Disentangling Interactions in the Microbiome: A Network Perspective

Abstract

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.

Fungal community reveals less dispersal limitation and potentially more connected network than that of bacteria in bamboo forest soils

“…Additionally, these features indicated that the fungi displayed small-world behaviour. In a small-world network, most OTUs are accessible to every other OTU through a relatively short path (Layeghifard, Hwang, & Guttman, 2017). The degrees for the bacterial network were distributed according to power-law distributions (Fig.…”

Plant–microbe networks in soil are weakened by century‐long use of inorganic fertilizers

“…Network inference techniques are increasingly employed to decipher the relationships among microbes (Faust et al, 2015). These techniques range from simple pairwise Pearson or Spearman correlation measures, to more complex multiple regression and Gaussian graphical models (Zhou et al, 2010;Van den Bergh et al, 2012;Layeghifard et al, 2017). The application of network theory to microbiome studies can be used to model the co-occurrence of microorganisms, find microbial relationships essential for community assembly or stability and deduce the influence of various interactions on the host health (Layeghifard et al, 2017).…”

“…These techniques range from simple pairwise Pearson or Spearman correlation measures, to more complex multiple regression and Gaussian graphical models (Zhou et al, 2010;Van den Bergh et al, 2012;Layeghifard et al, 2017). The application of network theory to microbiome studies can be used to model the co-occurrence of microorganisms, find microbial relationships essential for community assembly or stability and deduce the influence of various interactions on the host health (Layeghifard et al, 2017). Long-term inorganic fertilization can increase the availability of soil nutrients and thus weaken the stability of the microbial network structure; for example, microbial networks in soils with applications of N-P-K fertilizer become sparse and divergent (Li et al, 2017;Wang et al, 2017a,b).…”

ProphTools: general prioritization tools for heterogeneous biological networks

“…From protein-protein interaction prediction [3] to the identification of candidate disease genes to drug repositioning [4] or very recent applications on microbiology [5], it seems to be clear that inference on graph or network data structures can be effective for the purpose of finding relations between entities that interact in such ways [1]. These in silico predictions allow researchers to reduce the search space to focus on a small set of entities that are more likely to be related to the entities of interest.…”