A reverse metabolic approach to weaning: in silico identification of immune-beneficial infant gut bacteria, mining their metabolism for prebiotic feeds and sourcing these feeds in the natural product space

Abstract

BackgroundWeaning is a period of marked physiological change. The introduction of solid foods and the changes in milk consumption are accompanied by significant gastrointestinal, immune, developmental, and microbial adaptations. Defining a reduced number of infections as the desired health benefit for infants around weaning, we identified in silico (i.e., by advanced public domain mining) infant gut microbes as potential deliverers of this benefit. We then investigated the requirements of these bacteria for exogenous metabolites as potential prebiotic feeds that were subsequently searched for in the natural product space.ResultsUsing public domain literature mining and an in silico reverse metabolic approach, we constructed probiotic-prebiotic-food associations, which can guide targeted feeding of immune health-beneficial microbes by weaning food; analyzed competition and synergy for (prebiotic) nutrients between selected microbes; and translated this information into designing an experimental complementary feed for infants enrolled in a pilot clinical trial (http://www.nourishtoflourish.auckland.ac.nz/).ConclusionsIn this study, we applied a benefit-oriented microbiome research strategy for enhanced early-life immune health. We extended from “classical” to molecular nutrition aiming to identify nutrients, bacteria, and mechanisms that point towards targeted feeding to improve immune health in infants around weaning. Here, we present the systems biology-based approach we used to inform us on the most promising prebiotic combinations known to support growth of beneficial gut bacteria (“probiotics”) in the infant gut, thereby favorably promoting development of the immune system.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0545-x) contains supplementary material, which is available to authorized users.

History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications

“…Ontology-based approaches ( Smith et al, 2007 ; Groß et al, 2016 ; Vitali et al, 2018 ), databases ( Caspi et al, 2016 ; Olsen et al, 2017 ), and expert knowledge help craft extended domains of concepts made up of synonyms, alternatives, and related keywords each specifically addressing different areas of the modeled biology. Such concept-oriented approaches have been used, for example in ( Michelini et al, 2018 ; Azer et al, 2019 ), allowing to screen for thousands of terms at once, dramatically widening the recall of the mining searches. At this stage, a first-pass screening allows the expert to identify the most promising literature and tune the queried concepts.…”

Microbiome Product Toxicology: Regulatory View on Translational Challenges

“…They will among other things support the development of applications to manipulate microbiome (OECD 2017). Nevertheless, researchers have already identified the potential use of M&S applied to the microbiome field (Moorthy and Eberl 2017; Borenstein 2012; Bauer and Thiele 2018; Greenhalgh et al 2019;Michelini et al 2018). Moorthy et al proposed an in silico platform to investigate host microbiome, its interactions, and its role on the host health (Moorthy and Eberl 2017).…”

“…Moorthy et al proposed an in silico platform to investigate host microbiome, its interactions, and its role on the host health (Moorthy and Eberl 2017). Other research teams also developed M&S strategies to explore microbiome impact on immune system and even identify personalized microbiota (Bauer and Thiele 2018;Michelini et al 2018).…”

Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis

“…Text-mining is increasingly used to extract knowledge from unstructured text of scientific articles ( Huang and Lu, 2016 ; Przybyla et al, 2016 ; Westergaard et al, 2018 ; Lee et al, 2020 ). Successful examples of text-mining in the biomedical field include extractions of gene-disease associations ( Piñero et al, 2015 ; Zhou and Fu, 2018 ), protein–protein interactions ( Saik et al, 2016 ; Szklarczyk et al, 2019 ), drug discovery ( Azer et al, 2019 ; Zheng et al, 2019 ; Hansson et al, 2020 ) and clinical trial design ( Michelini et al, 2018 ). Text-mining has also been applied to the study of the immune system.…”