bioRxiv Science⌕ Search

Biology subjects

Nissen, M.

Publications and source records attributed to Nissen, M..

2 recordsLinked to original sources

A rational approach for the targeted discovery and characterisation of microbiome-derived therapeutics

The human gut microbiome is intrinsically involved in health and disease, representing a wealth of untapped therapeutic potential. Here, we demonstrate the utility and potential of a metagenome guided, large cohort-based approach for the rational selection of live biotherapeutics from the human gut. We applied this approach to Inflammatory Bowel Disease (IBD), identifying several lead candidates that were significantly depleted in individuals with IBD compared to healthy controls. Their therapeutic potential was assessed in preclinical models of IBD where they improved markers of disease pathology by reducing inflammation and promoting mucosal healing and wound repair. All leads had excellent safety profiles in silico and in vitro, and several additionally presented favourable manufacturing properties, supporting their progression into clinical trials. We believe that this rational approach will be generalisable to any disease state with underlying microbiome aetiology and will expedite the development of novel microbiome-derived therapeutics to improve human health.

microbiology↗

SpatialSort: A Bayesian Model for Clustering and CellPopulation Annotation of Spatial Proteomics Data

Emerging spatial proteomics technologies have created new opportunities to move beyond quantifying the composition of cell types in tissue and begin probing spatial structure. However, current methods for analysing such data are designed for non-spatial data and ignore spatial information. We present SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. SpatialSort clusters cells by accounting for affinities of cells of different types to neighbours in space. Additionally, by incorporating prior information about cell types, SpatialSort outperforms current methods and can perform automated annotation of clusters.

bioinformatics↗