Automated electrotransformation of Escherichia coli on a digital microfluidic platform using bioactivated magnetic beads.

This paper reports on the use of a digital microfluidic platform to perform multiplex automated genetic engineering (MAGE) cycles on droplets containing Escherichia coli cells. Bioactivated magnetic beads were employed for cell binding, washing, and media exchange in the preparation of electrocompetent cells in the electrowetting-on-dieletric (EWoD) platform. On-cartridge electroporation was used to deliver oligonucleotides into the cells. In addition to the optimization of a magnetic bead-based benchtop protocol for generating and transforming electrocompetent E. coli cells, we report on the implementation of this protocol in a fully automated digital microfluidic platform. Bead-based media exchange and electroporation pulse conditions were optimized on benchtop for transformation frequency to provide initial parameters for microfluidic device trials. Benchtop experiments comparing electrotransformation of free and bead-bound cells are presented. Our results suggest that dielectric shielding intrinsic to bead-bound cells significantly reduces electroporation field exposure efficiency. However, high transformation frequency can be maintained in the presence of magnetic beads through the application of more intense electroporation pulses. As a proof of concept, MAGE cycles were successfully performed on a commercial EWoD cartridge using variations of the optimal magnetic bead-based preparation procedure and pulse conditions determined by the benchtop results. Transformation frequencies up to 22% were achieved on benchtop; this frequency was matched within 1% (21%) by MAGE cycles on the microfluidic device. However, typical frequencies on the device remain lower, averaging 9% with a standard deviation of 9%. The presented results demonstrate the potential of digital microfluidics to perform complex and automated genetic engineering protocols.

Overcoming Challenges in Engineering the Genetic Code.

Withstanding 3.5 billion years of genetic drift, the canonical genetic code remains such a fundamental foundation for the complexity of life that it is highly conserved across all three phylogenetic domains. Genome engineering technologies are now making it possible to rationally change the genetic code, offering resistance to viruses, genetic isolation from horizontal gene transfer, and prevention of environmental escape by genetically modified organisms. We discuss the biochemical, genetic, and technological challenges that must be overcome in order to engineer the genetic code.

Probing the limits of genetic recoding in essential genes.

Engineering radically altered genetic codes will allow for genomically recoded organisms that have expanded chemical capabilities and are isolated from nature. We have previously reassigned the translation function of the UAG stop codon; however, reassigning sense codons poses a greater challenge because such codons are more prevalent, and their usage regulates gene expression in ways that are difficult to predict. To assess the feasibility of radically altering the genetic code, we selected a panel of 42 highly expressed essential genes for modification. Across 80 Escherichia coli strains, we removed all instances of 13 rare codons from these genes and attempted to shuffle all remaining codons. Our results suggest that the genome-wide removal of 13 codons is feasible; however, several genome design constraints were apparent, underscoring the importance of a strategy that rapidly prototypes and tests many designs in small pieces.

Manipulating replisome dynamics to enhance lambda Red-mediated multiplex genome engineering.

Disrupting the interaction between primase and helicase in Escherichia coli increases Okazaki fragment (OF) length due to less frequent primer synthesis. We exploited this feature to increase the amount of ssDNA at the lagging strand of the replication fork that is available for λ Red-mediated Multiplex Automatable Genome Engineering (MAGE). Supporting this concept, we demonstrate that MAGE enhancements correlate with OF length. Compared with a standard recombineering strain (EcNR2), the strain with the longest OFs displays on average 62% more alleles converted per clone, 239% more clones with 5 or more allele conversions and 38% fewer clones with 0 allele conversions in 1 cycle of co-selection MAGE (CoS-MAGE) with 10 synthetic oligonucleotides. Additionally, we demonstrate that both synthetic oligonucleotides and accessible ssDNA targets on the lagging strand of the replication fork are limiting factors for MAGE. Given this new insight, we generated a strain with reduced oligonucleotide degradation and increased genomic ssDNA availability, which displayed 111% more alleles converted per clone, 527% more clones with 5 or more allele conversions and 71% fewer clones with 0 allele conversions in 1 cycle of 10-plex CoS-MAGE. These improvements will facilitate ambitious genome engineering projects by minimizing dependence on time-consuming clonal isolation and screening.

Lambda red recombineering in Escherichia coli occurs through a fully single-stranded intermediate.

The phage lambda-derived Red recombination system is a powerful tool for making targeted genetic changes in Escherichia coli, providing a simple and versatile method for generating insertion, deletion, and point mutations on chromosomal, plasmid, or BAC targets. However, despite the common use of this system, the detailed mechanism by which lambda Red mediates double-stranded DNA recombination remains uncertain. Current mechanisms posit a recombination intermediate in which both 5' ends of double-stranded DNA are recessed by λ exonuclease, leaving behind 3' overhangs. Here, we propose an alternative in which lambda exonuclease entirely degrades one strand, while leaving the other strand intact as single-stranded DNA. This single-stranded intermediate then recombines via beta recombinase-catalyzed annealing at the replication fork. We support this by showing that single-stranded gene insertion cassettes are recombinogenic and that these cassettes preferentially target the lagging strand during DNA replication. Furthermore, a double-stranded DNA cassette containing multiple internal mismatches shows strand-specific mutations cosegregating roughly 80% of the time. These observations are more consistent with our model than with previously proposed models. Finally, by using phosphorothioate linkages to protect the lagging-targeting strand of a double-stranded DNA cassette, we illustrate how our new mechanistic knowledge can be used to enhance lambda Red recombination frequency. The mechanistic insights revealed by this work may facilitate further improvements to the versatility of lambda Red recombination.

Algorithms for identifying protein cross-links via tandem mass spectrometry.

Cross-linking technology combined with tandem mass spectrometry (MS-MS) is a powerful method that provides a rapid solution to the discovery of protein-protein interactions and protein structures. We studied the problem of detecting cross-linked peptides and cross-linked amino acids from tandem mass spectral data. Our method consists of two steps: the first step finds two protein subsequences whose mass sum equals a given mass measured from the mass spectrometry; and the second step finds the best cross-linked amino acids in these two peptide sequences that are optimally correlated to a given tandem mass spectrum. We designed fast and space-efficient algorithms for these two steps and implemented and tested them on experimental data of cross-linked hemoglobin proteins. An interchain cross-link between two beta subunits was found in two tandem mass spectra. The length of the cross-linker (7.7 A) is very close to the actual distance (8.18 A) obtained from the molecular structure in PDB.

Selection analyses of insertional mutants using subgenic-resolution arrays.

We describe a method of genome-wide analysis of quantitative growth phenotypes using insertional mutagenesis and DNA microarrays. We applied the method to assess the fitness contributions of Escherichia coli gene domains under specific growth conditions. A transposon library was subjected to competitive growth selection in Luria-Bertani (LB) and in glucose minimal media. Transposon-containing genomic DNA fragments from the selected libraries were compared with the initial unselected transposon insertion library on DNA microarrays to identify insertions that affect fitness. Genes involved in the biosynthesis of nutrients not provided in the growth medium were found to be significantly enriched in the set of genes containing negatively selected insertions. The data also identify fitness contributions of several uncharacterized genes, including putative transcriptional regulators and enzymes. The applicability of this high-resolution array selection in other species is discussed.

Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae.

Genomic and proteomic approaches can provide hypotheses concerning function for the large number of genes predicted from genome sequences. Because of the artificial nature of the assays, however, the information from these high-throughput approaches should be considered with caution. Although it is possible that more meaningful hypotheses could be formulated by integrating the data from various functional genomic and proteomic projects, it has yet to be seen to what extent the data can be correlated and how such integration can be achieved. We developed a 'transcriptome-interactome correlation mapping' strategy to compare the interactions between proteins encoded by genes that belong to common expression-profiling clusters with those between proteins encoded by genes that belong to different clusters. Using this strategy with currently available data sets for Saccharomyces cerevisiae, we provide the first global evidence that genes with similar expression profiles are more likely to encode interacting proteins. We show how this correlation between transcriptome and interactome data can be used to improve the quality of hypotheses based on the information from both approaches. The strategy described here may help to integrate other functional genomic and proteomic data, both in yeast and in higher organisms.

A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry.

Tandem mass spectrometry fragments a large number of molecules of the same peptide sequence into charged molecules of prefix and suffix peptide subsequences and then measures mass/charge ratios of these ions. The de novo peptide sequencing problem is to reconstruct the peptide sequence from a given tandem mass spectral data of k ions. By implicitly transforming the spectral data into an NC-spectrum graph G (V, E) where /V/ = 2k + 2, we can solve this problem in O(/V//E/) time and O(/V/2) space using dynamic programming. For an ideal noise-free spectrum with only b- and y-ions, we improve the algorithm to O(/V/ + /E/) time and O(/V/) space. Our approach can be further used to discover a modified amino acid in O(/V//E/) time. The algorithms have been implemented and tested on experimental data.

Identifying regulatory networks by combinatorial analysis of promoter elements.

Several computational methods based on microarray data are currently used to study genome-wide transcriptional regulation. Few studies, however, address the combinatorial nature of transcription, a well-established phenomenon in eukaryotes. Here we describe a new approach using microarray data to uncover novel functional motif combinations in the promoters of Saccharomyces cerevisiae. In addition to identifying novel motif combinations that affect expression patterns during the cell cycle, sporulation and various stress responses, we observed regulatory cross-talk among several of these processes. We have also generated motif-association maps that provide a global view of transcription networks. The maps are highly connected, suggesting that a small number of transcription factors are responsible for a complex set of expression patterns in diverse conditions. This approach may be useful for modeling transcriptional regulatory networks in more complex eukaryotes.

Aligning gene expression time series with time warping algorithms.

UNLABELLED: motivation: Increasingly, biological processes are being studied through time series of RNA expression data collected for large numbers of genes. Because common processes may unfold at varying rates in different experiments or individuals, methods are needed that will allow corresponding expression states in different time series to be mapped to one another. RESULTS: We present implementations of time warping algorithms applicable to RNA and protein expression data and demonstrate their application to published yeast RNA expression time series. Programs executing two warping algorithms are described, a simple warping algorithm and an interpolative algorithm, along with programs that generate graphics that visually present alignment information. We show time warping to be superior to simple clustering at mapping corresponding time states. We document the impact of statistical measurement noise and sample size on the quality of time alignments, and present issues related to statistical assessment of alignment quality through alignment scores. We also discuss directions for algorithm improvement including development of multiple time series alignments and possible applications to causality searches and non-temporal processes ('concentration warping').

Exploring the DNA-binding specificities of zinc fingers with DNA microarrays.

A key step in the regulation of networks that control gene expression is the sequence-specific binding of transcription factors to their DNA recognition sites. A more complete understanding of these DNA-protein interactions will permit a more comprehensive and quantitative mapping of the regulatory pathways within cells, as well as a deeper understanding of the potential functions of individual genes regulated by newly identified DNA-binding sites. Here we describe a DNA microarray-based method to characterize sequence-specific DNA recognition by zinc-finger proteins. A phage display library, prepared by randomizing critical amino acid residues in the second of three fingers of the mouse Zif268 domain, provided a rich source of zinc-finger proteins with variant DNA-binding specificities. Microarrays containing all possible 3-bp binding sites for the variable zinc fingers permitted the quantitation of the binding site preferences of the entire library, pools of zinc fingers corresponding to different rounds of selection from this library, as well as individual Zif268 variants that were isolated from the library by using specific DNA sequences. The results demonstrate the feasibility of using DNA microarrays for genome-wide identification of putative transcription factor-binding sites.

Dynamic simulation of the human red blood cell metabolic network.

We have developed a Mathematica application package to perform dynamic simulations of the red blood cell (RBC) metabolic network. The package relies on, and integrates, many years of mathematical modeling and biochemical work on red blood cell metabolism. The extensive data regarding the red blood cell metabolic network and the previous kinetic analysis of all the individual components makes the human RBC an ideal 'model' system for mathematical metabolic models. The Mathematica package can be used to understand the dynamics and regulatory characteristics of the red blood cell.

Computational comparison of two draft sequences of the human genome.

We are in the enviable position of having two distinct drafts of the human genome sequence. Although gaps, errors, redundancy and incomplete annotation mean that individually each falls short of the ideal, many of these problems can be assessed by comparison. Here we present some comparative analyses of these drafts. We look at a number of features of the sequences, including sequence gaps, continuity, consistency between the two sequences and patterns of DNA-binding protein motifs.

RNA expression analysis using a 30 base pair resolution Escherichia coli genome array.

We have developed a high-resolution "genome array" for the study of gene expression and regulation in Escherichia coli. This array contains on average one 25-mer oligonucleotide probe per 30 base pairs over the entire genome, with one every 6 bases for the intergenic regions and every 60 bases for the 4,290 open reading frames (ORFs). Twofold concentration differences can be detected at levels as low as 0.2 messenger RNA (mRNA) copies per cell, and differences can be seen over a dynamic range of three orders of magnitude. In rich medium we detected transcripts for 97% and 87% of the ORFs in stationary and log phases, respectively. We found that 1, 529 transcripts were differentially expressed under these conditions. As expected, genes involved in translation were expressed at higher levels in log phase, whereas many genes known to be involved in the starvation response were expressed at higher levels in stationary phase. Many previously unrecognized growth phase-regulated genes were identified, such as a putative receptor (b0836) and a 30S ribosomal protein subunit (S22), both of which are highly upregulated in stationary phase. Transcription of between 3,000 and 4,000 predicted ORFs was observed from the antisense strand, indicating that most of the genome is transcribed at a detectable level. Examples are also presented for high-resolution array analysis of transcript start and stop sites and RNA secondary structure.

Sequence variability and candidate gene analysis in complex disease: association of mu opioid receptor gene variation with substance dependence.

To analyze candidate genes and establish complex genotype-phenotype relationships against a background of high natural genome sequence variability, we have developed approaches to (i) compare candidate gene sequence information in multiple individuals; (ii) predict haplotypes from numerous variants; and (iii) classify haplotypes and identify specific sequence variants, or combinations of variants (pattern), associated with the phenotype. Using the human mu opioid receptor gene (OPRM1) as a model system, we have combined these approaches to test a potential role of OPRM1 in substance (heroin/cocaine) dependence. All known functionally relevant regions of this prime candidate gene were analyzed by multiplex sequence comparison in 250 cases and controls; 43 variants were identified and 52 different haplotypes predicted in the subgroup of 172 African-Americans. These haplotypes were classified by similarity clustering into two functionally related categories, one of which was significantly more frequent in substance-dependent individuals. Common to this category was a characteristic pattern of sequence variants [-1793T-->A, -1699Tins, -1320A-->G, -111C-->T, +17C-->T (A6V)], which was associated with substance dependence. This study provides an example of approaches that have been successfully applied to the establishment of complex genotype-phenotype relationships in the presence of abundant DNA sequence variation.

Predicting regulons and their cis-regulatory motifs by comparative genomics.

We have combined and compared three techniques for predicting functional interactions based on comparative genomics (methods based on conserved operons, protein fusions and correlated evolution) and optimized these methods to predict coregulated sets of genes in 24 complete genomes, including Saccharomyces cerevisiae, Caenorhabditis elegans and 22 prokaryotes. The method based on conserved operons was the most useful for this purpose. Upstream regions of the genes comprising these predicted regulons were then used to search for regulatory motifs in 22 prokaryotic genomes using the motif-discovery program AlignACE. Many significant upstream motifs, including five known Escherichia coli regulatory motifs, were identified in this manner. The presence of a significant regulatory motif was used to refine the members of the predicted regulons to generate a final set of predicted regulons that share significant regulatory elements.

The Isw2 chromatin remodeling complex represses early meiotic genes upon recruitment by Ume6p.

The ISWI class of chromatin remodeling factors exhibits potent chromatin remodeling activities in vitro. However, the in vivo functions of this class of factors are unknown at a molecular level. We have found that S. cerevisiae Isw2 complex represses transcription of early meiotic genes during mitotic growth in a parallel pathway to Rpd3-Sin3 histone deacetylase complex. This repressor function of lsw2 complex is largely dependent upon Ume6p, which recruits the complex to target genes. Nuclease digestion analyses revealed that lsw2 complex establishes nuclease-inaccessible chromatin structure near the Ume6p binding site in vivo. Based on these findings, we propose a model for the mechanism of transcriptional repression by two distinct chromatin remodeling complexes.

Regulatory networks revealed by transcriptional profiling of damaged Saccharomyces cerevisiae cells: Rpn4 links base excision repair with proteasomes.

Exposure to carcinogenic alkylating agents, oxidizing agents, and ionizing radiation modulates transcript levels for over one third of Saccharomyces cerevisiae's 6,200 genes. Computational analysis delineates groups of coregulated genes whose upstream regions bear known and novel regulatory sequence motifs. One group of coregulated genes contain a number of DNA excision repair genes (including the MAG1 3-methyladenine DNA glycosylase gene) and a large selection of protein degradation genes. Moreover, transcription of these genes is modulated by the proteasome-associated protein Rpn4, most likely via its binding to MAG1 upstream repressor sequence 2-like elements, that turn out to be almost identical to the recently identified proteasome-associated control element (G. Mannhaupt, R. Schnall, V. Karpov, I. Vetter, and H. Feldmann, FEBS Lett. 450:27-34, 1999). We have identified a large number of genes whose transcription is influenced by Rpn4p.