Data from: Increased gene dosage plays a predominant role in the initial stages of evolution of duplicate TEM-1 beta lactamase genes

Gene duplication is important in evolution, because it provides new raw material for evolutionary adaptations. Several existing hypotheses about the causes of duplicate retention and diversification differ in their emphasis on gene dosage, sub-functionalization, and neo-functionalization. Little experimental data exists on the relative importance of gene expression changes and changes in coding regions for the evolution of duplicate genes. Furthermore, we do not know how strongly the environment could affect this importance. To address these questions, we performed evolution experiments with the TEM-1 beta lactamase gene in E. coli to study the initial stages of duplicate gene evolution in the laboratory. We mimicked tandem duplication by inserting two copies of the TEM-1 gene on the same plasmid. We then subjected these copies to repeated cycles of mutagenesis and selection in various environments that contained antibiotics in different combinations and concentrations. Our experiments showed that gene dosage is the most important factor in the initial stages of duplicate gene evolution, and overshadows the importance of point mutations in the coding region

Transcription factor binding process is the primary driver of noise in gene expression.

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence

Data from: Increased gene dosage plays a predominant role in the initial stages of evolution of duplicate TEM-1 beta lactamase genes

Gene duplication is important in evolution, because it provides new raw material for evolutionary adaptations. Several existing hypotheses about the causes of duplicate retention and diversification differ in their emphasis on gene dosage, sub-functionalization, and neo-functionalization. Little experimental data exists on the relative importance of gene expression changes and changes in coding regions for the evolution of duplicate genes. Furthermore, we do not know how strongly the environment could affect this importance. To address these questions, we performed evolution experiments with the TEM-1 beta lactamase gene in E. coli to study the initial stages of duplicate gene evolution in the laboratory. We mimicked tandem duplication by inserting two copies of the TEM-1 gene on the same plasmid. We then subjected these copies to repeated cycles of mutagenesis and selection in various environments that contained antibiotics in different combinations and concentrations. Our experiments showed that gene dosage is the most important factor in the initial stages of duplicate gene evolution, and overshadows the importance of point mutations in the coding region

Evolution_sequencing_data

This file contains 454 amplicon sequencing data for the ancestral strains and the evolved lines (EvoS and EvoD lines). The file includes raw sequencing data as well as alignment files

Single cell functional genomics reveals the importance of mitochondria in cell-to-cell phenotypic variation

Mutations frequently have outcomes that differ across individuals, even when these individuals are genetically identical and share a common environment. Moreover, individual microbial and mammalian cells can vary substantially in their proliferation rates, stress tolerance, and drug resistance, with important implications for the treatment of infections and cancer. To investigate the causes of cell-to-cell variation in proliferation, we used a high-throughput automated microscopy assay to quantify the impact of deleting >1500 genes in yeast. Mutations affecting mitochondria were particularly variable in their outcome. In both mutant and wild-type cells mitochondrial membrane potential - but not amount - varied substantially across individual cells and predicted cell-to-cell variation in proliferation, mutation outcome, stress tolerance, and resistance to a clinically used anti-fungal drug. These results suggest an important role for cell-to-cell variation in the state of an organelle in single cell phenotypic variation.Work in the lab of BL was supported by a European Research Council Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2017-89488-P and SEV-2012–0208), the AXA Research Fund, the Bettencourt Schueller Foundation, Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR SGR 1322), the EMBL Partnership, and the Generalitat/CERCA program. Work in the lab of LBC was supported by MINECO (BFU2015-68351-P) and AGAUR (2014 SGR 0974) and the Unidad de Excelencia Maria de Maeztu (MDM-2014–0370)

Single cell functional genomics reveals the importance of mitochondria in cell-to-cell phenotypic variation

Mutations frequently have outcomes that differ across individuals, even when these individuals are genetically identical and share a common environment. Moreover, individual microbial and mammalian cells can vary substantially in their proliferation rates, stress tolerance, and drug resistance, with important implications for the treatment of infections and cancer. To investigate the causes of cell-to-cell variation in proliferation, we used a high-throughput automated microscopy assay to quantify the impact of deleting >1500 genes in yeast. Mutations affecting mitochondria were particularly variable in their outcome. In both mutant and wild-type cells mitochondrial membrane potential - but not amount - varied substantially across individual cells and predicted cell-to-cell variation in proliferation, mutation outcome, stress tolerance, and resistance to a clinically used anti-fungal drug. These results suggest an important role for cell-to-cell variation in the state of an organelle in single cell phenotypic variation.Work in the lab of BL was supported by a European Research Council Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2017-89488-P and SEV-2012–0208), the AXA Research Fund, the Bettencourt Schueller Foundation, Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR SGR 1322), the EMBL Partnership, and the Generalitat/CERCA program. Work in the lab of LBC was supported by MINECO (BFU2015-68351-P) and AGAUR (2014 SGR 0974) and the Unidad de Excelencia Maria de Maeztu (MDM-2014–0370)

Microarray data

CEL files containing data collected for yeast experiment

Changes in gene expression predictably shift and switch genetic interactions

Non-additive interactions between mutations occur extensively and also change across conditions, making genetic prediction a difficult challenge. To better understand the plasticity of genetic interactions (epistasis), we combine mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, genetic interactions switch from positive (suppressive) to negative (enhancing) as the expression of the gene changes. These seemingly complicated changes can be predicted using a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. More generally, changes in gene expression should be expected to alter the effects of mutations and how they interact whenever the relationship between expression and a phenotype is nonlinear, which is the case for most genes. These results have important implications for understanding genotype-phenotype maps and illustrate how changes in genetic interactions can often-but not always-be predicted by hierarchical mechanistic models.This work was supported by a European Research Council (ERC) Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2017-89488-P and SEV-2012-0208), the Bettencourt Schueller Foundation, Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322.), and the CERCA Program/Generalitat de Catalunya. X. Li was supported in part by a fellowship from the Ramón Areces Foundation. We also acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and the Centro de Excelencia Severo Ochoa

Changes in gene expression predictably shift and switch genetic interactions

Non-additive interactions between mutations occur extensively and also change across conditions, making genetic prediction a difficult challenge. To better understand the plasticity of genetic interactions (epistasis), we combine mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, genetic interactions switch from positive (suppressive) to negative (enhancing) as the expression of the gene changes. These seemingly complicated changes can be predicted using a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. More generally, changes in gene expression should be expected to alter the effects of mutations and how they interact whenever the relationship between expression and a phenotype is nonlinear, which is the case for most genes. These results have important implications for understanding genotype-phenotype maps and illustrate how changes in genetic interactions can often-but not always-be predicted by hierarchical mechanistic models.This work was supported by a European Research Council (ERC) Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2017-89488-P and SEV-2012-0208), the Bettencourt Schueller Foundation, Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322.), and the CERCA Program/Generalitat de Catalunya. X. Li was supported in part by a fellowship from the Ramón Areces Foundation. We also acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and the Centro de Excelencia Severo Ochoa