Living with noise: The evolution of gene expression noise in gene regulatory networks

One of the keystones of evolutionary biology is the study of how organismal traits change in time. Technological advancements in the past twenty years have enabled us to study the variation of an important trait, gene expression level, at single cell resolution. One of the sources of gene expression level variation is gene expression noise, a result of the innate stochasticity of the gene expression process. Gene expression noise is gene-specific and can be tuned by selection, but what drives the evolution of gene-specific expression noise remains an open question. In this thesis, I explore the selective pressure and evolvability of gene-specific expression noise in gene regulatory networks. I use evolutionary simulations by applying rounds of mutation, recombination and reproduction to populations of model gene regulatory networks in different selection scenarios. In the first chapter, I investigate the response of gene-specific expression noise in gene regulatory networks in constant environments, which imposes stabilizing selection on gene expression level. The probability of responding to selection and the strength of the selective response was affected by local network centrality metrics. Furthermore, global network features, such as network diameter, centralization and average degree affected the average expression variance and average selective pressure acting on constituent genes. In the second chapter, I investigate the response of mean gene expression level and gene-specific expression noise in isolated genes and genes in gene regulatory networks in changing environments. Gene-specific expression noise of genes increased under fluctuating selection, indicating the evolution of a bet-hedging strategy. Under directional selection gene-specific expression noise transiently increased, showing that expression noise plays a role in the adaptation process towards a new mean expression optimum

Resilience - towards an interdisciplinary definition using information theory

The term “resilience” has risen in popularity following a series of natural disasters, the impacts of climate change, and the Covid-19 pandemic. However, different disciplines use the term in widely different ways, resulting in confusion regarding how the term is used and difficulties operationalising the underlying concept. Drawing on an overview of eleven disciplines, our paper offers a guiding framework to navigate this ambiguity by suggesting a novel typology of resilience using an information-theoretic approach. Specifically, we define resilience by borrowing an existing definition of individuals as sub-systems within multi-scale systems that exhibit temporal integrity amidst interactions with the environment. We quantify resilience as the ability of individuals to maintain fitness in the face of endogenous and exogenous disturbances. In particular, we distinguish between four different types of resilience: (i) preservation of structure and function, which we call “strong robustness”; (ii) preservation of function but change in structure (“weak robustness”); (iii) change in both structure and function (“strong adaptability”); and (iv) change in function but preservation in structure (“weak adaptability”). Our typology offers an approach for navigating these different types and demonstrates how resilience can be operationalised across disciplines

Being noisy in a crowd: Differential selective pressure on gene expression noise in model gene regulatory networks

Expression noise, the variability of the amount of gene product among isogenic cells grown in identical conditions, originates from the inherent stochasticity of diffusion and binding of the molecular players involved in transcription and translation. It has been shown that expression noise is an evolvable trait and that central genes exhibit less noise than peripheral genes in gene networks. A possible explanation for this pattern is increased selective pressure on central genes since they propagate their noise to downstream targets, leading to noise amplification. To test this hypothesis, we developed a new gene regulatory network model with inheritable stochastic gene expression and simulated the evolution of gene-specific expression noise under constraint at the network level. Stabilizing selection was imposed on the expression level of all genes in the network and rounds of mutation, selection, replication and recombination were performed. We observed that local network features affect both the probability to respond to selection, and the strength of the selective pressure acting on individual genes. In particular, the reduction of gene-specific expression noise as a response to stabilizing selection on the gene expression level is higher in genes with higher centrality metrics. Furthermore, global topological structures such as network diameter, centralization and average degree affect the average expression variance and average selective pressure acting on constituent genes. Our results demonstrate that selection at the network level leads to differential selective pressure at the gene level, and local and global network characteristics are an essential component of gene-specific expression noise evolution. Author summary “No man is an island, entire of itself. Each is a piece of the continent, a part of the main.” declares John Donne in his poem For Whom the Bell Tolls, emphasizing that no individual human is entirely separate from humanity as a whole interconnected system. Organisms are biological systems constituted of many interacting components that also interact with each other and the environment. Understanding the evolution of single components such as individual cells or genes can only be fully achieved by considering the interactions with other components. Here, we study the evolution of the cell-to-cell variability of gene expression, the so-called expression noise. To understand the evolution of gene-specific expression noise, we develop a model of gene network evolution with selection at the gene regulatory network level. We find that selection at the gene network level has different repercussions for individual genes based on their position in the network and that gene expression noise is more constrained in genes that are central in the network. Furthermore, the topological structure of the background network affects the propagation and evolvability of gene expression noise. These findings indicate that selection on a given system results in differential selective pressures at the level of subsystems. Our results further suggest that selection to mitigate inherent noise plays a role in network and gene evolution

Being noisy in a crowd: Differential selective pressure on gene expression noise in model gene regulatory networks.

Expression noise, the variability of the amount of gene product among isogenic cells grown in identical conditions, originates from the inherent stochasticity of diffusion and binding of the molecular players involved in transcription and translation. It has been shown that expression noise is an evolvable trait and that central genes exhibit less noise than peripheral genes in gene networks. A possible explanation for this pattern is increased selective pressure on central genes since they propagate their noise to downstream targets, leading to noise amplification. To test this hypothesis, we developed a new gene regulatory network model with inheritable stochastic gene expression and simulated the evolution of gene-specific expression noise under constraint at the network level. Stabilizing selection was imposed on the expression level of all genes in the network and rounds of mutation, selection, replication and recombination were performed. We observed that local network features affect both the probability to respond to selection, and the strength of the selective pressure acting on individual genes. In particular, the reduction of gene-specific expression noise as a response to stabilizing selection on the gene expression level is higher in genes with higher centrality metrics. Furthermore, global topological structures such as network diameter, centralization and average degree affect the average expression variance and average selective pressure acting on constituent genes. Our results demonstrate that selection at the network level leads to differential selective pressure at the gene level, and local and global network characteristics are an essential component of gene-specific expression noise evolution

Global network properties affect the average selective pressure acting on gene expression noise under stabilizing selection on gene expression level.

A, B—Principal component variables consisting of the diameter and network centralization (A) and average degree (B) have a significant negative effect on the average selective pressure per network. The two synthetic variables were constructed by performing a principal component analysis on 12 graph-level network metrics. The lines indicate the 25% (lower dashed line), 50% (solid line), and 75% (upper dashed line) fitted quantiles. The dataset consisted of 3,000 populations with unique 40-gene random, scale-free and small-world network topology samples, which were independently evolved 10 times under selection and 10 times under neutrality. The selective pressure on each gene is calculated as the average normalized reduction of the intrinsic noise parameter during the evolutionary simulation and summarized over all replicates in each scenario. Coefficients and p-values are estimated using a linear model with average selective pressure as the response variable, and PC1 and PC2 as explanatory variables. Mutual information (MI) p-values were computed with permutation test with 10,000 permutations.</p

Network topology type affects the probability of responding to selection and selective pressure on gene-specific expression noise under stabilizing selection on gene expression level.

Network topology type affects the probability of responding to selection and selective pressure on gene-specific expression noise under stabilizing selection on gene expression level.</p

Differential selective pressure is acting on genes based on their centrality.

A, B—Distributions of the measured selective pressure in selected (A) and neutral (B) populations. Genes with a selective pressure above 0.5 were categorized as responsive to selection. C, D—High instrength genes are less likely to respond to selection. Absolute instrength (C) has a strong significant negative effect on the probability of selection response. Absolute outstrength (D) has a weak significant negative effect on the probability of selection response. E, F—In the subset of genes that responded to selection, high instrength (E) decreases the selective pressure, while high outstrength (F) increases the selective pressure acting on individual genes. The lines indicate the 25% (lower dashed line), 50% (solid line), and 75% (upper dashed line) fitted quantiles. G, H—Absolute instrength (G) and outstrength (H) have no significant effect on the selective pressure in the non-selected populations. The dataset consists of 74,443 genes from 2,000 populations with unique 40-gene random network topology samples, which were independently evolved 10 times under selection and 10 times under neutrality. The selective pressure on each gene is calculated as the average normalized reduction of the intrinsic noise parameter during the evolutionary simulation and summarized as the mean over all replicates in each scenario. Coefficients, p-values and partial marginal R2 measures are estimated using logistic regression and linear mixed-effects models with selection responsiveness or selective pressure as the response variable, instrength and outstrength as fixed effect explanatory variables, and the network topology sample as the random effect explanatory variable. Mutual information (MI) p-values were using 10,000 permutations.</p

Node-level network centrality measures affect the relative change of gene-specific expression variance under network-level selection.

For each gene, the relative change of expression variance before and after evolution (Rel. Δ expr. variance) was averaged over all replicates. A, B—Absolute instrength (A) and absolute outstrength (B) have a significant negative effect on the relative change in gene expression variance in populations evolved under selection. A lower value of relative change of expression variance indicates a bigger reduction in expression variance between the first and last generation and a stronger response to selection. The lines indicate the 25% (lower dashed line), 50% (solid line), and 75% (upper dashed line) fitted quantiles. C, D—Absolute instrength (C) and absolute outstrength (D) have a significant, but negligible, negative effect on the relative change in gene expression variance in the populations evolved under neutrality. The dataset consists of 74,443 genes from 2,000 populations with unique 40-gene random network topology samples, which were independently evolved 10 times under selection and 10 times under neutrality. Coefficients, p-values and partial marginal R2 measures were estimated using linear mixed-effects models with relative change of gene-specific variance as the response variable, instrength and outstrength as fixed effect explanatory variables, and the network topology sample as the random effect explanatory variable. Mutual information (MI) p-values were computed using 10,000 permutations.</p

DataSheet1_Resilience—Towards an interdisciplinary definition using information theory.PDF

The term “resilience” has risen in popularity following a series of natural disasters, the impacts of climate change, and the Covid-19 pandemic. However, different disciplines use the term in widely different ways, resulting in confusion regarding how the term is used and difficulties operationalising the underlying concept. Drawing on an overview of eleven disciplines, our paper offers a guiding framework to navigate this ambiguity by suggesting a novel typology of resilience using an information-theoretic approach. Specifically, we define resilience by borrowing an existing definition of individuals as sub-systems within multi-scale systems that exhibit temporal integrity amidst interactions with the environment. We quantify resilience as the ability of individuals to maintain fitness in the face of endogenous and exogenous disturbances. In particular, we distinguish between four different types of resilience: (i) preservation of structure and function, which we call “strong robustness”; (ii) preservation of function but change in structure (“weak robustness”); (iii) change in both structure and function (“strong adaptability”); and (iv) change in function but preservation in structure (“weak adaptability”). Our typology offers an approach for navigating these different types and demonstrates how resilience can be operationalised across disciplines.</p