5 research outputs found
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Exploring protein fitness landscapes with new high-throughput technologies
The concept of a protein’s fitness landscape – an abstract space in which related sequences are close together and matched with their fitness – is a useful tool to visualize core principles of protein evolution. Acquiring a new function, for example the laboratory evolution of an enzyme to convert an industrially relevant substrate, can be understood as a stepwise climb through a fitness landscape, reaching higher fitness (or activity) with each step (or mutation). The valleys of such a space relate to the starting points of protein engineering campaigns. Understanding this area could enlighten principles of how proteins quickly adapt in nature and help to identify starting points with a high potential for evolution, a high ‘evolvability’, speeding up protein engineering. In this study, high-throughput technologies will be developed that enable the read-out of directed evolution on a large scale, tracking the exploration of the valley of a fitness landscape: the conversion of an amino acid- to amine dehydrogenase will be investigated as a model of enzyme evolvability with a drastic change of substrate specificity. A sensitive high-throughput screening assay as well as a comprehensive sequencing read-out will be required to establish the identity of selected variants during evolution. I will first generate and characterize three different but related starting points and test their initial evolvability. Stabilizing the starting point results in increased mutational robustness, broadening the range of accepted mutations. However, increased initial stability does not necessarily correlate to higher functional improvement, hinting at a nuanced view of evolvability. A sensitive high-throughput assay is necessary to verify the full potential of the starting points and study the early steps of evolution comprehensively. Broadly applicable ultrahigh-throughput assays of enzyme function, such as absorbance-activated droplet sorting, currently lack the sensitivity of more specific fluorescence-based or low-throughput counterparts. A universal approach to increase detectability in single cell-lysate microfluidic enzyme assays is established by amplifying the enzyme content per droplet more than 10-fold via homogeneous clonal cell growth. Clonal amplification enables the sensitive and precise detection of newly introduced amine dehydrogenase activities, a feat restricted in conventional assays by low initial activity and stability. To generate a truly complete view of directed evolution in a fitness landscape, however, an equally powerful sequencing read-out is necessary to identify all selected variants. Here, unique molecular identifiers are used to increase the accuracy of nanopore sequencing to levels that can reliably distinguish point mutations. I establish an inexpensive and straightforward long read amplicon sequencing workflow which is then applied to map the trajectories of two comparative long-term directed evolution campaigns. In the parallel evolution campaigns, initial beneficial mutations are exclusive to each starting point and lead to incompatible trajectories. Beneficial mutations are scarce and large improvements are unavailable until recombination occurs and a jump through the fitness landscape is realized. The recombined variant holds high evolvability and quickly evolves to take over the population and form the most successful lineages, indicating the power of recombination as a means to innovation in protein evolution. The tools established in this thesis can help protein engineers explore fitness landscapes more economically and comprehensively. Their application to mapping full trajectories of early adaptation uncovers differences in the evolvability of homologs, potentially aiding the identification of evolvable starting points as well as strategies to increase evolvability for efficient protein engineering in the future
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Growth amplification in ultrahigh-throughput microdroplet screening increases sensitivity of clonal enzyme assays and minimizes phenotypic variation.
Microfluidic ultrahigh-throughput screening of enzyme activities provides information on libraries with millions of variants in a day. Each individual library member is represented by a recombinant single cell, compartmentalised in an emulsion droplet, in which an activity assay is carried out. Key to the success of this approach is the precision and sensitivity of the assay. Assay quality is most profoundly challenged when initially weak, promiscuous activities are to be enhanced in early rounds of directed evolution or when entirely novel catalysts are to be identified from metagenomic sources. Implementation of measures to widen the dynamic range of clonal assays would increase the chances of finding and generating new biocatalysts. Here, we demonstrate that the assay sensitivity and DNA recovery can be improved by orders of magnitude by growth of initially singly compartmentalised cells in microdroplets. Homogeneous cell growth is achieved by continuous oxygenation and recombinant protein expression is regulated by diffusion of an inducer from the oil phase. Reaction conditions are adjusted by directed droplet coalescence to enable full control of buffer composition and kinetic incubation time, creating level playing field conditions for library selections. The clonal amplification multiplies the product readout because more enzyme is produced per compartment. At the same time, phenotypic variation is reduced by measuring monoclonal populations rather than single cells and recovery efficiency is increased. Consequently, this workflow increases the efficiency of lysate-based microfluidic enzyme assays and will make it easier for protein engineers to identify or evolve new enzymes for applications in synthetic and chemical biology.EPSRC, H2020, ER
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UMI-linked consensus sequencing enables phylogenetic analysis of directed evolution
Funder: EC | EC Seventh Framework Programm | FP7 People: Marie-Curie Actions (FP7-PEOPLE - Specific Programme "People" Implementing the Seventh Framework Programme of the European Community for Research, Technological Development and Demonstration Activities (2007 to 2013)); doi: https://doi.org/10.13039/100011264; Grant(s): 722610Abstract: The success of protein evolution campaigns is strongly dependent on the sequence context in which mutations are introduced, stemming from pervasive non-additive interactions between a protein’s amino acids (‘intra-gene epistasis’). Our limited understanding of such epistasis hinders the correct prediction of the functional contributions and adaptive potential of mutations. Here we present a straightforward unique molecular identifier (UMI)-linked consensus sequencing workflow (UMIC-seq) that simplifies mapping of evolutionary trajectories based on full-length sequences. Attaching UMIs to gene variants allows accurate consensus generation for closely related genes with nanopore sequencing. We exemplify the utility of this approach by reconstructing the artificial phylogeny emerging in three rounds of directed evolution of an amine dehydrogenase biocatalyst via ultrahigh throughput droplet screening. Uniquely, we are able to identify lineages and their founding variant, as well as non-additive interactions between mutations within a full gene showing sign epistasis. Access to deep and accurate long reads will facilitate prediction of key beneficial mutations and adaptive potential based on in silico analysis of large sequence datasets
Ultrahigh-Throughput Detection of Enzymatic Alcohol Dehydrogenase Activity in Microfluidic Droplets with a Direct Fluorogenic Assay.
The exploration of large DNA libraries of metagenomic or synthetic origin is greatly facilitated by ultrahigh-throughput assays that use monodisperse water-in-oil emulsion droplets as sequestered reaction compartments. Millions of samples can be generated and analysed in microfluidic devices at kHz speeds, requiring only micrograms of reagents. The scope of this powerful platform for the discovery of new sequence space is, however, hampered by the limited availability of assay substrates, restricting the functions and reaction types that can be investigated. Here, we broaden the scope of detectable biochemical transformations in droplet microfluidics by introducing the first fluorogenic assay for alcohol dehydrogenases (ADHs) in this format. We have synthesized substrates that release a pyranine fluorophore (8-hydroxy-1,3,6-pyrenetrisulfonic acid, HPTS) when enzymatic turnover occurs. Pyranine is well retained in droplets for >6 weeks (i. e. 14-times longer than fluorescein), avoiding product leakage and ensuring excellent assay sensitivity. Product concentrations as low as 100 nM were successfully detected, corresponding to less than one turnover per enzyme molecule on average. The potential of our substrate design was demonstrated by efficient recovery of a bona fide ADH with an >800-fold enrichment. The repertoire of droplet screening is enlarged by this sensitive and direct fluorogenic assay to identify dehydrogenases for biocatalytic applications.ERC, H2020 Marie-Curi
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UMI-linked consensus sequencing enables phylogenetic analysis of directed evolution
Funder: EC | EC Seventh Framework Programm | FP7 People: Marie-Curie Actions (FP7-PEOPLE - Specific Programme "People" Implementing the Seventh Framework Programme of the European Community for Research, Technological Development and Demonstration Activities (2007 to 2013)); doi: https://doi.org/10.13039/100011264; Grant(s): 722610Abstract: The success of protein evolution campaigns is strongly dependent on the sequence context in which mutations are introduced, stemming from pervasive non-additive interactions between a protein’s amino acids (‘intra-gene epistasis’). Our limited understanding of such epistasis hinders the correct prediction of the functional contributions and adaptive potential of mutations. Here we present a straightforward unique molecular identifier (UMI)-linked consensus sequencing workflow (UMIC-seq) that simplifies mapping of evolutionary trajectories based on full-length sequences. Attaching UMIs to gene variants allows accurate consensus generation for closely related genes with nanopore sequencing. We exemplify the utility of this approach by reconstructing the artificial phylogeny emerging in three rounds of directed evolution of an amine dehydrogenase biocatalyst via ultrahigh throughput droplet screening. Uniquely, we are able to identify lineages and their founding variant, as well as non-additive interactions between mutations within a full gene showing sign epistasis. Access to deep and accurate long reads will facilitate prediction of key beneficial mutations and adaptive potential based on in silico analysis of large sequence datasets