1Despite over a billion years of evolutionary divergence, several thousand human genes possess 2 clearly identifiable orthologs in yeast, and many have undergone lineage-specific duplications in 3 one or both lineages. The ortholog conjecture postulates that orthologous genes between species 4 retain ancestral functions despite divergence over vast timescales, but duplicated genes will be free 5 to diverge in function. However, the retention of ancestral functions among co-orthologs between 6 species and within gene families has been difficult to test experimentally at scale. In order to 7 investigate how ancestral functions are retained or lost post-duplication, we systematically 8 replaced hundreds of essential yeast genes with their human orthologs from gene families that have 9 undergone lineage-specific duplications, including those with single duplications (one yeast gene 10 to two human genes, 1:2) or higher-order expansions (1:>2) in the human lineage. We observe a 11 variable pattern of replaceability across different ortholog classes, with an obvious trend towards 12 differential replaceability inside gene families, rarely observing replaceability by all members of 13 a family. We quantify the ability of various properties of the orthologs to predict replaceability, 14showing that in the case of 1:2 orthologs, replaceability is predicted largely by the divergence and 15 tissue-specific expression of the human co-orthologs, i.e. the human proteins that are less diverged 16 from their yeast counterpart and more ubiquitously expressed across human tissues more often 17 replace their single yeast ortholog. These trends were consistent with in silico simulations 18demonstrating that when only one ortholog is replaceable, it tends to be the least diverged of the 19 pair. Replaceability of yeast genes having more than two human co-orthologs was marked by 20 retention of orthologous interactions in functional or protein networks as well as by more ancestral 21 subcellular localization. Overall, we performed >400 human gene replaceability assays revealing 22 56 new human-yeast complementation pairs, thus opening up avenues to further functionally 23 characterize these human genes in a simplified organismal context. 24 25 and are distinguished from those related by speciation, termed orthologs [8,9]. Importantly, an 46 expanded family of paralogs in one lineage may be co-orthologs to a single gene in another lineage. 47How ancestral functions are partitioned, lost, or retained between paralogs and orthologs during 48 these duplication events is a major topic of study for evolutionary biology. To address these 49 questions, the ortholog conjecture was put forth, a major thesis stating that orthologs are more 50 likely to retain function between species than paralogs, which will tend to diverge in function after 51 duplication due to drift and relaxed selection [10,11]. This conjecture underlies common methods 52 by which functions are assigned to newly discovered or understudied genes in un-annotated 53 4 genomes ...

Most evolving populations are subdivided into multiple subpopulations connected to each other by varying levels of gene flow. However, how population structure and gene flow (i.e., migration) affect adaptive evolution is not well understood. Here, we studied the impact of migration on asexually reproducing evolving computer programs (digital organisms). We found that digital organisms evolve the highest fitness values at intermediate migration rates, and we tested three hypotheses that could potentially explain this observation: (i) migration promotes passage through fitness valleys, (ii) migration increases genetic variation, and (iii) migration reduces clonal interference through a process called "leapfrogging". We found that migration had no appreciable effect on the number of fitness valleys crossed and that genetic variation declined monotonously with increasing migration rates, instead of peaking at the optimal migration rate.However, the number of leapfrogging events, in which a superior beneficial mutation emerges on a genetic background that predates the previously best genotype in the population, did peak at the optimal migration rate. We thus conclude that in structured, asexual populations intermediate migration rates allow for optimal exploration of multiple, distinct fitness peaks, and thus yield the highest long-term adaptive success.

Synthetic biology has successfully advanced our ability to design complex, time-varying genetic circuits executing precisely specified gene expression patterns. However, such circuits usually require regulatory genes whose only purpose is to regulate the expression of other genes. When designing very small genetic constructs, such as viral genomes, we may want to avoid introducing such auxiliary gene products. To this end, here we demonstrate that varying only the placement and strengths of promoters, terminators, and RNase cleavage sites in a computational model of a bacteriophage genome is sufficient to achieve solutions to a variety of basic expression patterns. We discover these solutions by computationally evolving genomes to reproduce desired target expression patterns. Our approach shows non-trivial patterns can be evolved, including patterns in which the relative ordering of genes by abundance changes over time. We find that some patterns are easier to evolve than others, and different genomes that express comparable expression patterns may differ in their genetic architecture. Our work opens up a novel avenue to genome engineering via fine-tuning the balance of gene expression and gene degradation rates.