George M. Church

RNA-Guided Human Genome Engineering via Cas9

Genome Editing Clustered regularly interspaced short palindromic repeats (CRISPR) function as part of an adaptive immune system in a range of prokaryotes: Invading phage and plasmid DNA is targeted for cleavage by complementary CRISPR RNAs (crRNAs) bound to a CRISPR-associated endonuclease (see the Perspective by van der Oost). Cong et al. (p. 819, published online 3 January) and Mali et al. (p. 823, published online 3 January) adapted this defense system to function as a genome editing tool in eukaryotic cells. A bacterial genome defense system is adapted to function as a genome-editing tool in mammalian cells. [Also see Perspective by van der Oost] Bacteria and archaea have evolved adaptive immune defenses, termed clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) systems, that use short RNA to direct degradation of foreign nucleic acids. Here, we engineer the type II bacterial CRISPR system to function with custom guide RNA (gRNA) in human cells. For the endogenous AAVS1 locus, we obtained targeting rates of 10 to 25% in 293T cells, 13 to 8% in K562 cells, and 2 to 4% in induced pluripotent stem cells. We show that this process relies on CRISPR components; is sequence-specific; and, upon simultaneous introduction of multiple gRNAs, can effect multiplex editing of target loci. We also compute a genome-wide resource of ~190 K unique gRNAs targeting ~40.5% of human exons. Our results establish an RNA-guided editing tool for facile, robust, and multiplexable human genome engineering.

Assessing computational tools for the discovery of transcription factor binding sites

The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering

Prokaryotic type II CRISPR-Cas systems can be adapted to enable targeted genome modifications across a range of eukaryotes. Here we engineer this system to enable RNA-guided genome regulation in human cells by tethering transcriptional activation domains either directly to a nuclease-null Cas9 protein or to an aptamer-modified single guide RNA (sgRNA). Using this functionality we developed a transcriptional activation–based assay to determine the landscape of off-target binding of sgRNA:Cas9 complexes and compared it with the off-target activity of transcription activator–like (TALs) effectors. Our results reveal that specificity profiles are sgRNA dependent, and that sgRNA:Cas9 complexes and 18-mer TAL effectors can potentially tolerate 1–3 and 1–2 target mismatches, respectively. By engineering a requirement for cooperativity through offset nicking for genome editing or through multiple synergistic sgRNAs for robust transcriptional activation, we suggest methods to mitigate off-target phenomena. Our results expand the versatility of the sgRNA:Cas9 tool and highlight the critical need to engineer improved specificity.

Analysis of optimality in natural and perturbed metabolic networks

An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism.

Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation

Whole-genome mRNA quantitation can be used to identify the genes that are most responsive to environmental or genotypic change. By searching for mutually similar DNA elements among the upstream non-coding DNA sequences of these genes, we can identify candidate regulatory motifs and corresponding candidate sets of coregulated genes. We have tested this strategy by applying it to three extensively studied regulatory systems in the yeast Saccharomyces cerevisiae: galactose response, heat shock, and mating type. Galactose-response data yielded the known binding site of Gal4, and six of nine genes known to be induced by galactose. Heat shock data yielded the cell-cycle activation motif, which is known to mediate cell-cycle dependent activation, and a set of genes coding for all four nucleosomal proteins. Mating type α and a data yielded all of the four relevant DNA motifs and most of the known a- and α-specific genes.

Programming cells by multiplex genome engineering and accelerated evolution

The breadth of genomic diversity found among organisms in nature allows populations to adapt to diverse environments. However, genomic diversity is difficult to generate in the laboratory and new phenotypes do not easily arise on practical timescales. Although in vitro and directed evolution methods have created genetic variants with usefully altered phenotypes, these methods are limited to laborious and serial manipulation of single genes and are not used for parallel and continuous directed evolution of gene networks or genomes. Here, we describe multiplex automated genome engineering (MAGE) for large-scale programming and evolution of cells. MAGE simultaneously targets many locations on the chromosome for modification in a single cell or across a population of cells, thus producing combinatorial genomic diversity. Because the process is cyclical and scalable, we constructed prototype devices that automate the MAGE technology to facilitate rapid and continuous generation of a diverse set of genetic changes (mismatches, insertions, deletions). We applied MAGE to optimize the 1-deoxy-d-xylulose-5-phosphate (DXP) biosynthesis pathway in Escherichia coli to overproduce the industrially important isoprenoid lycopene. Twenty-four genetic components in the DXP pathway were modified simultaneously using a complex pool of synthetic DNA, creating over 4.3 billion combinatorial genomic variants per day. We isolated variants with more than fivefold increase in lycopene production within 3 days, a significant improvement over existing metabolic engineering techniques. Our multiplex approach embraces engineering in the context of evolution by expediting the design and evolution of organisms with new and improved properties.

A Logic-Gated Nanorobot for Targeted Transport of Molecular Payloads

Nanorobots Deliver DNA aptamers are short strands that have high binding affinity for a target protein that can be used as triggers for releasing cargo from delivery vehicles. Douglas et al. (p. 831) used this strategy to design DNA origami “nanorobots”—complex shaped structures created by manipulating a long DNA strand through binding with shorter “staple” strands—that could deliver payloads such as gold nanoparticles or fluorescently labeled antibody fragments. These nanorobots were designed to open in response to specific cell-surface proteins, releasing molecules that triggered cell signaling. Cargoes stored in folded DNA origami are released when aptamers in the structure bind target protein molecules. We describe an autonomous DNA nanorobot capable of transporting molecular payloads to cells, sensing cell surface inputs for conditional, triggered activation, and reconfiguring its structure for payload delivery. The device can be loaded with a variety of materials in a highly organized fashion and is controlled by an aptamer-encoded logic gate, enabling it to respond to a wide array of cues. We implemented several different logical AND gates and demonstrate their efficacy in selective regulation of nanorobot function. As a proof of principle, nanorobots loaded with combinations of antibody fragments were used in two different types of cell-signaling stimulation in tissue culture. Our prototype could inspire new designs with different selectivities and biologically active payloads for cell-targeting tasks.

Human Genome Sequencing Using Unchained Base Reads on Self-Assembling DNA Nanoarrays

Toward

Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems

1000 Genomes The ability to generate human genome sequence data that is complete, accurate, and inexpensive is a necessary prerequisite to perform genome-wide disease association studies. Drmanac et al. (p. 78, published online 5 November) present a technique advancing toward this goal. The method uses Type IIS endonucleases to incorporate short oligonucleotides within a set of randomly sheared circularized DNA. DNA polymerase then generates concatenated copies of the circular oligonucleotides leading to formation of compact but very long oligonucleotides which are then sequenced by ligation. The relatively low cost of this technology, which shows a low error rate, advances sequencing closer to the goal of the

Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells

1000 genome. A low-cost sequencing technique advances us closer to the goal of the