DynamicKit

Finding new combination therapies against multi-resistant tuberculosis through a new proteomics technology and artificial intelligence
Artificial intelligence-assisted translation of a new bioassay to decipher the dynamic mode of action of tuberculosis-active antibiotics to develop new combination therapies for multidrug-resistant and dormant tuberculosis.

Tuberculosis (TB) is the deadliest infectious disease in humans and claims around 1.5 million lives worldwide every year. To successfully treat this lung disease, a mix of different drugs has to be administered for several months. This however, is problematic as the bacterial pathogens become resistant, and even in treatable bacterial populations, highly resistant subpopulations can be detected. In order to prevent the spread of the disease, it is therefore not only necessary to develop new antibiotics, but also to constantly find new combinations of active substances. Such combinations can so far only be identified empirically in expensive clinical studies. New digital tools in combination with novel analytical tools, such as artificial intelligence self-learning algorithms, have the potential to decipher the interplay of different antibiotics on mycobacterial metabolism in a faster, more cost efficient way, making it possible to identify suitable drug cocktails to overcome TB drug-resistance and improve current treatment regimens.

Multi-drug-resistance (MDR) is a dramatic challenge for the most deadly infectious disease of the world, tuberculosis (TB). In our project, we use self-learning algorithms to understand the interaction of different drugs in their effect on the metabolism of mycobacteria, the causative agents of tuberculosis. This way, we are not only able to predict new suitable drug combinations for tuberculosis treatment, but also determine biological molecules that reflect resistance mechanisms, so that we can find out how we can specifically reverse this with drugs. This combined approach yields an urgently needed preclinical laboratory model that will enable us to stop the further spread of the disease.

Strategy and conditions

The identification of new drug-combinations in TB is very difficult as appropriate preclinical models to predict synergistic effects are missing, so that is the unmet need we plan on addressing.

First, we will study the action of common antimycobacterials. A new experimental technique developed at the LMU allows us to describe their modes of action, escape mechanisms and adaptive reactions over time in unprecedented detail. This will enable us to characterize the effect of different antibiotics both individually and in combination.

This analysis will be extended thanks to artificial intelligence and systems biology. The obtained dynamic data will be modelled to gain a better understanding of the pathogen and ultimately highlight novel drug targets. Neural-networks and random forests can be used to perform in silico screens of untested drug combinations, predicting their impact.

Aims of the research project

Our main goal is to develop fundamentally new approaches against resistant as well as susceptible tuberculosis leveraging the potential of new experimental methods and artificial intelligence.

In this way, we hope to find out which active ingredients are an ideal match to be used as combination treatment for tuberculosis. This could provide less toxic and shorter treatment regimens. Furthermore, we aim to identify new drug combinations that may efficiently battle drug-resistant TB.

Expected benefits for society

According to the WHO, antimicrobial resistances such as the ones we find in tuberculosis currently pose the greatest long-term threat to human health and wellbeing. This project builds on novel, data science approaches within basic research to address and counteract the development and spread of resistance within this infectious disease.

Team

PD Dr. Andreas Wieser research group is spearheading a new proteomic technology, which for the first time enables to accurately measure newly formed proteins over time in Mycobacteria, the causative agents of tuberculosis. Prof. Dr. Michael Hoelscher contributes with his world leading expertise in infectious diseases and experience in coordinating drug trials. Through his work group we have access to novel substances and data on clinical correlates of diseases and treatment. Prof. Dr. Dr. Fabian Theis and Dr. Michael Menden are driving computational analyses with artificial intelligence.

Cooperations

With their interdisciplinary basic research, the research team combines expertise from fields such as bioinformatics, artificial intelligence and machine learning to understand cellular processes (F. Theis / M. Menden), analytical chemistry, medical microbiology (A. Wieser) and tropical medicine, including therapy in tuberculosis/clinical trials (M.Hoelscher). This project will strongly benefit from the scientific network provided by BayResQ.net as well.

PD Dr. Andreas Wieser
Project Management

Ludwig-Maximilians-Universität München
Max von Pettenkofer Institut

Prof. Dr. Dr. Fabian Theis
Project Management

Technische Universität München
Institut für Computational Biology
Helmholtz Zentrum München

Prof. Dr. med. Michael Hoelscher
Project Management

Ludwig-Maximilians-Universität München
Department of Infectious Diseases and and Tropical Medicine
Medical Faculty

Dr. Michael Menden
Project Management

Ludwig-Maximilians-Universität München
Institut für Computational Biology
Helmholtz Zentrum München

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

Ludwig-Maximilians-Universität München
Max von Pettenkofer Institut

Technische Universität München
Helmholtz Zentrum München

Ludwig-Maximilians-Universität München
Department of Infectious Diseases and and Tropical Medicine
Medical Faculty

Technische Universität München
Institut für Computational Biology