (IFN-γ)-induced persistent infection. Inhibition of fatty acid synthesis was able to attenuate β-lactam-induced chlamydial persistence. Our findings highlight the importance of the mitochondrion-fatty acid interplay for the metabolic reprogramming of during treatment with β-lactam antimicrobials. The mitochondrion generates most of the ATP in eukaryotic cells, and its activity is used for controlling the intracellular growth of Furthermore, mitochondrial activity is tightly connected to host fatty acid synthesis that is indispensable for chlamydial membrane biogenesis. Phospholipids, which are composed of fatty acids, are the central components of the bacterial membrane and play a crucial role in the protection against antimicrobials. Chlamydial persistence that is induced by various stimuli is clinically relevant. While one of the well-recognized inducers, β-lactam antimicrobials, has been used to characterize chlamydial persistence, little is known about the role of mitochondria in persistent infection. Here, we demonstrate how undergoes metabolic reprogramming to switch from the tricarboxylic acid cycle to fatty acid synthesis with promoted host mitochondrial activity in response to treatment with β-lactam antimicrobials.

Shima K, Kaufhold I, Eder T, Käding N, Schmidt N, Ogunsulire IM, Deenen R, Köhrer K, Friedrich D, Isay SE, Grebien F, Klinger M, Richer BC, Günther UL, Deepe GS, Rattei T, Rupp J
2021 - mBio, 2: in press

Learning From Limited Data: Towards Best Practice Techniques for Antimicrobial Resistance Prediction From Whole Genome Sequencing Data.

Antimicrobial resistance prediction from whole genome sequencing data (WGS) is an emerging application of machine learning, promising to improve antimicrobial resistance surveillance and outbreak monitoring. Despite significant reductions in sequencing cost, the availability and sampling diversity of WGS data with matched antimicrobial susceptibility testing (AST) profiles required for training of WGS-AST prediction models remains limited. Best practice machine learning techniques are required to ensure trained models generalize to independent data for optimal predictive performance. Limited data restricts the choice of machine learning training and evaluation methods and can result in overestimation of model performance. We demonstrate that the widely used random k-fold cross-validation method is ill-suited for application to small bacterial genomics datasets and offer an alternative cross-validation method based on genomic distance. We benchmarked three machine learning architectures previously applied to the WGS-AST problem on a set of 8,704 genome assemblies from five clinically relevant pathogens across 77 species-compound combinations collated from public databases. We show that individual models can be effectively ensembled to improve model performance. By combining models stacked generalization with cross-validation, a model ensembling technique suitable for small datasets, we improved average sensitivity and specificity of individual models by 1.77% and 3.20%, respectively. Furthermore, stacked models exhibited improved robustness and were thus less prone to outlier performance drops than individual component models. In this study, we highlight best practice techniques for antimicrobial resistance prediction from WGS data and introduce the combination of genome distance aware cross-validation and stacked generalization for robust and accurate WGS-AST.

Lüftinger L, Májek P, Beisken S, Rattei T, Posch AE
2021 - Front Cell Infect Microbiol, 610348