Strategies to identify and edit improvements in synthetic genome segments episomally.

Genome engineering projects often utilize bacterial artificial chromosomes (BACs) to carry multi-kilobase DNA segments at low copy number. However, all stages of whole-genome engineering have the potential to impose mutations on the synthetic genome that can reduce or eliminate the fitness of the final strain. Here, we describe improvements to a multiplex automated genome engineering (MAGE) protocol to improve recombineering frequency and multiplexability. This protocol was applied to recoding an Escherichia coli strain to replace seven codons with synonymous alternatives genome wide. Ten 44 402-47 179 bp de novo synthesized DNA segments contained in a BAC from the recoded strain were unable to complement deletion of the corresponding 33-61 wild-type genes using a single antibiotic resistance marker. Next-generation sequencing (NGS) was used to identify 1-7 non-recoding mutations in essential genes per segment, and MAGE in turn proved a useful strategy to repair these mutations on the recoded segment contained in the BAC when both the recoded and wild-type copies of the mutated genes had to exist by necessity during the repair process. Finally, two web-based tools were used to predict the impact of a subset of non-recoding missense mutations on strain fitness using protein structure and function calls.

Engineering posttranslational proofreading to discriminate nonstandard amino acids.

Incorporation of nonstandard amino acids (nsAAs) leads to chemical diversification of proteins, which is an important tool for the investigation and engineering of biological processes. However, the aminoacyl-tRNA synthetases crucial for this process are polyspecific in regard to nsAAs and standard amino acids. Here, we develop a quality control system called "posttranslational proofreading" to more accurately and rapidly evaluate nsAA incorporation. We achieve this proofreading by hijacking a natural pathway of protein degradation known as the N-end rule, which regulates the lifespan of a protein based on its amino-terminal residue. We find that proteins containing certain desired N-terminal nsAAs have much longer half-lives compared with those proteins containing undesired amino acids. We use the posttranslational proofreading system to further evolve a Methanocaldococcus jannaschii tyrosyl-tRNA synthetase (TyrRS) variant and a tRNATyr species for improved specificity of the nsAA biphenylalanine in vitro and in vivo. Our newly evolved biphenylalanine incorporation machinery enhances the biocontainment and growth of genetically engineered Escherichia coli strains that depend on biphenylalanine incorporation. Finally, we show that our posttranslational proofreading system can be designed for incorporation of other nsAAs by rational engineering of the ClpS protein, which mediates the N-end rule. Taken together, our posttranslational proofreading system for in vivo protein sequence verification presents an alternative paradigm for molecular recognition of amino acids and is a major advance in our ability to accurately expand the genetic code.

Optimizing complex phenotypes through model-guided multiplex genome engineering.

We present a method for identifying genomic modifications that optimize a complex phenotype through multiplex genome engineering and predictive modeling. We apply our method to identify six single nucleotide mutations that recover 59% of the fitness defect exhibited by the 63-codon E. coli strain C321.∆A. By introducing targeted combinations of changes in multiplex we generate rich genotypic and phenotypic diversity and characterize clones using whole-genome sequencing and doubling time measurements. Regularized multivariate linear regression accurately quantifies individual allelic effects and overcomes bias from hitchhiking mutations and context-dependence of genome editing efficiency that would confound other strategies.

Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast.

RNA Polymerase II pauses and backtracks during transcription, with many consequences for gene expression and cellular physiology. Here, we show that the energy required to melt double-stranded nucleic acids in the transcription bubble predicts pausing in Saccharomyces cerevisiae far more accurately than nucleosome roadblocks do. In addition, the same energy difference also determines when the RNA polymerase backtracks instead of continuing to move forward. This data-driven model corroborates-in a genome wide and quantitative manner-previous evidence that sequence-dependent thermodynamic features of nucleic acids influence both transcriptional pausing and backtracking.

Population structure and coalescence in pedigrees: Comparisons to the structured coalescent and a framework for inference.

Contrary to what is often assumed in population genetics, independently segregating loci do not have completely independent ancestries, since all loci are inherited through a single, shared population pedigree. Previous work has shown that the non-independence between gene genealogies of independently segregating loci created by the population pedigree is weak in panmictic populations, and predictions made from standard coalescent theory are accurate for populations that are at least moderately sized. Here, we investigate patterns of coalescence in pedigrees of structured populations. We find that the pedigree creates deviations away from the predictions of the structured coalescent that persist on a longer timescale than in the case of panmictic populations. Nevertheless, we find that the structured coalescent provides a reasonable approximation for the coalescent process in structured population pedigrees so long as migration events are moderately frequent and there are no migration events in the recent pedigree of the sample. When there are migration events in the recent sample pedigree, we find that distributions of coalescence in the sample can be modeled as a mixture of distributions from different initial sample configurations. We use this observation to motivate a maximum-likelihood approach for inferring migration rates and mutation rates jointly with features of the pedigree such as recent migrant ancestry and recent relatedness. Using simulation, we show that our inference framework accurately recovers long-term migration rates in the presence of recent migration events in the sample pedigree.

Emergent rules for codon choice elucidated by editing rare arginine codons in Escherichia coli.

The degeneracy of the genetic code allows nucleic acids to encode amino acid identity as well as noncoding information for gene regulation and genome maintenance. The rare arginine codons AGA and AGG (AGR) present a case study in codon choice, with AGRs encoding important transcriptional and translational properties distinct from the other synonymous alternatives (CGN). We created a strain of Escherichia coli with all 123 instances of AGR codons removed from all essential genes. We readily replaced 110 AGR codons with the synonymous CGU codons, but the remaining 13 "recalcitrant" AGRs required diversification to identify viable alternatives. Successful replacement codons tended to conserve local ribosomal binding site-like motifs and local mRNA secondary structure, sometimes at the expense of amino acid identity. Based on these observations, we empirically defined metrics for a multidimensional "safe replacement zone" (SRZ) within which alternative codons are more likely to be viable. To evaluate synonymous and nonsynonymous alternatives to essential AGRs further, we implemented a CRISPR/Cas9-based method to deplete a diversified population of a wild-type allele, allowing us to evaluate exhaustively the fitness impact of all 64 codon alternatives. Using this method, we confirmed the relevance of the SRZ by tracking codon fitness over time in 14 different genes, finding that codons that fall outside the SRZ are rapidly depleted from a growing population. Our unbiased and systematic strategy for identifying unpredicted design flaws in synthetic genomes and for elucidating rules governing codon choice will be crucial for designing genomes exhibiting radically altered genetic codes.

Design, synthesis, and testing toward a 57-codon genome.

Recoding--the repurposing of genetic codons--is a powerful strategy for enhancing genomes with functions not commonly found in nature. Here, we report computational design, synthesis, and progress toward assembly of a 3.97-megabase, 57-codon Escherichia coli genome in which all 62,214 instances of seven codons were replaced with synonymous alternatives across all protein-coding genes. We have validated 63% of recoded genes by individually testing 55 segments of 50 kilobases each. We observed that 91% of tested essential genes retained functionality with limited fitness effect. We demonstrate identification and correction of lethal design exceptions, only 13 of which were found in 2229 genes. This work underscores the feasibility of rewriting genomes and establishes a framework for large-scale design, assembly, troubleshooting, and phenotypic analysis of synthetic organisms.

Single cell resolution in vivo imaging of DNA damage following PARP inhibition.

Targeting DNA repair pathways is a powerful strategy to treat cancers. To gauge efficacy in vivo, typical response markers include late stage effects such as tumor shrinkage, progression free survival, or invasive repeat biopsies. These approaches are often difficult to answer critical questions such as how a given drug affects single cell populations as a function of dose and time, distance from microvessels or how drug concentration (pharmacokinetics) correlates with DNA damage (pharmacodynamics). Here, we established a single-cell in vivo pharmacodynamic imaging read-out based on a truncated 53BP1 double-strand break reporter to determine whether or not poly(ADP-ribose) polymerase (PARP) inhibitor treatment leads to accumulation of DNA damage. Using this reporter, we show that not all PARP inhibitor treated tumors incur an increase in DNA damage. The method provides a framework for single cell analysis of cancer therapeutics in vivo.