Quantitative analysis of disease-related metabolic dysregulation of human microbiota

Maria Rita Fumagalli; Stella Maria Saro; Matteo Tajana; Stefano Zapperi; Caterina A M La Porta

doi:10.1016/j.isci.2022.105868

Quantitative analysis of disease-related metabolic dysregulation of human microbiota

iScience. 2022 Dec 23;26(1):105868. doi: 10.1016/j.isci.2022.105868. eCollection 2023 Jan 20.

Authors

Maria Rita Fumagalli^{1

2}, Stella Maria Saro³, Matteo Tajana³, Stefano Zapperi^{3

4}, Caterina A M La Porta^{1

2}

Affiliations

¹ Center for Complexity and Biosystems, Department of Environmental Science and Policy, University of Milan, via Celoria 26, 20133 Milano, Italy.
² CNR - Consiglio Nazionale delle Ricerche, Istituto di Biofisica, via De Marini 6, 16149 Genova, Italy.
³ Center for Complexity and Biosystems, Department of Physics, University of Milan, Via Celoria 16, 20133 Milano, Italy.
⁴ CNR - Consiglio Nazionale delle Ricerche, Istituto di Chimica della Materia Condensata e di Tecnologie per l'Energia, Via R. Cozzi 53, 20125 Milano, Italy.

Abstract

The metabolic activity of all the micro-organism composing the human microbiome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with the microbiome of an individual. STELLA integrates known information on metabolic pathways associated with each bacterial species and extracts from these the list of metabolic products of each singular reaction by means of automatic text analysis. By comparing the result obtained on a single subject with the metabolic profile data of a control set of healthy subjects, we are able to identify individual metabolic alterations. To illustrate the method, we present applications to autism spectrum disorder and multiple sclerosis.

Keywords: Computational bioinformatics; Microbiome.