Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs

Mass Activated Droplet Sorting (MADS) Enables Highâ Throughput Screening of Enzymatic Reactions at Nanoliter Scale

Microfluidic droplet sorting enables the highâ throughput screening and selection of waterâ inâ oil microreactors at speeds and volumes unparalleled by traditional wellâ plate approaches. Most such systems sort using fluorescent reporters on modified substrates or reactions that are rarely industrially relevant. We describe a microfluidic system for highâ throughput sorting of nanoliter droplets based on direct detection using electrospray ionization mass spectrometry (ESIâ MS). Droplets are split, one portion is analyzed by ESIâ MS, and the second portion is sorted based on the MS result. Throughput of 0.7â samplesâ sâ 1 is achieved with 98â % accuracy using a selfâ correcting and adaptive sorting algorithm. We use the system to screen â 15â 000â samples in 6â h and demonstrate its utility by sorting 25â nL droplets containing transaminase expressed in vitro. Labelâ free ESIâ MS droplet screening expands the toolbox for droplet detection and recovery, improving the applicability of droplet sorting to protein engineering, drug discovery, and diagnostic workflows.A microfluidic system for sorting nanoliter droplets based on mass spectrometry is presented. Fully automated, labelâ free sorting at 0.7â samplesâ sâ 1 is achieved with 98â % accuracy. In vitro transcription and translation (ivTT) of a transaminase enzyme in ca.â 25â nL samples is demonstrated and samples are sorted on the basis of enzyme activity.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154315/1/anie201913203.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154315/2/anie201913203-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154315/3/anie201913203_am.pd

Mass Activated Droplet Sorting (MADS) Enables Highâ Throughput Screening of Enzymatic Reactions at Nanoliter Scale

Microfluidic droplet sorting enables the highâ throughput screening and selection of waterâ inâ oil microreactors at speeds and volumes unparalleled by traditional wellâ plate approaches. Most such systems sort using fluorescent reporters on modified substrates or reactions that are rarely industrially relevant. We describe a microfluidic system for highâ throughput sorting of nanoliter droplets based on direct detection using electrospray ionization mass spectrometry (ESIâ MS). Droplets are split, one portion is analyzed by ESIâ MS, and the second portion is sorted based on the MS result. Throughput of 0.7â samplesâ sâ 1 is achieved with 98â % accuracy using a selfâ correcting and adaptive sorting algorithm. We use the system to screen â 15â 000â samples in 6â h and demonstrate its utility by sorting 25â nL droplets containing transaminase expressed in vitro. Labelâ free ESIâ MS droplet screening expands the toolbox for droplet detection and recovery, improving the applicability of droplet sorting to protein engineering, drug discovery, and diagnostic workflows.Ein Mikrofluidiksystem zur Sortierung von Nanolitertröpfchen basierend auf Massenspektrometrie erreicht eine vollautomatische markierungsfreie Sortierung bei 0.7 Probenâ sâ 1 mit 98â % Genauigkeit. Die Inâ vitroâ Transkription und â Translation (ivTT) eines Transaminaseâ Enzyms in Proben von etwa 25â nL wird demonstriert, und die Proben werden nach ihrer Enzymaktivität sortiert.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154446/1/ange201913203-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154446/2/ange201913203.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154446/3/ange201913203_am.pd

A Biophysical Model of CRISPR/Cas9 Activity for Rational Design of Genome Editing and Gene Regulation

<div><p>The ability to precisely modify genomes and regulate specific genes will greatly accelerate several medical and engineering applications. The CRISPR/Cas9 (Type II) system binds and cuts DNA using guide RNAs, though the variables that control its on-target and off-target activity remain poorly characterized. Here, we develop and parameterize a system-wide biophysical model of Cas9-based genome editing and gene regulation to predict how changing guide RNA sequences, DNA superhelical densities, Cas9 and crRNA expression levels, organisms and growth conditions, and experimental conditions collectively control the dynamics of dCas9-based binding and Cas9-based cleavage at all DNA sites with both canonical and non-canonical PAMs. We combine statistical thermodynamics and kinetics to model Cas9:crRNA complex formation, diffusion, site selection, reversible R-loop formation, and cleavage, using large amounts of structural, biochemical, expression, and next-generation sequencing data to determine kinetic parameters and develop free energy models. Our results identify DNA supercoiling as a novel mechanism controlling Cas9 binding. Using the model, we predict Cas9 off-target binding frequencies across the lambdaphage and human genomes, and explain why Cas9’s off-target activity can be so high. With this improved understanding, we propose several rules for designing experiments for minimizing off-target activity. We also discuss the implications for engineering dCas9-based genetic circuits.</p></div

Apparent Cas9 binding energies to canonical and non-canonical PAM sites (kcal/mol).

<p>The energies are average values of all combinations in the first and fifth positions. (blue) The canonical PAM sites (NGGN) are bolded. N.B: no statistically significant binding. nt: nucleotide.</p

A summary of all studies used to estimate the model's parameters.

<p>A summary of all studies used to estimate the model's parameters.</p

Rational design of genome editing and gene regulation.

<p>(A) The dynamics of Cas9-based cleavage at DNA sites with either (blue) zero, (green) one, (red) two, or (cyan) three mismatches, comparing the effects of increasing guide RNA concentration by 10-fold, increasing the genome size by 2-fold, or increasing the cellular growth rate by 2-fold. (B) A sensitivity analysis shows how changing system parameters affect a DNA site’s steady-state cleavage efficiency in growing cells. (C) The dynamics of dCas9-based transcriptional repression (promoter activity) at DNA sites with either (blue) zero, (green) one, (red) two, or (cyan) three mismatches, performing the same comparisons as in A. (D) A sensitivity analysis shows how changing system parameters affect a DNA sites’ steady-state transcriptional repression (promoter activity) in growing cells. mm, mismatch.</p

Calculation of dCas9:crRNA_λ2 binding occupancy across 34,363 PAM sites on a λ-phage genome.

<p>(A) Model-calculated target binding free energies (ΔG_target) are shown across genome position, plotting only one in ten positions for improved visualization. Panels represent either the (top, blue) forward strand or (bottom, red) reverse strand of the λ-phage genome. The target binding free energies are the sum of (B) the free energy change when dCas9 binds to a PAM site (ΔG_PAM), (C) the free energy change during R-loop formation at PAM-proximal sites, compared to a perfectly complementary sequence (ΔΔG_exchange), and the free energy change as a result of varying DNA site superhelical density (ΔΔG_supercoiling). The major on-target site λ2 is denoted by stars. A major off-target site OS1 is denoted by crosses. Here, each mismatch in the crRNA and DNA site sequences contributes up to 0.78 kcal/mol to ΔΔG_exchange, depending on their distance from the PAM site. The λ-phage genome is assumed to have uniform DNA superhelical density. The model-calculated binding probabilities of (d)Cas9:crRNA_λ2 to all possible PAM sites are shown at (D) the initial time before any Cas9 activity or (F) after a 10 minute incubation with (d)Cas9:crRNA_λ2. (E) We show the model-calculated dynamics of (d)Cas9 binding occupancy at the (black line) λ2 DNA site, the (green line) major off-target site OS1, and a (inset) single off-target site with ΔG_target = 0 kcal/mol.</p

Parameterization of the model using <i>in vitro</i> data.

<p>Equimolar mixtures of Cas9 and crRNA (concentrations shown) were pre-incubated for 10 minutes, followed by the addition of target DNA and measuring the amount of cleaved DNA. Normalized cleaved DNA measurements (orange circles) using 25 nM negatively supercoiled plasmid DNA are compared to normalized model-calculated amounts of cleaved DNA (lines). Data points represent single measurements from Sternberg et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004724#pcbi.1004724.ref038" target="_blank">38</a>].</p

Model predictions for human genome editing.

<p>(A) Model-calculated distributions show the numbers of human genome DNA sites that will be cleaved with varying efficiencies when using a LTR-B crRNA with either (yellow) baseline, (blue) 10-fold lower, or (green) 10-fold higher Cas9 and crRNA concentrations. (B) The expected number of off-target indel mutations when counting sites with cleavage efficiencies higher than a cut-off value. (C) The required next-generation sequencing coverage to identify the expected number of off-target indel mutations with 99% certainty. Colors same as in A. (D) The model-calculated dynamics of human genome modification under the same three scenarios, comparing (solid lines) on-target cleavage versus (dashed lines) the ratio between on-target and total off-target cleavage (specificity).</p