DOI: 10.5061/dryad.2fqz61312
title: Data from: Gene network topology drives the mutational landscape of gene expression

description: Regulatory mutations, coding sequence variations, and gene deletions and
 duplications are generally expected to have qualitatively different
 effects on fitness during adaptation. We aim to ground this expectation
 within a theoretical framework using evolutionary simulations of gene
 regulatory networks (GRNs) controlling the expression of fitness-related
 genes. We examined the distribution of fitness effects as a function of
 the type of mutation and the topology of the gene network. Contrary to our
 expectation, the GRN topology had more influence on the effect of
 mutations than the type of mutation itself. In particular, the topology
 conditioned (i) the speed of adaptation, (ii) the distribution of fitness
 effects, and (iii) the degree of pleiotropy which acts as explanatory
 factor for all mutation types. All mutations had the potential to
 participate in adaptation, although their propensity to generate
 beneficial variants differed according to the network topology. In
 scale-free networks, arguably the most common topology for biological
 networks, coding mutations were more pleiotropic and overrepresented in
 both beneficial and deleterious mutations, while regulatory mutations were
 more often neutral. However, this observation was not general, as this
 pattern was reversed in the other network topologies. These results
 highlight the critical role of gene interactions in defining
 mutations' contributions to adaptation.
description: # Data from: Gene network topology drives the mutational landscape of gene
 expression ## Project organization The project repository contains the
 following directories: | Directory | Location | | ------------------- |
 ------------------------------ | | Extracted\_data | Dryad | | figures |
 *empty* | | movies | *empty* | | SIMULATIONS | Dryad | | src | Zenodo
 (Software Related work) | | *various R scripts* | Zenodo (Software Related
 work) | It is necessary to have both the code (from Zenodo) and simulation
 data in the same directory to generate the figures. Simulation data can be
 either downloaded from Dryad, or re-generated from the code (takes several
 days depending on the hardware). ## Simulation data in Dryad This Dryad
 repository contains a zip file (GeneRation_Data_Analysis_pack.zip) with
 two directories: * `SIMULATIONS` folder: raw simulation results (two
 replicated runs for each network topology: HIGHCO (highly connected),
 RANDOM (random), SCALEF (scale free)). Each run reports: the parameter
 file (.param), the initial and final populations (.rds files), the full
 simulation table (.table), and two PNG image files (.graphall and
 .heatmap) that monitor the behavior of the simulations (warning: depending
 on the system setting, it might be necessary to add the .png extension to
 these files for a correct visualization). The folder names in the Dryad
 repository are: * 0612_215807_HIGHCO_002_november *
 0612_215807_HIGHCO_004_november * 0613_091236_RANDOM_001_november *
 0613_091244_RANDOM_002_november * 0613_222625_SCALEF_002_november *
 0613_222625_SCALEF_002_november * `Extracted_data`: processed simulation
 results * allmutations_20250619003541_001_TUTO_SIMUS.csv This file is a
 table containing the effects of mutations calculated in the second step of
 the analysis, from the last generations of the simulations. Columns in
 this csv file stands for: * id: unique mutation #id (line number) * name:
 simulation series (one of the folder name in the SIMULATIONS directory) *
 topology: either HIGHCO, RANDOM, or SCALEF * mut_type: either REG, COD, or
 DUP (regulatory, coding, duplication, respectively) * i, j: coordinates of
 the affected slot in the W matrix. for COD mutations, j=11 (technically,
 the vector of the coding value was the 11th column of the genotype
 matrix). DUP mutations are not associated with i,j coordinates, so the
 value is NA. * mutvalue_init and mutvalue_final: value of the slot i,j
 before and after the mutation (NA for DUP mutations) * mutvalue_delta:
 mutvalue_final - mutvalue_init * dupdel_number: number of duplicated genes
 (NA if not DUP) * dupdel_genes: identity of duplicated genes (indexed from
 1 to 10, i.e. 594 stands for genes 5, 9, and 4; 710 stands for 7 and 10) *
 fit_pop: fitness of the average genotype in the population * fit_indE1:
 fitness before mutation in environment 1 (= fit_pop since mutations were
 performed from the average genotype) * fit_indE2: fitness before mutation
 in environment 2 * fit_mutE2: fitness after mutation in environment 2 *
 fit_delta: difference before - after mutation * fit_effect: NEUTR, BENEF,
 or DELET * fit_thresh (always 0.001): threshold used to determine whether
 mutations are neutral or not * pleiotropy: number of genes which
 expression was affected by the mutation * affected_genes_thresh:
 phenotypic threshold (in gene expression unit) to determine whether a
 mutation affects gene expression * reg_muteff, cod_mutef: same as
 mutvalue_delta, but each column is specific of a type of mutation * notes:
 comments * The next columns are relevant only for mutations that have a
 phenotypic effect. They give the gene id (gene), the change in gene
 expression (dExpr), and the nature of the effect (cis or trans) for a
 random gene (rand suffix), the minimum expression change (min suffix), the
 maximum expression change (max suffix), and the mean expression change
 (mean suffix). The last column (Expr_effect_cisness) is the
 cis/(cis+trans) ratio (1 for cis-only mutations, 1/10 for mutations that
 affect all 10 genes). Other files: * storeFits_TUTO_SIMUS: csv file,
 average fitnesses at every generation for the simulation runs from the
 SIMULATIONS directory * storeJustLastFits_TUTO_SIMUS: csv file, the same
 but only the last generation * storeLasts_TUTO_SIMUS: csv file, full
 simulation summary (from the simulation tables in the SIMULATIONS
 directory) but only for the last generation. These simulated data were the
 ones used to generate figures and tables in the associated publication. ##
 GeneRation software The associated Zenodo repository contains the R code
 to reproduce results of Pouzet & Le Rouzic 2025 - Evolution:
 "Gene network topology drives the mutational landscape of gene
 expression". ### Packages required This work was carried out on R
 version 4.04 (R Core Team 2021) * rlist (v0.4.6.2) - save and load
 simulations * RColorBrewer (v1.1.2) - color generation * igraph (v1.2.10)
 - display networks * MASS (v7.3.53.1) - fits for degree distributions ###
 Reproduction of the results * Make sure R has the packages installed. *
 Launch simulations: In bash, use cd /SIMULATIONS to move to the correct
 directory and launch sh ../0-run_simulations.sh, which will launch 9 small
 simulations labeled "TUTO_SIMUS" * Extract simu features and
 isolate successful ones (1-data_extract_from_simus.R) * Carry out mutation
 tests (2-run_mutation_tests.R) * Plot figure 2: check and plot simu, fit
 and plot degree (3-Plot_fig2.R) * Plot figure 3: compare adaptation
 profiles (4-Plot_fig3.R) * Plot figure 4: Plot fitness effects
 (5-Plot-fig4.R) * Plot figure 5: Plot cis-effects and pleiotropy
 (6-Plot-fig5.R) * Plot figure 6: Enrichment analysis (7-Plot-fig6.R) ###
 Folders * /SIMULATIONS folder: where raw simulations results are placed *
 /Extracted_data folder: contains results extracted from the simulations
 and mutation tests * /figures folder: contains generated figures * /src
 folder: source files and parameters. Find more details on data structure
 and parameter files in the readme - it contains the 3 sets of parameter
 files used to obtain the 3 topologies described in the article, as well as
 a detail account of each parameter used in the model. * src/playground:
 see this folder for a small tutorial and useful functions. It contains a
 step by step template R script to get familiar with the treatment of the
 data generated from the simulations. Two simulation outputs are also
 included. ### Notes * Figure 1 is the materials and methods figures, so
 not part of the results reproduction. * Each subfolder has a corresponding
 README which will provide additional detail. ## Useful links * Access to
 the paper is via
 [https://doi.org/10.1093/evolut/qpaf068](https://doi.org/10.1093/evolut/qpaf068) * This github repository is [https://github.com/spouze/GeneRation_Pouzet2025/](https://github.com/spouze/GeneRation_Pouzet2025/) * The Dryad corresponding repository is [https://doi.org/10.5061/dryad.2fqz61312](https://doi.org/10.5061/dryad.2fqz61312) * The former BioRxiv entry is [https://www.biorxiv.org/content/10.1101/2024.11.28.625874v1](https://www.biorxiv.org/content/10.1101/2024.11.28.625874v1)