Noroviruses are responsible for the majority of gastroenteritis in developed countries and can trigger acute outbreaks in hospitals and nursing homes. However, there is no vaccine and no specific therapy. Therefore, the PRESENt partners from TWINCORE, the Hannover Medical School, the L3S Research Institute of Leibniz University Hannover and the Helmholtz Centre for Infection Research are pooling their expertise at the newly established Centre for Individualised Infection Medicine (CiiM) in Hannover. The PRESENt consortium will use data-intensive technologies and machine learning methods to develop strategies for personalized prognosis, prevention and treatment of severe norovirus infections.
PRESENt addresses three central questions around human norovirus gastroenteritis:
- What are the host factors associated with pathogenesis?
- What are the microbiome signatures of acute, severe and chronic disease?
- Which individual and environmental factors are associated with severe and chronic gastroenteritis?
- How can disinfectants prevent transmission of noroviruses?
In this study we will collect and analyze more than 108 datapoints (proteomics, genomics, clinical and environmental data) from retrospective and prospective patient cohorts. For the first time, this study will take into account the recent discovery of the tight association of human norovirus with host cell membranes and proteins and link it to clinical, environmental and ecological niche (microbiota) data. This has four major implications. First, by understanding the individual differences in norovirus associated host factors, we aim to define prognostic biomarkers of severe and chronic gastroenteritis to stratify patient groups. Second, we will investigate how efficient disinfectants can inactivate patient-derived membrane cloaked norovirus. Third, the microbiome signatures may inform about possibilities of microbiota therapies to alleviate the course of disease in critical patient groups. Fourth, the informatics meta-analysis of the retrospective and prospective study will reveal in addition to prognostic biomarkers and microbiome signatures environmental and individual factors, which associate with severe gastroenteritis. Data will be shared according to FAIR management principles.
In the future, this work will thus pave the way towards better prevention (environmental factors, individualized disinfectant use) and care (targeting/modifying microbiome or host factors) of severe and chronic viral gastroenteritis.
To understand determinants of acute versus severe and chronic norovirus infection and develop preventive measures, we will apply a combined approach of large scale –OMICs data acquisition in Hannover Medial School (MHH) patient cohorts and state of the art Big Data analysis. The informatics analysis will include (a) a retrospective study on available patient data at MHH and (b) a prospective seed study on samples collected over a period of two years. Finally, we will use feature selection, Bayesian networks and machine learning approaches to compute predictors of severe norovirus infection using the proteOMICS data and delineate possible prevention and care strategies based on the microbiome and disinfectant studies.
Specifically, we will apply the following wet lab and informatics methods:
- ProteOMICs: High resolution and quantitative mass spectrometry of non-invasive patient samples (estimated 1.200.000 data points)
- Microbiome analysis incl. MetagenOMICs: 16S sequencing (500.000 data points) and metagenomics (150 million data points)
- Feature selection to understand signatures of severe infections in the –OMICs data
- Machine learning approaches to integrate environmental, individual patient and –OMICs data into a model to predict severe gastroenteritis and possible points of intervention
- Human enteroid infection models to evaluate disinfectant efficacy and patient specific differences in sensitivity to disinfectants (based on norovirus associated host factors)
- Leibniz University (L3S)
- Hannover Medical School
- Helmholtz Centre for Infection Research