High dimensional geometry of fitness landscapes identifies master regulators of evolution and the microbiome

Abstract

A longstanding goal of biology is to identify the key genes and species that critically impact evolution, ecology, and health. Yet biological interactions between genes, species, and different environmental contexts change the individual effects due to non-additive interactions, known as epistasis. In the fitness landscape concept, each gene/organism/environment is modeled as a separate biological dimension, yielding a high dimensional landscape, with epistasis adding local peaks and valleys to the landscape. Massive efforts have defined dense epistasis networks on a genome-wide scale, but these have mostly been limited to pairwise, or two-dimensional, interactions. Here we develop a new mathematical formalism that allows us to quantify interactions at high dimensionality in genetics and the microbiome. We then generate and also reanalyze combinatorically complete datasets (two genetic, two microbiome). In higher dimensions, we find that key genes (e.g. pykF) and species (e.g. Lactobacillus plantarum) distort the fitness landscape, changing the interactions for many other genes/species. These distortions can fracture a landscape with one optimal fitness peak into a landscape with many local optima, regulating evolutionary or ecological diversification, which may explain how a probiotic bacterium can stabilize the gut microbiome.

Global epistasis and the emergence of ecological function

“…1C-D). Based on the success of recently found parallelisms between genetic and functional ecological interactions ( 28,29,31 ), we reasoned that the latter can be partitioned in the same manner, as the sum of (i) a global, species-by-community (S×C) interaction described by how the functional effect of a species scales with the function of the community to which it is added, and (ii) an idiosyncratic interaction captured by the residuals.…”

“…Historically, the study of genetic interactions (epistasis) has broken them down as the sum of pairwise interactions (G×G), third-order interactions (G×G×G), fourth-order, and so on (25). This has paralleled the similar partitioning of ecological interactions as the sum of pairwise speciesby-species (S×S) and higher-order (e.g., S×S×S) effects (26)(27)(28)(29)(30)(31)(32). Recent work in genetics has proposed that epistasis can be instead partitioned into a global epistasis component, described by a linear regression between the fitness effect of a mutation and the fitness of the background, and an idiosyncratic component described by the residuals of this fit (Fig.…”

The community-function landscape of microbial consortia

“…Despite this and other recent attempts to characterize high-order functional interactions (Eble et al, 2021;Gould et al, 2018;Sanchez-Gorostiaga et al, 2019) mojavensis to the co-culture of P. polymyxa with the other partner, their impact on function is either neutral or negative. This shows that the functional effect of adding a species to a consortium may be different when a second species is present, indicating the existence of a High-Order Functional Interaction (HOFI).…”

“…These and other studies (Bittleston et al, 2020;Clark et al, 2021;Eble et al, 2021;George and Korolev, 2021;Gopalakrishnappa et al, 2022;Gould et al, 2018;Sanchez-Gorostiaga et al, 2019;Senay et al, 2019;Xie and Shou, 2021;Xie et al, 2019) have formally defined the structure-function (or composition-function, or community-function) landscape as the empirical map between community composition and function in a given habitat and set of conditions. The structure of a microbial consortium is given by the list of all it genotypes g = {g1,g2,...,gn} and their respective abundances xg = {x1,x2,...,xn}.…”

“…Interactions can similarly be defined in other combinatorial systems that are not genetic, and in fact the term"epistasis" has been used to describe systems as diverse as drug interactions (Tekin Elif et al, 2016;Wood et al, 2012) or combinations of stressors (Beppler Casey et al, 2016), among others (Tekin et al, 2018). In recent years, we (and others) have extended it to ecological systems as well (Eble et al, 2021;Gould et al, 2018;Guo and Boedicker, 2016;Sanchez, 2019;Sanchez-Gorostiaga et al, 2019;Senay et al, 2019;Senne de Oliveira Lino et al, 2021), and the underlying idea was already present in earlier efforts to model the emergence of community function (Chen et al, 2009;Eng and Borenstein, 2019). (or multiplicative, depending on the scale).…”