StressRegNet

A chemical-genomics approach to decipher stress response and virulence pathways in infection
Identifying stressor-regulator pairs involved in bacterial stress response, virulence, and antibiotic sensitivity using high-throughput approaches and machine learning.

Pathogens are constantly exposed to numerous environmental cues, which can originate from their host, the microbiome, as well as from food, antibiotics, and other drugs. Pathogens employ diverse strategies to adapt to these continuously changing environments, mostly through transcriptional or post-transcriptional gene-expression control. Besides proteins that act as global stress regulators at the transcriptional level, small regulatory RNAs (sRNAs) are important players that control stress response and virulence at the post-transcriptional level. In addition to regulation of virulence genes or metabolism during host colonization, there is an increasing number of examples where sRNAs can impact antibiotic resistance and tolerance. However, the external cues that trigger many molecular pathways and regulators are still largely elusive, as well as how these regulatory cascades impact bacterial virulence and sensitivity to antibiotics.

Using high-throughput approaches, our StressRegNet consortium aims to explore, which chemical signals (stressors) trigger pathways responsible for controlling bacterial adaptation to the host and to antibiotics in the two major human pathogens Salmonella and Campylobacter. Identifying such stressors will help unravel the extent of cross-talk (epistasis) between different sensing and adaptation mechanisms in bacteria, and expose unknown bacterial “Achilles heels”, such as virulence or antibiotic sensitivity pathways, as targets for novel therapeutic intervention.

Strategy and conditions

In our StressRegNet project, we will combine bacterial genetics, high-throughput screening, and machine learning approaches to obtain a general picture of chemical stimuli that trigger bacterial stress responses mediated by sRNAs and/or global regulators. To this end, we will establish a transcriptional reporter library of stress-related regulatory sRNAs in Salmonella and Campylobacter, and profile their activity upon exposure to >3,000 host-related small molecules. Subsequently, we will develop machine-learning techniques to decipher the implications of these pathways for bacterial sensitivity to antimicrobials. The interdisciplinary approach of our StressRegNet consortium enables this unique chemical genomics approach, as each of the three project partners contribute crucial complementary expertise and essential technology. The strong interactions between wet-lab scientists and mathematicians will advance infection biology research through digitalization.

Aims of the research project

The goal of our project is to identify stressors and bacterial regulatory pathways that control host adaptation and antibiotic sensitivity. By using a high-throughput chemical genomics approach, we will explore chemical stimuli from the host environment that induce stress response pathways in Salmonella and Campylobacter, and elucidate the underlying molecular crosstalk between sensory pathways of these microbes. We aim to generate predictive knowledge of bacterial responses to antibiotics, and of virulence determinants, thereby offering a significant contribution to the development of novel antimicrobial strategies. For example, we aim to identify stressors that affect expression of bacterial efflux pumps. Efflux pumps are activated by several stress pathways, and their activation/repression has direct consequences for virulence and antibiotic resistance. Interfering with their regulation could improve efficacy of antibiotics treatment.

Expected benefits for society

While antibiotics have been powerful tools to treat infectious diseases, their efficiency is seriously threatened by rising antibiotic resistances. A growing list of bacterial pathogens has acquired or developed new resistances, sometimes even multiple or against last resort antibiotics, making them harder, and sometimes impossible, to treat. This includes the intestinal pathogens Salmonella and Campylobacter, both of which were recently classified by the WHO with high priority for research and development of new antibiotics.

Based on a unique chemical-genomics approach, we will profile the molecular adaptation of these pathogens to host-derived and antibiotic stimuli. The systematic assessment of how environmental cues impact antibiotic activity and virulence pathways will be a groundbreaking step towards exploring the potential of host-related metabolites as antibiotic adjuvants to fight infections. Moreover, we believe that the unique combination of methods and expertise employed and developed with this project can be extended to other pathogens, enabling a strategic approach to address the rising threat of antibiotic resistance to global health.

Team

Prof. Dr. Cynthia M. Sharma
Project Management

Julius-Maximilians-Universität Würzburg
Institut für Molekulare Infektionsbiologie

Dr. Ana Rita Brochado
Project Management

Julius-Maximilians-Universität Würzburg
Biozentrum / Zentrum für Infektionsforschung

Prof. Dr. Christian L. Müller
Project Management

Ludwig-Maximilians-Universität München
Fakultät für Mathematik, Informatik und Statistik

Julius-Maximilians-Universität Würzburg
Medizinische Fakultät
Institut für Molekulare Infektionsbiologie

Julius-Maximilians-Universität Würzburg
Biozentrum / Zentrum für Infektionsforschung

Ludwig-Maximilians-Universität München
Fakultät für Mathematik, Informatik und Statistik

Cooperations

CoreUnit Systems Medicine (CU SysMed), Würzburg.

Helmholtz Institute for RNA-based Infection Research (HIRI), Würzburg.

Vertis Biotechnology AG, Freising.

Publications

Alzheimer M, Svensson SL, König F, Schweinlin M, Metzger M, Walles H, Sharma CM (2020). A three-dimensional intestinal tissue model reveals factors and small regulatory RNAs important for colonization with Campylobacter jejuni. PLoS Pathogens, 16(2): e1008304.

Domenech A, Brochado AR, Sender V, Hentrich K, Henriques-Normark B, Typas A and Veening JW (2020) Proton Motive Force Disruptors Block Bacterial Competence and Horizontal Gene Transfer. Cell Host Microbe 8;27(4):544-555.

Yoon G, Gaynanova I and Müller CL (2019). Microbial networks in SPRING – Semi-parametric rank-based correlation and partial correlation estimation for quantitative microbiome data. Frontiers in Genetics. 10:1–25.

Brochado AR, Telzerow R, Bobonis J, Banzhaf M, Mateus A, Selkrig J, Huth E,Bassler S, Zamarreño J, Zietek M, Ng N, Foerster S, Ezraty B, Py B, Barras F, Savitski MM, Bork P, Göttig S, Typas A (2018) Species-specific activity of antibacterial drug combinations Nature, 559:259–263.

Dugar G, Leenay RT, Eisenbart SK, Bischler T, Aul BU, Beisel CL, Sharma CM (2018) CRISPR RNA-dependent binding and cleavage of endogenous RNAs by the Campylobacter jejuni Cas9. Molecular Cell, 69(5):893-905.e7.

Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A, Anderson EE, Brochado AR, Fernandez KC, Dose H, Mori H, Patil KR, Bork P, Typas A (2018). Extensive impact of non-antibiotic drugs on human gut bacteria. Nature, 555:634-628.

Tipton L*, Müller CL*, Kurtz ZD, Morris A, Huang L, Kleerup E, Bonneau R, Ghedin E# (2018). Fungi Stabilize Connectivity in Lung and Skin Microbial Ecosystems. Microbiome, 6:12.
Ruiz VE, Battaglia T, Kurtz ZD, Bijnens L, Ou A, Engstrand I, Zheng X, Iizumi T, Mullins BJ,

Müller CL, Cadwell K, Bonneau R, Perez-Perez GI, Blaser MJ# (2017). A single early-in-life antibiotic course has long-lasting effects on microbial network topology and host immunity. Nature Comm., 8(1)-518.

Mahana D, Kurtz ZD , Bokulich NA, Trent CM, Battaglia T, Chung J, Müller CL, Li H, Bonneau R, and Blaser MJ# (2016). Antibiotic perturbation of the murine gut microbiome enhances the adiposity, insulin resistance, and liver disease associated with high-fat diet. Genome Medicine, 8:48.

Kurtz ZD*, Müller CL*, Miraldi ER*, Littman DR, Blaser MJ, Bonneau RA# (2015). Sparse and compositionally robust inference of microbial ecological networks. PLoS Computational Biology, DOI:10.1371/journal.pcbi.1004226.

Brochado AR and Typas A (2013) High-throughput approaches to understanding gene function and mapping network architecture in bacteria. Current Opinion in Microbiology, 16(2) p.199-206.

Dugar G, Herbig A, Förstner KU, Heidrich N, Reinhardt R, Nieselt K, Sharma CM (2013) High-resolution transcriptome maps reveal strain-specific regulatory features of multiple Campylobacter jejuni isolates. PLoS Genetics 9(5):e1003495.

Ezraty B, Vergnes A, Banzhaf M, Duverger Y, Huguenot A, Brochado AR, Su SY, Espinosa L, Loiseau L, Py B, Typas A, Barras F (2013). Fe-S Cluster Biosynthesis Controls Uptake of Aminoglycosides in a ROS-Less Death Pathway. Science, 340(6140) p.1583-1587.

Brochado AR, Andrejev S, Maranas CD and Patil KR (2012). Impact of Stoichiometry Representation on Simulation of Genotype-Phenotype Relationships in Metabolic Networks. PLoS Computational Biology. DOI: 10.1371/journal.pcbi.1002758.