Enabling predictive modeling of molecular connections between fermented foods and human inflammation through a paired dataset of cell-based models and multi-omics approaches

Fermented foods contain a diverse array of microbes and bioactive compounds, which have been associated with anti-inflammatory properties and enhanced gut microbiome diversity. Although numerous studies have explored fermented foods, including large-scale genome sequencing and metabolite analysis, the intricate mechanisms by which these foods, their microbial populations, and metabolic byproducts influence human biological pathways remain poorly understood.

In this study, the researchers systematically analyzed more than 100 commercially available fermented foods to assess their potential in mitigating inflammation. They employed human cell-based models to evaluate the impact of these foods. Additionally, they performed bulk RNA sequencing on the treated human cells, alongside metagenomic sequencing and metabolomics of the consumed fermented foods.

The researchers generated sample-matched multi-omics data as part of this investigation. The primary objective of this work is to establish a robust foundation that will enable scientists to develop predictive models and generate testable hypotheses regarding the molecular pathways underlying the anti-inflammatory effects of fermented foods.

The team has made all raw data along with parsed intermediary files available as an open-source resource, empowering the scientific community to delve into the molecular mechanisms linking fermented foods to human biology. This approach has the potential to pave the way for novel, evidence-based strategies that utilize fermented foods as functional ingredients.