We aim to reconstruct intestinal cell, microbial strain, disease and patient specific signalling and regulatory networks, and integrate them in a multiscale system containing both host cells and microbes to understand molecular (patho)mechanisms.

Our key interests are the role of autophagy in intestinal inflammation and cancer, the systems biology of ulcerative colitis (a type of inflammatory bowel disease), and microbial rewiring of host regulatory processes.

The group combines in silico network biology methods with experimental organoid-based validation and ‘omics approaches to explore and understand how pathogen and commensal microbes are affecting host processes.

Key projects and their impact:

• Investigating the host adaptation of Salmonella enterica strains: Salmonella enterica and its many strains are one of the most prominent pathogens causing disease in humans and livestock. Most of these strains are generalists, capable of infecting many species, but some of them specialise on just a narrow range of hosts. We are using various network comparison methods on a multi-layered Salmonella network database developed in our group – SalmoNet – to identify key pathways and regulators that differentiate the host adapted strains from the generalist pathogens. We hope uncovering these key elements will elucidate the evolutionary past of Salmonella, and might offer potential intervention targets against this prominent pathogen.

• Understanding the effect of Salmonella on the regulation of autophagy: Autophagy is an important host process in stress responses, regulation of inflammation, and intestinal homeostasis, including the elimination of intracellular pathogens. Conversely, autophagy is often hijacked by intestinal pathogenic bacteria, such as Salmonella. By combining computational and wet lab approaches we are aiming to understand how certain virulence proteins of Salmonella are affecting the expression of autophagy genes.

• Uncovering the mechanisms of the beneficial regulatory effect of Bifidobacteria: Whilst the human gut commensal Bifidobacteria has been highlighted as a protective agent against a number of health conditions, the specific modulating factors are largely unidentified. We are combining network biology and organoid approaches to identify the impact of Bifidobacteria on the regulatory landscapes and phenotypes of intestinal cells. We hope this work will lead to a greater understanding of the mechanisms of action of Bifidobacteria and pave the way for translational developments in prevention and treatment of gastrointestinal disease and disturbances.

• Predicting host-microbe interactions and their cellular effect in human meta’omics approaches: The microbiome plays an important role to maintain homeostatic functions in host system, disrupting the healthy microbiota the established dysbiotic state could lead to the development of various disorders. During the project we aim to develop a bioinformatic pipeline to infer host-microbe interactions using -omics data.

• Integrating multi-omic datasets to uncover pathomechanisms and biomarkers of Inflammatory Bowel Disease (IBD): IBD is a complex disease of the gastro-intestinal tract characterised mainly by chronic inflammation and microbial dysbiosis. ‘Omic approaches offer a distinct possibility to explain the confounding factors as well as the underlying pathomechanisms that drive the disease. We use a combination of statistical and apriori knowledge in terms of biological networks to interpret patient-derived ‘omic datasets such as gene expression derived from tissue and blood monocytes, genotyping information and blood proteomics. This is followed by Machine Learning techniques to either identify and classify combinatorial ‘omic signatures corresponding to disease sub-types or predict responses to particular treatment regimens. The project, is a collaborative effort between the Korcsmaros group and the IBD unit headed by Prof. Severine Vermeire at KU Leuven, Belgium.

• Developing machine learning based systems biology approach to analyse gut microbiome data: The gut microbiome primes the immune system during early development and contributes to lifelong homeostasis. Several age-related disorders such as dementia and type-2-diabetes are associated with gut microbiome alterations. Despite developments in generating microbiome data, a key challenge is to extract predictive biomarkers. Furthermore, large number of unknown microbes (mostly commensals), whose health contributing potential remains unclear. To address these issues, we are developing an integrated machine learning based systems biology workflow, for identifying prognostic indicators of healthy ageing and age-related disorders. The project is co-funded by BenevolentAI.

We collaborate with many individuals and organisations throughout the world, including:

• Julio Saez-Rodriguez (RWTH Aachen University, Germany)
• Andrei Zinovyev (Institut Curie, France)
• Jost Enninga  (Pasteur Institute, France)
• Ruth Nussinov (National Cancer Institute, USA)
• Tibor Vellai (Eotvos Lorand University, Hungary)
• Peter Csermely (Semmelweis University, Hungary)
• Ioannis Nezis (University of Warwick)
• Sharon Tooze (The Crick Institute)
• Daniela Delneri and Jean-Marc Schwartz (University of Manchester)
• Sandra Orchard and Henning Hermjakob (EMBL-EBI)
• Jay Hinton (University of Liverpool)