Periodic Reporting for period 1 - YEFF (Comprehensive analysis of yeast expression-fitness functions (EFFs)) Reporting period: 2018-05-01 to 2020-04-30 Summary of the context and overall objectives of the project The project focused on a systematic understanding of how changes in the functional abundance of genes affect organismal fitness. The proper abundance or expression of gene products is critical to virtually all cellular processes, from DNA replication and cell growth, to signalling and metabolism. So far, a systematic understanding of how both changes to the average as well as time-varying abundance of protein products can affect cellular processes and thus organismal fitness is missing. Investigating this problem is at is core fundamental science, as it is trying to address general mechanisms of how gene expression is regulated and has evolved. However, it is easy to see, how a better understanding of this problem also has translational aspects, such as in disease mechanisms or applications to synthetic biology (the engineering of organisms).The overall objectives are:- Quantifying how expression changes in many different genes affect organismal fitness- Generating insights into the core mechanisms of how gene expression changes affect fitness- Systematic classification of genes into groups with similar effect characteristics- Gaining insights into how the regulation of gene expression evolves Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far To explore how gene expression changes affect organismal fitness, we computationally analysed previously published experimental data (Keren et al., Cell 2016). These data recorded how yeast growth changes when 80 different genes were driven by 120 different promoters. Additionally, for these promoters the average and time-varying expression strength was already known (Sharon et al.,Genome Research 2014). Data were integrated to reconstruct continuous landscapes of yeast fitness as a function of average and time-varying expression of each genes using a mathematical technique called Gaussian smoothing. We used machine learning approaches to show that, while each landscape looks unique, all landscapes originate from the combination of two principal topologies: the effect of lowered fitness if the gene is expressed at too low copy numbers, or the effect of lowered fitness if the gene is expressed at too high copy numbers. Each gene has a unique combination of these two fitness topologies, depending on its cellular function, and thus a unique fitness landscape. Moreover, integrating over all analysed landscapes we could show that expression noise itself is detrimental to yeast fitness, thus highlighting the adverse effects of the fundamental randomness of all molecular motion on life. Finally, we used stochastic evolutionary simulations to explore possible scenarios for the evolution of gene expression parameters, revealing that for genes with certain landscape topologies, only a combination of transcriptional and post-transcriptional changes can quickly optimize gene expression parameters. This work was disseminated in Nature Communications (Schmiedel et al., Nature Communications 2019) and presented in a talk at the Evolutionary Systems Biology Conference in Cambridge, UK in 2018. Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far) The work contributed to the state of the art in three aspects: - It was the first systematic analysis across many genes of how the average and time-varying aspects of gene expression interact to impact organismal fitness- It contributed the first systematic evidence for the long-hold assumption that noisy gene expression is detrimental to organisms- It extended models of gene expression evolution by the dimension of time-varying gene expression, showing that the interplay between average and time-varying expression create specific evolutionary scenarios for how gene expression evolution might proceed reconstructing fitness landscapes in mean expression and noise space
