Disentangling Interactions in the Microbiome: A Network Perspective

• Published in: Trends in microbiology (Regular ed.) Vol. 25; no. 3; pp. 217 - 228
• Main Authors: Layeghifard, Mehdi, Hwang, David M., Guttman, David S.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.03.2017; Elsevier Science Ltd
• Subjects: Agriculture; analytical methods; Animals; ecosystems; Humans; Industrial applications; Internal Medicine; keystone species; Medical research; medicine; microbial clusters; microbial interactions; Microbiological Phenomena; microbiome; Microbiota - physiology; Microorganisms; Models, Biological; network; network theory; Precision medicine; Public health; Theory; microbial interactions; microbial clusters; keystone species; microbiome; network
• ISSN: 0966-842X; 1878-4380; 1878-4380
• DOI: 10.1016/j.tim.2016.11.008

Microbiota are now widely recognized as being central players in the health of all organisms and ecosystems, and subsequently have been the subject of intense study. However, analyzing and converting microbiome data into meaningful biological insights remain very challenging. In this review, we highlight recent advances in network theory and their applicability to microbiome research. We discuss emerging graph theoretical concepts and approaches used in other research disciplines and demonstrate how they are well suited for enhancing our understanding of the higher-order interactions that occur within microbiomes. Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe–host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture. Polymicrobial communities (microbiota) are complex, dynamic, and ubiquitous. Microbiota play a central role in host health and development. For example, dysbiotic shifts in the composition of the human microbiome have been linked to a wide variety of health issues, such as obesity, diabetes, eczema, heart disease, asthma, colitis, etc. The complexity of microbiomes motivates a movement from reductionist approaches that focus on individual pathogens in isolation to more holistic approaches that focus on interactions among members of the community and their hosts. Network theory has emerged as an extremely promising approach for modelling complex biological systems with multifaceted interactions between members, such as microbiota. Networks enhance the analysis of polymicrobial interactions within microbiota and their role in health, disease, and development.