A drug stabilizable GAL80ds for conditional control of gene expression via GAL4-UAS and CRISPR-Cas9 systems in Drosophila.

The binary GAL4-UAS expression system has been widely used in Drosophila to achieve tissue-specific expression of genes. To further allow for simultaneous spatial and conditional control of gene expression in existing GAL4 expression lines backgrounds, temperature and chemical controllable GAL80 variants have been engineered. Here we add a new drug stabilizable GAL80ds variant, by fusing it to a low-background DHFR-22-DD. We first quantify both single (DD-GAL80) and double (DD-GAL80-DD) architectures and show varied background and activation levels. Next, we demonstrate the utility of GAL80ds Drosophila line to regulate a cell death gene ectopically, in a drug-dependent manner, by utilizing an existing tissue-specific GAL4 driver that regulates the expression of a cell death gene under a UAS. Finally, we showcase the usefulness of GAL80ds in tight drug-mediated regulation of a target gene, from an endogenous locus, by utilizing an existing tissue-specific GAL4 to drive the expression of a dead Cas9 variant fused to the transcriptional coactivator nejire, under a UAS and in gRNA lines. Overall, these new GAL80ds lines expand the use of the wide variety of existing tissue-specific GAL4 and gene-specific gRNA lines. This enables conditional control of genes, both ectopically and endogenously, for a broad array of gene expression control applications.

Engineered transcription-associated Cas9 targeting in eukaryotic cells.

DNA targeting Class 2 CRISPR-Cas effector nucleases, including the well-studied Cas9 proteins, evolved protospacer-adjacent motif (PAM) and guide RNA interactions that sequentially license their binding and cleavage activities at protospacer target sites. Both interactions are nucleic acid sequence specific but function constitutively; thus, they provide intrinsic spatial control over DNA targeting activities but naturally lack temporal control. Here we show that engineered Cas9 fusion proteins which bind to nascent RNAs near a protospacer can facilitate spatiotemporal coupling between transcription and DNA targeting at that protospacer: Transcription-associated Cas9 Targeting (TraCT). Engineered TraCT is enabled when suboptimal PAM interactions limit basal activity in vivo and when one or more nascent RNA substrates are still tethered to the actively transcribing target DNA in cis. We further show that this phenomenon can be exploited for selective editing at one of two identical targets in distinct gene loci, or, in diploid allelic loci that are differentially transcribed. Our work demonstrates that temporal control over Cas9's targeting activity at specific DNA sites may be engineered without modifying Cas9's core domains and guide RNA components or their expression levels. More broadly, it establishes RNA binding in cis as a mechanism that can conditionally stimulate CRISPR-Cas DNA targeting in eukaryotes.

A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Cys2His2 zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence.

Predicting Evolutionary Constraints by Identifying Conflicting Demands in Regulatory Networks.

Gene regulation networks allow organisms to adapt to diverse environmental niches. However, the constraints underlying the evolution of gene regulation remain ill defined. Here, we show that partial order-a concept that ranks network output levels as a function of different input signals-identifies such constraints. We tested our predictions by experimentally evolving an engineered signal-integrating network in multiple environments. We find that populations: (1) expand in fitness space along the Pareto-optimal front associated with conflicts in regulatory demands, by fine-tuning binding affinities within the network, and (2) expand beyond the Pareto-optimal front through changes in the network structure. Our constraint predictions are based only on partial order and do not require information on the network architecture or underlying genetics. Overall, our findings show that limited knowledge of current regulatory phenotypes can provide predictions on future evolutionary constraints.

Predicting Evolution Using Regulatory Architecture.

The limits of evolution have long fascinated biologists. However, the causes of evolutionary constraint have remained elusive due to a poor mechanistic understanding of studied phenotypes. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology models with population and single-cell quantification and with new genetic tools, and they have been applied to a range of complex cellular functions and engineered networks. In this article, we review these developments, which are revealing the mechanistic causes of epistasis at different levels of biological organization-in molecular recognition, within a single regulatory network, and between different networks-providing first indications of predictable features of evolutionary constraint.

Drug-Inducible Control of Lethality Genes: A Low Background Destabilizing Domain Architecture Applied to the Gal4-UAS System in Drosophila.

Destabilizing domains (DDs) are genetic tags that conditionally control the level of abundance of proteins-of-interest (POI) with specific stabilizing small-molecule drugs, rapidly and reversibly, in a wide variety of organisms. The amount of the DD-tagged fusion protein directly impacts its molecular function. Hence, it is important that the background levels be tightly regulated in the absence of any drug. This is especially true for classes of proteins that function at extremely low levels, such as lethality genes involved in tissue development and certain transcriptional activator proteins. Here, we establish the uninduced background and induction levels for two widely used DDs (FKBP and DHFR) by developing an accurate quantification method. We show that both DDs exhibit functional background levels in the absence of a drug, but each to a different degree. To overcome this limitation, we systematically test a double architecture for these DDs (DD-POI-DD) that completely suppresses the protein's function in an uninduced state, while allowing tunable functional levels upon adding a drug. As an example, we generate a drug-stabilizable Gal4 transcriptional activator with extremely low background levels. We show that this functions in vivo in the widely used Gal4-UAS bipartite expression system in Drosophila melanogaster. By regulating a cell death gene, we demonstrate that only the low background double architecture enables tight regulation of the lethal phenotype in vivo. These improved tools will enable applications requiring exceptionally tight control of protein function in living cells and organisms.

Sign epistasis caused by hierarchy within signalling cascades.

Sign epistasis is a central evolutionary constraint, but its causal factors remain difficult to predict. Here we use the notion of parameterised optima to explain epistasis within a signalling cascade, and test these predictions in Escherichia coli. We show that sign epistasis arises from the benefit of tuning phenotypic parameters of cascade genes with respect to each other, rather than from their complex and incompletely known genetic bases. Specifically, sign epistasis requires only that the optimal phenotypic parameters of one gene depend on the phenotypic parameters of another, independent of other details, such as activating or repressing nature, position within the cascade, intra-genic pleiotropy or genotype. Mutational effects change sign more readily in downstream genes, indicating that optimising downstream genes is more constrained. The findings show that sign epistasis results from the inherent upstream-downstream hierarchy between signalling cascade genes, and can be addressed without exhaustive genotypic mapping.

Origin of a folded repeat protein from an intrinsically disordered ancestor.

Repetitive proteins are thought to have arisen through the amplification of subdomain-sized peptides. Many of these originated in a non-repetitive context as cofactors of RNA-based replication and catalysis, and required the RNA to assume their active conformation. In search of the origins of one of the most widespread repeat protein families, the tetratricopeptide repeat (TPR), we identified several potential homologs of its repeated helical hairpin in non-repetitive proteins, including the putatively ancient ribosomal protein S20 (RPS20), which only becomes structured in the context of the ribosome. We evaluated the ability of the RPS20 hairpin to form a TPR fold by amplification and obtained structures identical to natural TPRs for variants with 2-5 point mutations per repeat. The mutations were neutral in the parent organism, suggesting that they could have been sampled in the course of evolution. TPRs could thus have plausibly arisen by amplification from an ancestral helical hairpin.

An improved Escherichia coli strain to host gene regulatory networks involving both the AraC and LacI inducible transcription factors.

Many of the gene regulatory networks used within the field of synthetic biology have extensively employed the AraC and LacI inducible transcription factors. However, there is no Escherichia coli strain that provides a proper background to use both transcription factors simultaneously. We have engineered an improved E. coli strain by knocking out the endogenous lacI from a strain optimal for AraC containing networks, and thoroughly characterized the strain both at molecular and functional levels. We further show that it enables the gradual and independent induction of both AraC and LacI in a simultaneous manner. This construct will be of direct use for various synthetic biology applications.

Multiple peaks and reciprocal sign epistasis in an empirically determined genotype-phenotype landscape.

Insight into the ruggedness of adaptive landscapes is central to understanding the mechanisms and constraints that shape the course of evolution. While empirical data on adaptive landscapes remain scarce, a handful of recent investigations have revealed genotype-phenotype and genotype-fitness landscapes that appeared smooth and single peaked. Here, we used existing in vivo measurements on lac repressor and operator mutants in Escherichia coli to reconstruct the genotype-phenotype map that details the repression value of this regulatory system as a function of two key repressor residues and four key operator base pairs. We found that this landscape is multipeaked, harboring in total 19 distinct optima. Analysis showed that all direct evolutionary pathways between peaks involve significant dips in the repression value. Consistent with earlier predictions, we found reciprocal sign epistatic interactions at the repression minimum of the most favorable paths between two peaks. These results suggest that the occurrence of multiple peaks and reciprocal epistatic interactions may be a general feature in coevolving systems like the repressor-operator pair studied here.

Revealing evolutionary pathways by fitness landscape reconstruction.

The concept of epistasis has since long been used to denote non-additive fitness effects of genetic changes and has played a central role in understanding the evolution of biological systems. Owing to an array of novel experimental methodologies, it has become possible to experimentally determine epistatic interactions as well as more elaborate genotype-fitness maps. These data have opened up the investigation of a host of long-standing questions in evolutionary biology, such as the ruggedness of fitness landscapes and the accessibility of mutational trajectories, the evolution of sex, and the origin of robustness and modularity. Here we review this recent and timely marriage between systems biology and evolutionary biology, which holds the promise to understand evolutionary dynamics in a more mechanistic and predictive manner.