Metabodefense

The host metabolism as antibacterial effector
Artificial intelligence to identify new antimicrobial metabolites in macrophages to combat multi-resistant bacteria

Products of the body’s own metabolism not only have a regulatory effect on the immune system, but can also influence the growth or persistence of bacteria. The contribution of host metabolites in antimicrobial defence is still largely unexplored. We postulate that a targeted modulation of the host metabolism can inhibit pathogen growth and develop an antimicrobial efficacy against persisters. We will investigate this paradigmatically with a Salmonella infection model. We will use methods of bioinformatics and machine learning to identify new antimicrobially effective target structures anchored in the metabolism from highly complex metabolome and transcriptome data. In the Jantsch-Lab macrophages will be infected with Salmonella and different signalling pathways of the macrophages will be disturbed. In the Dettmer-Lab, genome-wide gene expression analyses (in collaboration with the Genomics Core Unit of the University of Regensburg), comprehensive metabolome analyses as well as targeted quantitative metabolite and metabolic tracer analyses are performed on samples obtained from infected macrophages. The Spang group develops predictive models of pathogen control, network modelling of host-pathogen interaction and causal models for the identification of putative antimicrobial target structures. The new candidates are then validated in vitro and in vivo in the Jantsch-Lab and Dettmer-Lab. This will provide the basis for new approaches to host-based therapy of multi-resistant pathogens.

Multi-resistant pathogens do not infect everyone they affect. Some people have high-end macrophages that control and fend off the infection, while other people’s macrophages cannot. We suspect the difference between these scavenger cells of the immune system in their metabolism. As a rule, nobody knows how fit his macrophages will be in the event of an infection. Therefore, it is important to better understand the characteristic properties of potent macrophages and then make these properties diagnosable. In this way, high-risk patients for infections could be identified early on. Furthermore, the metabolism of macrophages can be influenced in different ways not only by a variety of drugs, but also by an inflammatory reaction (metaflammation) caused by excessive food intake and triggered by metabolic processes. We do not yet know how these factors affect the fitness of macrophages to defend themselves against pathogens.

Strategy and conditions

In this project, we rely on a strategy that combines modern metabolic analysis and artificial intelligence methods with experimental infection immunology. All three areas have made great progress in recent years and we see the opportunity to make rapid progress in their networking. Therefore, research teams from all three areas are working together in our project. The Jantsch-Lab has set itself the goal of investigating the interplay between infection defence and immune metabolism and is responsible for the experimental work on infected macrophages. The Dettmer-Lab is well established in the field of metabolomics, i.e. metabolic analysis, and generates in our project high-dimensional measurement data on macrophage metabolism using modern mass spectrometers. In these data sets, the Spang group uses artificial intelligence methods to search for data patterns that can be used for diagnostics or the detection of therapeutic target structures. The group has been developing such algorithms for many years and applies them in clinical contexts.

Aims of the research project

Our goal is to establish the scientific basis for an approach focused on macrophage metabolism for the diagnosis, prevention and therapy of infections caused by multi-resistant pathogens. We therefore study the metabolism of infected macrophages and investigate their ability to control the ingested infectious agents. Using artificial intelligence methods, we aim to identify patterns in the metabolism that characterize high-end macrophages in particular. In our infection experiments we also intervene experimentally in the metabolism in order to mimic the potential effect of drugs. In addition, we use intelligent algorithms of causal inference to understand how specific therapeutic interventions in macrophage metabolism alter its antimicrobial properties. In this way, we want to learn how to reprogram ordinary macrophages into high-end macrophages.

Macrophages (red) phagocytose bacteria (green)

Expected benefits for society

From the knowledge thus gained, new diagnostic and also immunotherapeutic approaches to combat multi-resistant germs can be derived. The classical antibiotic is to be supplemented by a new therapeutic principle. Instead of killing the germ directly, we strengthen the immune system in controlling the infection. The AI and medical research location Bavaria offers us ideal starting conditions for implementing this project. A strategy for fighting infections that arises from this approach could also be pursued within the framework of a spin-off company. However, the results could also strengthen our healthcare systems far beyond Bavaria and open up new therapeutic options for patients.

Team

The Jantsch-Lab is active in the field of infection immunology. One of the main focuses of research is the role of the immune metabolism in the infection defence mediated by cells of the innate immune system.

The Dettmer-Lab focuses on the field of comprehensive qualitative and quantitative metabolic analysis using coupled mass spectrometric methods (metabolomics).

The Spang group focuses on bioinformatics and machine learning. This includes the predictive modeling of high-dimensional molecular data, the development of new statistical/algorithmic methods for the analysis of highly complex data sets and the modeling of biological networks and processes of causal discovery.

Cooperations

Genomics Core Unit, Universität Regensburg

Prof. Dr. Michael Hensel, Universität Osnabrück

Prof. Dr. Dirk Bumann, Biozentrum Basel

Prof. Dr. Jonathan Jantsch
Project Management

Universität Regensburg
Institut für Medizinische Mikrobiologie & Hygiene

PD Dr. Katja Dettmer-Wilde
Project Management

Universität Regensburg
Institut für Funktionelle Genomik

Prof. Dr. Rainer Spang
Project Management

Universität Regensburg
Institut für Funktionelle Genomik

Publications
  • Antibiotikaresistenzen: Mit Grundlagenforschung und Datenvernetzung gegen die globale Herausforderung
    Kaltenhauser U, Hauser A
    Biotechnologie in Bayern 2022; München, bioM
  • Identification of Antimotilins, Novel Inhibitors of Helicobacter pylori Flagellar Motility That Inhibit Stomach Colonization in a Mouse Model
    Suerbaum S, Coombs N, Patel L, Pscheniza D, Rox K, Falk C, Gruber AD, Kershaw O, Chhatwal P, Brönstrup M, Bilitewski U, Josenhans C
    mbio 2022; 13(2): e0375521
  • Efficacy of Vancomycin and Meropenem in Central Nervous System Infections in Children and Adults: Current Update
    Schneider F, Gessner A, El-Najjar N
    Antibiotics (Basel) 2022; 11(2): 173
  • On microbial syringes: Advances in our understanding of type III secretion systems in bacterial pathogenesis
    Hornef MW, Jantsch J
    Phys Life Rev 2021; 39: 96-98
  • High Na(+) Environments Impair Phagocyte Oxidase-Dependent Antibacterial Activity of Neutrophils
    Krampert L, Bauer K, Ebner S, Neubert P, Ossner T, Weigert A, Schatz V, Toelge M, Schroder A, Herrmann M, Schnare M, Dorhoi A, Jantsch J
    Front Immunol 2021; 12: 712948
  • Sfaira accelerates data and model reuse in single cell genomics
    Fischer DS, Dony L, König M, Moeed A, Zappia L, Heumos L, Tritschler S, Holmberg O, Aliee H, Theis FJ
    Genome Biol 2021; 22(1): 248
  • Salt Transiently Inhibits Mitochondrial Energetics in Mononuclear Phagocytes
    Geisberger S, Bartolomaeus H, Neubert P, Willebrand R, Zasada C, Bartolomaeus T, McParland V, Swinnen D, Geuzens A, Maifeld A, Krampert L, Vogl M, Mähler A, Wilck N, Marko L, Tilic E, Forslund SK, Binger KJ, Stegbauer J, Dechend R, Kleinewietfeld M, Jantsch J, Kempa S, Müller DN
    Circulation 2021; 144: 144-158
  • Small RNA mediated gradual control of lipopolysaccharide biosynthesis affects antibiotic resistance in Helicobacter pylori
    Pernitzsch SR, Alzheimer M, Bremer BU, Robbe-Saule M, de Reuse H, Sharma CM
    Nature Communications 2021; 12(1): 4433
  • Sodium and its manifold impact on our immune system
    Jobin K, Müller DN, Jantsch J, Kurts C
    Trends Immunol 2021; 42(6): 469-479
  • Inflammasomes in dendritic cells: Friend or foe?
    Hatscher L, Amon L, Heger L, Dudziak D
    Immunol Lett 2021; 234: 16-32
  • Global RNA profiles show target selectivity and physiological effects of peptide-delivered antisense antibiotics
    Popella L, Jung J, Popova K, Durica-Mitić S, Barquist L, Vogel J
    Nucleic Acids Res 2021; 49(8): 4705-4724
  • Select hyperactivating NLRP3 ligands enhance the TH1- and TH17-inducing potential of human type 2 conventional dendritic cells
    Hatscher L, Lehmann CHK, Purbojo A, Onderka C, Liang C, Hartmann A, Cesnjevar R, Bruns H, Gross O, Nimmerjahn F, Ivanović-Burmazović I, Kunz M, Heger L, Dudziak D
    Science Signaling 2021; 14(680): eabe1757
  • Evolved to vary: genome and epigenome variation in the human pathogen Helicobacter pylori
    Ailloud F, Estibariz I und Suerbaum S
    FEMS Microbiol Rev 2021; 45(1): fuaa042
  • A Repeat-Associated Small RNA Controls the Major Virulence Factors of Helicobacter pylori.
    Eisenbart SK, Alzheimer M, Pernitzsch SR, Dietrich S, Stahl S, Sharma CM
    Molecular Cell 2020; 80(2): 210-226.e7
  • Human Fcγ-receptor IIb modulates pathogen-specific versus self-reactive antibody responses in lyme arthritis
    Danzer H, Glaesner J, Baerenwaldt A, Reitinger C, Lux A, Heger L, Dudziak D, Harrer T, Gessner A, Nimmerjahn F
    Elife 2020; 9: e55319
  • Harnessing the Complete Repertoire of Conventional Dendritic Cell Functions for Cancer Immunotherapy
    Amon L, Hatscher L, Heger L, Dudziak D, Lehmann CHK
    Pharmaceutics 2020; 12(7): 663
  • Proton Motive Force Disruptors Block Bacterial Competence and Horizontal Gene Transfer.
    Domenech A, Brochado AR, Sender V, Hentrich K, Henriques-Normark B, Typas A and Veening JW
    Cell Host Microbe 2020; 27(4): 544-555.e3
  • A Novel Rapid Sample Preparation Method for MALDI-TOF MS Permits Borrelia burgdorferi Sensu Lato Species and Isolate Differentiation
    Neumann-Cip AC, Fingerle V, Margos G, Straubinger RK, Overzier E, Ulrich S, Wieser A
    Front Microbiol 2020; 11: 690
  • An RNA biology perspective on species-specific programmable RNA antibiotics
    Vogel, Jörg
    Mol Microbiol 2020; 113(3): 550-559
  • A three-dimensional intestinal tissue model reveals factors and small regulatory RNAs important for colonization with Campylobacter jejuni.
    Alzheimer M, Svensson SL, König F, Schweinlin M, Metzger M, Walles H, Sharma CM
    PLoS Pathogens 2020; 16(2): e1008304
  • Precursors for Nonlymphoid-Tissue Treg Cells Reside in Secondary Lymphoid Organs and Are Programmed by the Transcription Factor BATF.
    Delacher M, Imbusch CD, Hotz-Wagenblatt A, Mallm JP, Bauer K, Simon M, Riegel D, Rendeiro AF, Bittner S, Sanderink L, Pant A, Schmidleithner L, Braband KL, Echtenachter B, Fischer A, Giunchiglia V, Hoffmann P, Edinger M, Bock C, Rehli M, Brors B, Schmidl C, Feuerer M
    Immunity 2020; 52(2): 295-312.e11
  • A decade of advances in transposon-insertion sequencing
    Cain AK, Barquist L, Goodman AL, Paulsen IT, Parkhill J
    Nat Rev Genet 2020; 9: 526-540
  • HIF1A and NFAT5 coordinate Na+-boosted antibacterial defense via enhanced autophagy and autolysosomal targeting
    Neubert P, Weichselbaum A, Reitinger C, Schatz V, Schröder A, Ferdinand JR, Simon M, Bär AL, Brochhausen C, Gerlach RG, Tomiuk S, Hammer K, Wagner S, van Zandbergen G, Binger KJ, Müller DN, Kitada K, Clatworthy MR, Kurts C, Titze J, Abdullah Z, Jantsch J
    Autophagy 2019; 15(11): 1899-1916
  • Deep learning: new computational modelling techniques for genomics
    Eraslan G, Avsec Ž, Gagneur J, Theis FJ
    Nat Rev Genet 2019; 20(7): 389-403
  • Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
    Menden MP, Wang D, Mason MJ, Szalai B, Bulusu KC, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, AstraZeneca-Sanger Drug Combination DREAM Consortium, Jang IS, Ghazoui Z, Ahsen ME, Vogel R, Neto EC, Norman T, Tang EKY, Garnett MJ, Veroli GYD, Fawell S, Stolovitzky G, Guinney J, Dry JR, Saez-Rodriguez J
    Nat Commun 2019; 10(1): 2674
  • Microbial networks in SPRING – Semi-parametric rank-based correlation and partial correlation estimation for quantitative microbiome data
    Yoon G, Gaynanova I, Müller CL
    Frontiers in Genetics 2019; 10: 516
  • Within-host evolution of Helicobacter pylori shaped by niche-specific adaptation, intragastric migrations and selective sweeps
    Ailloud F, Didelot X, Woltemate S, Pfaffinger G, Overmann, J, Bader RC, Schulz C, Malfertheiner P, Suerbaum S
    Nat Commun 2019; 10(1): 2273
  • Rbpj expression in regulatory T cells is critical for restraining TH2 responses
    Delacher M, Schmidl C, Herzig Y, Breloer M, Hartmann W, Brunk F, Kägebein D, Träger U, Hofer AC, Bittner S, Weichenhan D, Imbusch CD, Hotz-Wagenblatt A, Hielscher T, Breiling A, Federico G, Gröne, HJ, Schmid RM, Rehli M, Abramson J, Feuerer M
    Nat Commun 2019; 10(1): 1621
  • Limitation of TCA Cycle Intermediates Represents an Oxygen-Independent Nutritional Antibacterial Effector Mechanism of Macrophages
    Hayek I, Fischer F, Schulze-Luehrmann J, Dettmer K, Sobotta K, Schatz V, Kohl L, Boden K, Lang R, Oefner PJ, Wirtz S, Jantsch J, Lührmann A
    Cell Rep 2019; 26(13): 3502-3510.e6
Associated Institutes

Universität Regensburg
Institut für Medizinische Mikrobiologie & Hygiene

Universität Regensburg
Institut für Funktionelle Genomik