The Epistemic Revolution Induced by Microbiome Studies: An Interdisciplinary View

Abstract

Many separate fields and practices nowadays consider microbes as part of their legitimate focus. Therefore, microbiome studies may act as unexpected unifying forces across very different disciplines. Here, we summarize how microbiomes appear as novel major biological players, offer new artistic frontiers, new uses from medicine to laws, and inspire novel ontologies. We identify several convergent emerging themes across ecosystem studies, microbial and evolutionary ecology, arts, medicine, forensic analyses, law and philosophy of science, as well as some outstanding issues raised by microbiome studies across these disciplines and practices. An 'epistemic revolution induced by microbiome studies' seems to be ongoing, characterized by four features: (i) an ecologization of pre-existing concepts within disciplines, (ii) a growing interest in systemic analyses of the investigated or represented phenomena and a greater focus on interactions as their root causes, (iii) the intent to use openly multi-scalar interaction networks as an explanatory framework to investigate phenomena to acknowledge the causal effects of microbiomes, (iv) a reconceptualization of the usual definitions of which individuals are worth considering as an explanans or as an explanandum by a given field, which result in a fifth strong trend, namely (v) a de-anthropocentrification of our perception of the world.

Keywords: evolutionary microbiology; humanities; individuals; law; literature; microbial ecology; microbiomes; networks; philosophy of biology; selection; visual art.

Figure 1

Six hundred and fifty handshakes. This piece of art by the bioartist François-Joseph Lapointe belongs to the series ‘1000 handshakes’. For this project, the artist shook hands with as many people as possible in various cities, gradually changing the microbial community in the palm of his right hand. Periodically, assistants have taken a skin microbiome sample and the DNA collected has been sequenced and analyzed to generate sequence similarity networks. Two bacteria (nodes) are connected in the network when their genetic sequences are more similar than a fixed threshold (90%), with different clusters corresponding to distinct bacterial families and different colors representing the microbiome samples collected at regular intervals (i.e., every 50th handshakes).

Figure 2

An example of a network-based analysis of microbiome selection (from [40]). The co-occurrence matrices of the T-RFLP-defined genetic units present after 21 generations were used to build interaction networks for (a) the control (C) and (b) the selection treatment for low CO₂ emission (S). When two dots are connected by lines, it means that the abundances of the genetic units were significantly correlated (Spearman correlation coefficient; N = 6; p < 0.05). The interaction networks were used to calculate several network indices (c–f) as follows. The average degree (c) is the average number of interactions engaged in by one genetic unit (equal to 0 for an unconnected unit), providing an estimate of network complexity. The average betweenness (d) is the average number of shorter chains going through one node, which can signal the presence of keystone species in the network. The connectance (e) is the proportion of possible links between species that are actually realized. The connectedness (f) is the probability that at least one chain exists between any pair of units, which quantifies all the direct and indirect interactions within the network. The networks were bootstrapped (200 random samples from each group’s pool of genetic units) to determine (e) average connectance and (f) average connectedness. The values of these indices were compared for the control and selection treatment using Wilcoxon rank-sum tests (employing a continuity correction for non-parametric distributions).