Helicopredict

Genome-based resistance prediction in Helicobacter pylori
Development of a platform for genome-based resistance and virulence prediction in Helicobacter pylori

Helicobacter pylori (H. pylori) infection is one of the most prevalent bacterial infections worldwide. Chronic infection leads to chronic active gastritis and can result in the development of other complications such as ulcers or gastric cancer. Indeed, approximately 90% of all gastric cancers are associated with H. pylori. Failure of standard eradication therapies is rising dramatically due to the increased development of resistant bacterial strains. Since two antibiotics are needed for successful eradication, the usage of only one antibiotic in other indications such as respiratory diseases will render the (mostly yet undetected) H. pylori strain in these patients resistant. Nowadays, it is estimated that already 10-20% of H. pylori strains are multiresistant. However, culture-based resistance testing, which is currently only recommended after unsuccessful second-line therapy, is a lengthy process; in vitro growth of H. pylori takes 5-7 days after isolation from gastric tissue and further resistance tests last between 3 to 5 days. Taking this into account, a rapid method to determine whether an isolated strain will be prone to antibiotic resistance would be a tremendous help to choose the appropriate therapeutic regimen.To address this challenge, we plan to develop an algorithm for prediction of antibiotic resistance, which will primarily be based on H. pylori genome sequencing data that can be obtained quickly. The algorithm will be made publicly available to physicians and serve as a way to select the optimal therapy. This approach will help to optimize therapeutic efficacy and counteract further resistance development.

Strategy and conditions

Although some mutations in genes related to antibiotic resistance clearly correlate with phenotypic resistance, the significance of many mutations is unclear as phenotypic resistance can often not be associated with a single specific mutation. A drawback of most studies in this context is the small sample size, which will be addressed in our project by including data from up to 2000 patients and their respective H. pylori strains. Such a comprehensive approach will enable us to include in our analysis, in addition to genomic and phenotypic data, other factors that can contribute to resistance in vivo. For example, previous antibiotic treatment for a different condition might interfere with a later eradication therapy for H. pylori infection. In addition, the inflammatory response and degree of gastric pathology can eventually influence therapy success. Another important source for the development of resistances are co-inhabiting bacteria in the gastrointestinal tract, since antibiotic resistances can be transferred between different bacterial species by direct plasmid transfer or through common bacteriophages. Hence, the gastric and gut microbiota may also play an important role in the acquisition of antibiotic resistances. Therefore, we will build a database including genotypic and phenotypic bacterial data, host inflammation-associated parameters as well as microbiota signatures. Based on these data we will develop an algorithm for resistance prediction, which will be incorporated into an online platform. Once completed, the online prediction tool will be tested using selected strains for validation, and then opened for public access.

Aims of the research project

The aim of the research consortium is the development of a genotypic resistance testing database for the prediction of antibiotic susceptibility of H. pylori. Based on data from more than 2000 patients, we will build a database including genotypic and phenotypic bacterial data, host inflammation-associated parameters as well as microbiota signatures. We will make genetic resistance testing available based on this database in the sense of a “genotype to phenotype concept”. To this end, we will develop an algorithm for prediction of antibiotic resistance that will be made publicly available to physicians and serve as a way to select the optimal therapy. The algorithm will be based on whole genome sequencing of H. pylori but also include other parameters putatively influencing resistance in the stomach such as local inflammation and the gut microbiome, and use machine learning to continuously improve the accuracy.

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

Prof. Dr. Sebastian Suerbaum:
At the Ludwig-Maximilian-University, metagenomic sequencing is performed, and genomes of all individual H. pylori strains isolated from the study population are reconstructed and analyzed for mutations in resistance-related genes. Mutations are mapped and compared to published databases and literature, and correlated with phenotypic resistance profiles.

Prof. Dr. Markus Gerhard:
Prof. Gerhard’s group will perform microbiome sequencing of stomach and stool samples. After biostatistic analysis and description of the individual micobiome compositions, data are also included into the database for correlation with other parameters.

PD Dr. Christian Schulz:
At the Klinikum der Universität München, endoscopies will be performed. Gastric biopsies are taken for H. pylori culture, histology and stomach microbiome analysis; stool samples are collected for fecal microbiome analysis. All patient-related data are collected and entered into the study database.

Dr. Atefeh Kazeroonian:
The Technical University Munich will develop algorithms for resistance prediction based on mutations/single-nucleotide polymorphisms in the sequenced H. pylori genome and other parameters possibly influencing resistance (e.g., smoking, co-medication, virulence factors, and microbiome signatures). Models will be optimized for prediction, most importantly by carefully selecting only the most informative variables.

Cooperations

DZIF funded study “Helicobacter pyloriPrevalence and Antibiotic Resistance” (HPPreRes): In this study, 20,000 healthy volunteers are screened for H.pylori infection serologically. Positive volunteers will be offered the possibility to receive an upper endoscopy with biopsy sampling. 2.000 H. pylori positive volunteers are enrolled to undergo endoscopy at the Klinikum der Universität München. The samples from these 2.000 volunteers will form the basis for the Helicopredict project. The partners involved in this DZIF trial have already successfully been working together in several other DZIF trials.

Prof. Dr. Sebastian Suerbaum
Project Management

Ludwig-Maximilians-Universität München
Max von Pettenkofer Institut

Prof. Dr. Markus Gerhard
Project Management

PD Dr. Christian Schulz
Project Management

Klinikum der Universität München Großhadern
Medizinische Klinik und Poliklinik II

Dr. Atefeh Kazeroonian
Project Management

Technische Universität München
Institut für Medizinische Mikrobiologie
Immunologie und Hygiene

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
Institut für Medizinische Mikrobiologie
Immunologie und Hygiene

Klinikum der Universität München Großhadern
Medizinische Klinik und Poliklinik II