Systematic interaction mapping
At INET, we systematically generate molecular interactome maps using our well-established AI-informed and robotics-supported protein-protein interaction pipeline, integrating the latest experimental and theoretical approaches. In general, our high-quality protein-protein interaction maps not only expedite the functional characterization of unknown proteins, including those with potential biotechnological utility, but also enable systems-level investigations of genotype-to-phenotype relationships.
Our molecular interaction projects
Understanding and Preventing Virus-triggered ALS
Supported by the ALS Association, this project investigates how genetic susceptibility and environmental factors may converge in the development of amyotrophic lateral sclerosis (ALS). While genetic risk factors have been extensively studied, the potential contribution of viral infections to disease susceptibility remains poorly understood.
To address this question, we established NeuroViOme, a comprehensive viral ORFeome resource comprising genes from nine neurotropic and neuroinvasive viruses with suspected links to neurodegenerative disease. Using large-scale interactome mapping approaches, we systematically characterized the interactions between viral and human proteins, generating one of the most extensive host–virus interaction resources in the context of ALS research.
A major outcome of the project was the publication of the NeuroViOme resource, which provides the scientific community with a unique platform for studying virus–host interactions in neurodegenerative disease. Building on this foundation, we completed a comprehensive host–virus interactome map and integrated these data with human genetics and systems-level network analyses.
The resulting interactome represents a valuable resource for investigating how viral perturbations may intersect with cellular pathways relevant to neurodegeneration. The corresponding study has been completed and is currently being prepared for publication. Together, these resources provide a foundation for future studies aimed at understanding the interplay between genetic susceptibility, environmental factors, and disease progression in ALS.
Weller, B., Lin, C. W., Rothballer, S., Calderwood, M. A., Falter-Braun, P. & Falter, C. NeuroViOme: a viral orfeome collection for studies of neurodegenerative disease. J. Neurovirol. 32, 11 (2026).
links: Our ALS project Understanding and Preventing Virus-triggered ALS https://doi.org/10.52546/pc.gr.161505
Publication in J. Neurovirol. https://pubmed.ncbi.nlm.nih.gov/41772317/
CLARITY project
Chronic respiratory diseases are non-communicable diseases with a massive societal and economic burden that constitute the third most common cause of death worldwide. Infections by several respiratory viruses and human genetics constitute major risk factors for developing chronic respiratory diseases. The molecular and physiological mechanisms of how these viral infections cause and contribute to non- communicable disease development are unknown thus hampering prevention and therapeutic approaches.
Respiratory syncytial virus (RSV) is a genetically diverse common respiratory virus that infects nearly all infants before the age of 2 years. Strong epidemiological data link respiratory RSV infections to asthma development. We propose a highly integrative approach to understand how RSV infection interacts with genetic predisposition to identify viral and host genetic risk factors, pathways, and mechanisms underlying virus-induced asthma. Specifically, using two national cohorts (Estonian and Spanish), we will identify human genetic risk factors (WP1) and RSV strains (WP2) that contribute to severe bronchiolitis. Subsequently we analyse how RSV perturbs intracellular networks to change the structural and immunological properties of infected cells and thus trigger a pathologic course towards asthma (WP3). We will use knowledge graphs to integrate the generated data with the current biological corpus, and use graph embedding strategies to generate RSV Open Reading Frame (ORF)- specific and global RSV-induced perturbation signatures. Moreover, we will implement deep-learning approaches to identify drug-like molecules able to revert the effects of the RSV-induced perturbations in vitro (WP4). Specific causal mechanisms will be worked out in cell culture models. Both mechanisms and candidate compounds will be validated in patient derived airway organoid models and, when promising, in a controlled human infection model trial (WP5).
CLARITY will impact the understanding, prevention and possibly treatment of virus-triggered asthma pathogenesis. The genetic results will enable development of a genetic risk score for long-term asthma development that enables personalised prevention campaigns, which will be developed jointly with patient groups (WP7). The molecular mechanisms discovered, and the drug-like compounds that revert the perturbation signatures, will enable development of mechanism-targeted drugs. Fundamentally, the mechanisms identified in this specific model for a strong viral contribution to non-communicable disease will likely represent general mechanisms of how viral infections cause onset and development of other non-communicable diseases.
This project has received funding from the European Union's Horizon Europe research and innovation programme grant agreement No 101137201.
links: https://clarity-project.eu/
Excitation-transcription coupling alters activity of nociceptive neurons
Persistent activity of peripheral nociceptive neurons drives central chronification of pain. But what drives peripheral persistent activity is not well understood. In the central nervous system (CNS), neuronal activity translates into persistently increased activity by so-called excitation-transcription (E-T) coupling. Based on our preliminary
data, we aim to address the hypothesis that E-T coupling also regulates the activity of peripheral nociceptors and thus the process of pain chronification.
We aim 1) to elucidate signaling pathways initiating E-T coupling in nociceptive neurons, 2) to identify neuronal functions regulated by E-T coupling, and 3) to identify their contribution to chronic pain. To address these aims, we will combine
transcriptomic, pharmacological, and optogenetic approaches to understand how specific activity-patterns affect E-T coupling in subgroups of sensory neurons. The effect of E-T-induced changes for electrical activity as well as sensitization-signaling will be determined by MEA electrophysiology and high content screening microscopy
in primary rodent nociceptors and human stem cell derived nociceptors. The role of the elucidated mechanisms in vivo and in pain chronification will be validated in animal pain models. It is plausible to assume that understanding of molecular mechanisms of pain chronification will enable fundamentally new preventative and therapeutic
approaches.
link: https://gepris.dfg.de/gepris/projekt/516750869
Coordinator: Prof. Tim Hucho, University Hospital Cologne
Rapid interaction profiling of 2019-nCoV for network-based deep
drug-repurpose learning (DDRL)RipCoN
First, we aimed to identify approved drugs that can be repurposed for the treatment of COVID-19 (SARS-CoV-2) using interactome profiling and deep-learning. We deployed rapid high-throughput protein-protein interaction mapping and computational protein-RNA interaction predictions to chart the coronavirus host interactome network (CoHIN), which became a public resource for translational and basic coronavirus research few months after project start. CoHIN served as input into an existing deep-learning model to identify approved drugs that are likely effective against COVID-19, which have been validated in in vitro and in vivo systems.
In the second stage, we exploited our resources towards better preparedness against future outbreaks by pursuing mid-term goals. Experimentally, we determined the matrix of viral protein alleles vs. variants of the interacting human proteins to understand how human and viral natural variations jointly mediate disease severity in different individuals. These data have been integrated with epidemiological and human genomics data to improve risk management and improve preparedness for future coronavirus outbreaks. Similarly, on the drug discovery side we applied existing artificial intelligence (AI) approaches to identify the most likely efficient and already approved drugs against the COVID-19.
In summary at INET we
mapped the interactome of COVID-19 and related Coronaviridae with their human host
generated the allele interaction matrix and relate differences to epidemilogical data
identified pathways and nethwor modules targeted by COVID-19
Our corona related results are published in
Nature Biotechnology: https://www.nature.com/articles/s41587-022-01475-z
Communications biology:https://www.nature.com/articles/s42003-025-07933-z
Bioscience reports: https://portlandpress.com/bioscirep/article/44/1/BSR20231418/233934/SARS-CoV-2-NSP14-MTase-activity-is-critical-for
Signal transduction & targeted therapy: https://www.nature.com/articles/s41392-023-01717-9
The role of diet-dependent human microbiome encoded
T3SS-dependent
effectors in modulating health- DIME
Interrelation of the Intestinal Microbiome, Diet and Health HDHL INTIMIC cofunded call
Supported by EU and BmbBF
The human gut microbiome contains thousands of bacterial proteins that interact with their host and influence health and disease. In this project, we combined computational biology, machine learning, large-scale interactome mapping, and experimental validation to systematically identify bacterial effector proteins and investigate how they engage with human cellular pathways.
We established a comprehensive resource of bacterial effector proteins from human gut microbes and generated one of the first large-scale maps of microbiome–host protein interactions. To support the broader research community, we also developed computational tools for the identification and functional analysis of microbial effectors, including approaches to detect molecular mimicry mechanisms that enable bacteria to modulate host cell functions.
The project revealed extensive connections between microbial effectors and human cellular networks, providing new insights into how members of the gut microbiome may influence immune regulation and other biological processes relevant to human health. Experimental studies further demonstrated that selected bacterial effectors can directly modulate host signaling pathways.
The resulting resources, methodologies, and interaction datasets have been published and provide a foundation for future studies exploring the molecular mechanisms through which the gut microbiome shapes human physiology and disease.
The work is published in nature microbiology: https://www.nature.com/articles/s41564-025-02241-y
Young, V.†, Dohai, B.†, Halder, H., Fernandez-Macgregor, J., van Heusden, N. S., Hitch, T. C. A., Weller, B., Hyden, P., Saha, D., Pieren, D. K. J., Rittchen, S., Lambourne, L., Maseko, S. B., Lin, C. W., Tun, Y. M., Bibus, J., Pletschacher, L., Boujeant, M., Choteau, S. A., Bergogne, L., Perrin, J., Ober, F., Schwehn, P., Rothballer, S. T., Altmann, M., Altmann, S., Strobel, A., Rothballer, M., Tofaute, M., Kotlarz, D., Heinig, M., Clavel, T., Calderwood, M. A., Vidal, M., Twizere, J. C., Vincentelli, R., Krappmann, D., Boes, M., Falter, C., Rattei, T., Brun, C., Zanzoni, A. & Falter-Braun, P.* Effector-host interactome map links type III secretion systems in healthy gut microbiomes to immune modulation. Nat. Microbiol. 11, 442–460 (2026).
A Nanophysics-Inspired Platform for Exploring Transient Biomolecular
Interactions Towards Drug Development Against Viral Diseases
Supported by Volkswagen Stiftung
Coordinator/PI Dr. Jian Cui
Publication in ACS Appl. Opt. Matter: https://pmc.ncbi.nlm.nih.gov/articles/PMC11959585/
Systems Biology of Chlamydomonas reinhardtii metabolism
BMBF eBio-Modul III- junior group
Supported by the BmBF
Understanding evolutionary abiotic stress-network plasticity as
foundation for new biotechnological strategies - StressNetAdapt
Supported by the EU: ERC consolidator grant - GA 648420
Abiotic stresses, such as drought or salt stress, affect plant growth and threaten the capacity to feed a growing world population. Understanding and altering how plants deal with stress will be critical for society’s adaptation to a changed climate. Similar to most crops, Arabidopsis thaliana is an abiotic- stress sensitive glycophyte whereas several close relatives are stress tolerant. This constitutes an opportunity to understand how plant stress-signaling networks are modified by evolutionary processes to adapt to novel environmental conditions.
Biological processes are mediated by physically and functionally interacting proteins. Especially stress response networks are rewired when plants adapt to new environmental conditions. I aimed to experimentally map the abiotic stress networks of four closely related brassicaceae: A. thaliana, A. lyrata, A. halleri and E. salsugineum. Novel conceptual advances in interactome mapping and a state- of-the art interactome mapping pipeline was exploited to ensure direct alignability of the resulting reference networks. In addition the dynamic signaling events under drought stress was analysed.
Resulting publications:
Nature: Extensive signal integration by the phytohormone protein network (opens in new window)
MOLECULAR PLANT: Systems Biology of Plant-Microbiome Interactions (opens in new window)
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA: A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice (opens in new window)
Proceedings of the National Academy of Sciences: Mapping transcription factor interactome networks using HaloTag protein arrays (opens in new window)
New Phytologist: Drought resistance is mediated by divergent strategies in closely related Brassicaceae. (opens in new window)
The role of alternative splicing in tissue specific protein interaction networks
Awarded by the Human Frontier Science Program - Early career (2013)
The vast majority (>95%) of human proteins is thought to be affected by alternative splicing. In fact, alternative splicing is one of the mechanisms thought to be underlying the increased organismal complexity of higher mammals. Without a doubt, the combinatorial complexity enabled by alternative splicing is mediated by changes on the protein level. However, only few studies thus far have addressed the consequences of alternative splicing on protein function, e.g. in the context of interaction networks. Particularly, large-scale interactome network mapping so far ignores the increased complexity and dynamic modulation of network connectivity by differential splicing. This project aims to fill this gap and identify protein interactions that are regulated by tissue-specific alternative splicing. We will use a bioinformatic modeling approach and analysis of deep-sequencing transcriptional profiling data to identify physiologically relevant splice isoforms that are likely to affect physical protein-protein interactions. Subsequently, we will experimentally identify differential interaction partners of the identified proteins and their isoforms using high-quality high-throughput interactome mapping by screening the different isoforms for interactions against a genome-wide set of ORFs and subsequent systematic verification. In the third part of the project we aim to genetically demonstrate the biological role of experimentally validated differentially-interacting-splice-isoforms using phenotypic screens and biological follow-up studies for selected high-confidence candidates. All information will be integrated with existing network maps and other biological information.
Project on cooperation with KAIDA Daisuke (JAPAN), Graduate School of Medicine and Pharmaceutical Sciences - University of Toyama - Toyama - JAPAN and
KIM Philip M (GERMANY)
Terrence Donnelly Centre for Cellular and Biomolecular Research - University of Toronto - Toronto - CANADA
Publication:
