CREEPY: CRISPR-mediated editing of synthetic episomes in yeast.

Use of synthetic genomics to design and build 'big' DNA has revolutionized our ability to answer fundamental biological questions by employing a bottom-up approach. Saccharomyces cerevisiae, or budding yeast, has become the major platform to assemble large synthetic constructs thanks to its powerful homologous recombination machinery and the availability of well-established molecular biology techniques. However, introducing designer variations to episomal assemblies with high efficiency and fidelity remains challenging. Here we describe CRISPR Engineering of EPisomes in Yeast, or CREEPY, a method for rapid engineering of large synthetic episomal DNA constructs. We demonstrate that CRISPR editing of circular episomes presents unique challenges compared to modifying native yeast chromosomes. We optimize CREEPY for efficient and precise multiplex editing of >100 kb yeast episomes, providing an expanded toolkit for synthetic genomics.

A versatile platform for locus-scale genome rewriting and verification.

Routine rewriting of loci associated with human traits and diseases would facilitate their functional analysis. However, existing DNA integration approaches are limited in terms of scalability and portability across genomic loci and cellular contexts. We describe Big-IN, a versatile platform for targeted integration of large DNAs into mammalian cells. CRISPR/Cas9-mediated targeting of a landing pad enables subsequent recombinase-mediated delivery of variant payloads and efficient positive/negative selection for correct clones in mammalian stem cells. We demonstrate integration of constructs up to 143 kb, and an approach for one-step scarless delivery. We developed a staged pipeline combining PCR genotyping and targeted capture sequencing for economical and comprehensive verification of engineered stem cells. Our approach should enable combinatorial interrogation of genomic functional elements and systematic locus-scale analysis of genome function.

Humanization of yeast genes with multiple human orthologs reveals functional divergence between paralogs.

Despite over a billion years of evolutionary divergence, several thousand human genes possess clearly identifiable orthologs in yeast, and many have undergone lineage-specific duplications in one or both lineages. These duplicated genes may have been free to diverge in function since their expansion, and it is unclear how or at what rate ancestral functions are retained or partitioned among co-orthologs between species and within gene families. Thus, in order to investigate how ancestral functions are retained or lost post-duplication, we systematically replaced hundreds of essential yeast genes with their human orthologs from gene families that have undergone lineage-specific duplications, including those with single duplications (1 yeast gene to 2 human genes, 1:2) or higher-order expansions (1:>2) in the human lineage. We observe a variable pattern of replaceability across different ortholog classes, with an obvious trend toward differential replaceability inside gene families, and rarely observe replaceability by all members of a family. We quantify the ability of various properties of the orthologs to predict replaceability, showing that in the case of 1:2 orthologs, replaceability is predicted largely by the divergence and tissue-specific expression of the human co-orthologs, i.e., the human proteins that are less diverged from their yeast counterpart and more ubiquitously expressed across human tissues more often replace their single yeast ortholog. These trends were consistent with in silico simulations demonstrating that when only one ortholog can replace its corresponding yeast equivalent, it tends to be the least diverged of the pair. Replaceability of yeast genes having more than 2 human co-orthologs was marked by retention of orthologous interactions in functional or protein networks as well as by more ancestral subcellular localization. Overall, we performed >400 human gene replaceability assays, revealing 50 new human-yeast complementation pairs, thus opening up avenues to further functionally characterize these human genes in a simplified organismal context.

The Many Nuanced Evolutionary Consequences of Duplicated Genes.

Gene duplication is seen as a major source of structural and functional divergence in genome evolution. Under the conventional models of sub or neofunctionalization, functional changes arise in one of the duplicates after duplication. However, we suggest here that the presence of a duplicated gene can result in functional changes to its interacting partners. We explore this hypothesis by in silico evolution of a heterodimer when one member of the interacting pair is duplicated. We examine how a range of selection pressures and protein structures leads to differential patterns of evolutionary divergence. We find that a surprising number of distinct evolutionary trajectories can be observed even in a simple three member system. Further, we observe that selection to correct dosage imbalance can affect the evolution of the initial function in several unexpected ways. For example, if a duplicate is under selective pressure to avoid binding its original binding partner, this can lead to changes in the binding interface of a nonduplicated interacting partner to exclude the duplicate. Hence, independent of the fate of the duplicate, its presence can impact how the original function operates. Additionally, we introduce a conceptual framework to describe how interacting partners cope with dosage imbalance after duplication. Contextualizing our results within this framework reveals that the evolutionary path taken by a duplicate's interacting partners is highly stochastic in nature. Consequently, the fate of duplicate genes may not only be controlled by their own ability to accumulate mutations but also by how interacting partners cope with them.

Prediction of gene-phenotype associations in humans, mice, and plants using phenologs.

BACKGROUND: Phenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such "orthologous phenotypes," or "phenologs," are examples of deep homology, and may be used to predict additional candidate disease genes. RESULTS: In this work, we develop an unsupervised algorithm for ranking phenolog-based candidate disease genes through the integration of predictions from the k nearest neighbor phenologs, comparing classifiers and weighting functions by cross-validation. We also improve upon the original method by extending the theory to paralogous phenotypes. Our algorithm makes use of additional phenotype data--from chicken, zebrafish, and E. coli, as well as new datasets for C. elegans--establishing that several types of annotations may be treated as phenotypes. We demonstrate the use of our algorithm to predict novel candidate genes for human atrial fibrillation (such as HRH2, ATP4A, ATP4B, and HOPX) and epilepsy (e.g., PAX6 and NKX2-1). We suggest gene candidates for pharmacologically-induced seizures in mouse, solely based on orthologous phenotypes from E. coli. We also explore the prediction of plant gene-phenotype associations, as for the Arabidopsis response to vernalization phenotype. CONCLUSIONS: We are able to rank gene predictions for a significant portion of the diseases in the Online Mendelian Inheritance in Man database. Additionally, our method suggests candidate genes for mammalian seizures based only on bacterial phenotypes and gene orthology. We demonstrate that phenotype information may come from diverse sources, including drug sensitivities, gene ontology biological processes, and in situ hybridization annotations. Finally, we offer testable candidates for a variety of human diseases, plant traits, and other classes of phenotypes across a wide array of species.

Rapid, scalable, combinatorial genome engineering by marker-less enrichment and recombination of genetically engineered loci in yeast.

A major challenge to rationally building multi-gene processes in yeast arises due to the combinatorics of combining all of the individual edits into the same strain. Here, we present a precise and multi-site genome editing approach that combines all edits without selection markers using CRISPR-Cas9. We demonstrate a highly efficient gene drive that selectively eliminates specific loci by integrating CRISPR-Cas9-mediated double-strand break (DSB) generation and homology-directed recombination with yeast sexual assortment. The method enables marker-less enrichment and recombination of genetically engineered loci (MERGE). We show that MERGE converts single heterologous loci to homozygous loci at ∼100% efficiency, independent of chromosomal location. Furthermore, MERGE is equally efficient at converting and combining multiple loci, thus identifying compatible genotypes. Finally, we establish MERGE proficiency by engineering a fungal carotenoid biosynthesis pathway and most of the human α-proteasome core into yeast. Therefore, MERGE lays the foundation for scalable, combinatorial genome editing in yeast.

EUAdb: A resource for COVID-19 test development and comparison.

Due to the sheer number of COVID-19 (coronavirus disease 2019) cases there is a need for increased world-wide SARS-CoV-2 testing capability that is both efficient and effective. Having open and easy access to detailed information about these tests, their sensitivity, the types of samples they use, etc. would be highly useful to ensure their reproducibility, to help clients compare and decide which tests would be best suited for their applications, and to avoid costs of reinventing similar or identical tests. Additionally, this resource would provide a means of comparing the many innovative diagnostic tools that are currently being developed in order to provide a foundation of technologies and methods for the rapid development and deployment of tests for future emerging diseases. Such a resource might thus help to avert the delays in testing and screening that was observed in the early stages of the pandemic and plausibly led to more COVID-19-related deaths than necessary. We aim to address these needs via a relational database containing standardized ontology and curated data about COVID-19 diagnostic tests that have been granted Emergency Use Authorizations (EUAs) by the FDA (US Food and Drug Administration). Simple queries of this actively growing database demonstrate considerable variation among these tests with respect to sensitivity (limits of detection, LoD), controls and targets used, criteria used for calling results, sample types, reagents and instruments, and quality and amount of information provided.

Protein abundances are more conserved than mRNA abundances across diverse taxa.

Proteins play major roles in most biological processes; as a consequence, protein expression levels are highly regulated. While extensive post-transcriptional, translational and protein degradation control clearly influence protein concentration and functionality, it is often thought that protein abundances are primarily determined by the abundances of the corresponding mRNAs. Hence surprisingly, a recent study showed that abundances of orthologous nematode and fly proteins correlate better than their corresponding mRNA abundances. We tested if this phenomenon is general by collecting and testing matching large-scale protein and mRNA expression data sets from seven different species: two bacteria, yeast, nematode, fly, human, and rice. We find that steady-state abundances of proteins show significantly higher correlation across these diverse phylogenetic taxa than the abundances of their corresponding mRNAs (p=0.0008, paired Wilcoxon). These data support the presence of strong selective pressure to maintain protein abundances during evolution, even when mRNA abundances diverge.

Systematic Humanization of the Yeast Cytoskeleton Discerns Functionally Replaceable from Divergent Human Genes.

Many gene families have been expanded by gene duplications along the human lineage, relative to ancestral opisthokonts, but the extent to which the duplicated genes function similarly is understudied. Here, we focused on structural cytoskeletal genes involved in critical cellular processes, including chromosome segregation, macromolecular transport, and cell shape maintenance. To determine functional redundancy and divergence of duplicated human genes, we systematically humanized the yeast actin, myosin, tubulin, and septin genes, testing ∼81% of human cytoskeletal genes across seven gene families for their ability to complement a growth defect induced by inactivation or deletion of the corresponding yeast ortholog. In five of seven families-all but α-tubulin and light myosin, we found at least one human gene capable of complementing loss of the yeast gene. Despite rescuing growth defects, we observed differential abilities of human genes to rescue cell morphology, meiosis, and mating defects. By comparing phenotypes of humanized strains with deletion phenotypes of their interaction partners, we identify instances of human genes in the actin and septin families capable of carrying out essential functions, but failing to fully complement the cytoskeletal roles of their yeast orthologs, thus leading to abnormal cell morphologies. Overall, we show that duplicated human cytoskeletal genes appear to have diverged such that only a few human genes within each family are capable of replacing the essential roles of their yeast orthologs. The resulting yeast strains with humanized cytoskeletal components now provide surrogate platforms to characterize human genes in simplified eukaryotic contexts.

EUAdb: a resource for COVID-19 test development.

Due to the sheer number of COVID-19 (coronavirus disease 2019) cases, the prevalence of asymptomatic cases and the fact that undocumented cases appear to be significant for transmission of the causal virus, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), there is an urgent need for increased SARS-CoV-2 testing capability that is both efficient and effective1. In response to the growing threat of the COVID-19 pandemic in February, 2020, the FDA (US Food and Drug Administration) began issuing Emergency Use Authorizations (EUAs) to laboratories and commercial manufacturers for the development and implementation of diagnostic tests[1]. So far, the gold standard assay for SARS-CoV-2 detection is the RT-qPCR (real-time quantitative polymerase chain reaction) test[2]. However, the authorized RT-qPCR test protocols vary widely, not only in the reagents, controls, and instruments they use, but also in the SARS-CoV-2 genes they target, what results constitute a positive SARS-CoV-2 infection, and their limit of detection (LoD). The FDA has provided a web site that lists most of the tests that have been issued EUAs, along with links to the authorization letters and summary documents describing these tests[1]. However, it is very challenging to use this site to compare or replicate these tests for a variety of reasons. First, at least 12 of 18 tests for EUA submissions made prior to March 31, 2020, are not listed there. To our knowledge, no EUAs have been issued for these applications. Second, the data are not standardized and are only provided as longhand prose in the summary documents. Third, some details (e.g. primer sequences) are absent from several of the test descriptions. Fourth, for tests provided by commercial manufacturers, summary documents are completely missing. To address at least the first three issues, we have developed a database, EUAdb (EUAdb.org), that holds standardized information about EUA-issued tests and is focused on RT-qPCR diagnostic tests, or "high complexity molecular-based laboratory developed tests"[1]. By providing a standardized ontology and curated data in a relational architecture, we seek to facilitate comparability and reproducibility, with the ultimate goal of consistent, universal and high-quality testing nationwide. Here, we document the basics of the EUAdb data architecture and simple data queries. The source files can be provided to anyone who wants to modify the database for his/her own research purposes. We ask that the original source of the files be made clear and that the database not be used in its original or modified forms for commercial purposes.

Erratum: Correction Notice: Single-step Precision Genome Editing in Yeast Using CRISPR-Cas9.

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Big DNA as a tool to dissect an age-related macular degeneration-associated haplotype.

Age-related Macular Degeneration (AMD) is a leading cause of blindness in the developed world, especially in aging populations, and is therefore an important target for new therapeutic development. Recently, there have been several studies demonstrating strong associations between AMD and sites of heritable genetic variation at multiple loci, including a highly significant association at 10q26. The 10q26 risk region contains two genes, HTRA1 and ARMS2, both of which have been separately implicated as causative for the disease, as well as dozens of sites of non-coding variation. To date, no studies have successfully pinpointed which of these variant sites are functional in AMD, nor definitively identified which genes in the region are targets of such regulatory variation. In order to efficiently decipher which sites are functional in AMD phenotypes, we describe a general framework for combinatorial assembly of large 'synthetic haplotypes' along with delivery to relevant disease cell types for downstream functional analysis. We demonstrate the successful and highly efficient assembly of a first-draft 119kb wild-type 'assemblon' covering the HTRA1/ARMS2 risk region. We further propose the parallelized assembly of a library of combinatorial variant synthetic haplotypes covering the region, delivery and analysis of which will identify functional sites and their effects, leading to an improved understanding of AMD development. We anticipate that the methodology proposed here is highly generalizable towards the difficult problem of identifying truly functional variants from those discovered via GWAS or other genetic association studies.

Single-step Precision Genome Editing in Yeast Using CRISPR-Cas9.

Genome modification in budding yeast has been extremely successful largely due to its highly efficient homology-directed DNA repair machinery. Several methods for modifying the yeast genome have previously been described, many of them involving at least two-steps: insertion of a selectable marker and substitution of that marker for the intended modification. Here, we describe a CRISPR-Cas9 mediated genome editing protocol for modifying any yeast gene of interest (either essential or nonessential) in a single-step transformation without any selectable marker. In this system, the Cas9 nuclease creates a double-stranded break at the locus of choice, which is typically lethal in yeast cells regardless of the essentiality of the targeted locus due to inefficient non-homologous end-joining repair. This lethality results in efficient repair via homologous recombination using a repair template derived from PCR. In cases involving essential genes, the necessity of editing the genomic lesion with a functional allele serves as an additional layer of selection. As a motivating example, we describe the use of this strategy in the replacement of HEM2, an essential yeast gene, with its corresponding human ortholog ALAD.

Systematic bacterialization of yeast genes identifies a near-universally swappable pathway.

Eukaryotes and prokaryotes last shared a common ancestor ~2 billion years ago, and while many present-day genes in these lineages predate this divergence, the extent to which these genes still perform their ancestral functions is largely unknown. To test principles governing retention of ancient function, we asked if prokaryotic genes could replace their essential eukaryotic orthologs. We systematically replaced essential genes in yeast by their 1:1 orthologs from Escherichia coli. After accounting for mitochondrial localization and alternative start codons, 31 out of 51 bacterial genes tested (61%) could complement a lethal growth defect and replace their yeast orthologs with minimal effects on growth rate. Replaceability was determined on a pathway-by-pathway basis; codon usage, abundance, and sequence similarity contributed predictive power. The heme biosynthesis pathway was particularly amenable to inter-kingdom exchange, with each yeast enzyme replaceable by its bacterial, human, or plant ortholog, suggesting it as a near-universally swappable pathway.

Efforts to make and apply humanized yeast.

Despite a billion years of divergent evolution, the baker's yeast Saccharomyces cerevisiae has long proven to be an invaluable model organism for studying human biology. Given its tractability and ease of genetic manipulation, along with extensive genetic conservation with humans, it is perhaps no surprise that researchers have been able to expand its utility by expressing human proteins in yeast, or by humanizing specific yeast amino acids, proteins or even entire pathways. These methods are increasingly being scaled in throughput, further enabling the detailed investigation of human biology and disease-specific variations of human genes in a simplified model organism.

Evolution. Systematic humanization of yeast genes reveals conserved functions and genetic modularity.

To determine whether genes retain ancestral functions over a billion years of evolution and to identify principles of deep evolutionary divergence, we replaced 414 essential yeast genes with their human orthologs, assaying for complementation of lethal growth defects upon loss of the yeast genes. Nearly half (47%) of the yeast genes could be successfully humanized. Sequence similarity and expression only partly predicted replaceability. Instead, replaceability depended strongly on gene modules: Genes in the same process tended to be similarly replaceable (e.g., sterol biosynthesis) or not (e.g., DNA replication initiation). Simulations confirmed that selection for specific function can maintain replaceability despite extensive sequence divergence. Critical ancestral functions of many essential genes are thus retained in a pathway-specific manner, resilient to drift in sequences, splicing, and protein interfaces.