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The regulation of cell physiology largely depends upon intracellular interactions of functionally distinct proteins that act in combination. These interactions, which are often transient in nature, help to define the motion profiles of proteins. Measurement of protein motion within a living cellular environment will enable dissection of key interactions among proteins, however, attempts to measure protein motion are typically limited by the low spatial and temporal resolution of existing experimental platforms. Here, we describe a high-throughput single-molecule imaging platform that measures protein motion in living cells. We demonstrate the application of this platform by studying the dynamics of steroid hormone receptors and explore the pharmacology of compounds that affect estrogen receptor (ER) activity. Using our high-throughput single-molecule tracking (htSMT) platform, we screened 5,067 bioactive molecules, identifying multiple proteins and signaling pathways which perturb ER dynamics. We further deployed htSMT to characterize the impact of known ER modulators on ER protein dynamics, uncovering a correlation between ER dynamics and the ability of ER antagonists to suppress cancer cell growth. SMT provides a novel platform capable of measuring real-time target engagement within the living cellular environment. Our results support the view that this tool will prove uniquely valuable in measuring the dynamic interactions among proteins and will prove powerful for the identification of novel therapeutics.

The multikilobase reads that can be produced by single-molecule sequencing technologies may span complex, repetitive genomic regions but have high error rates. Bashir et al. use these reads to organize contigs assembled from accurate, short-read data, facilitating the analysis of clinically important regions of an outbreak strain of cholera. Advances in DNA sequencing technology have improved our ability to characterize most genomic diversity. However, accurate resolution of large structural events is challenging because of the short read lengths of second-generation technologies. Third-generation sequencing technologies, which can yield longer multikilobase reads, have the potential to address limitations associated with genome assembly. Here we combine sequencing data from second- and third-generation DNA sequencing technologies to assemble the two-chromosome genome of a recent Haitian cholera outbreak strain into two nearly finished contigs at >99.9% accuracy. Complex regions with clinically relevant structure were completely resolved. In separate control assemblies on experimental and simulated data for the canonical N16961 cholera reference strain, we obtained 14 scaffolds of greater than 1 kb for the experimental data and 8 scaffolds of greater than 1 kb for the simulated data, which allowed us to correct several errors in contigs assembled from the short-read data alone. This work provides a blueprint for the next generation of rapid microbial identification and full-genome assembly.

We present a hierarchical genome-assembly process (HGAP) for high-quality de novo microbial genome assemblies using only a single, long-insert shotgun DNA library in conjunction with Single Molecule, Real-Time (SMRT) DNA sequencing. Our method uses the longest reads as seeds to recruit all other reads for construction of highly accurate preassembled reads through a directed acyclic graph-based consensus procedure, which we follow with assembly using off-the-shelf long-read assemblers. In contrast to hybrid approaches, HGAP does not require highly accurate raw reads for error correction. We demonstrate efficient genome assembly for several microorganisms using as few as three SMRT Cell zero-mode waveguide arrays of sequencing and for BACs using just one SMRT Cell. Long repeat regions can be successfully resolved with this workflow. We also describe a consensus algorithm that incorporates SMRT sequencing primary quality values to produce de novo genome sequence exceeding 99.999% accuracy.

A large outbreak of diarrhea and the hemolytic-uremic syndrome caused by an unusual serotype of Shiga-toxin-producing Escherichia coli (O104:H4) began in Germany in May 2011. As of July 22, a large number of cases of diarrhea caused by Shiga-toxin-producing E. coli have been reported--3167 without the hemolytic-uremic syndrome (16 deaths) and 908 with the hemolytic-uremic syndrome (34 deaths)--indicating that this strain is notably more virulent than most of the Shiga-toxin-producing E. coli strains. Preliminary genetic characterization of the outbreak strain suggested that, unlike most of these strains, it should be classified within the enteroaggregative pathotype of E. coli.We used third-generation, single-molecule, real-time DNA sequencing to determine the complete genome sequence of the German outbreak strain, as well as the genome sequences of seven diarrhea-associated enteroaggregative E. coli serotype O104:H4 strains from Africa and four enteroaggregative E. coli reference strains belonging to other serotypes. Genomewide comparisons were performed with the use of these enteroaggregative E. coli genomes, as well as those of 40 previously sequenced E. coli isolates.The enteroaggregative E. coli O104:H4 strains are closely related and form a distinct clade among E. coli and enteroaggregative E. coli strains. However, the genome of the German outbreak strain can be distinguished from those of other O104:H4 strains because it contains a prophage encoding Shiga toxin 2 and a distinct set of additional virulence and antibiotic-resistance factors.Our findings suggest that horizontal genetic exchange allowed for the emergence of the highly virulent Shiga-toxin-producing enteroaggregative E. coli O104:H4 strain that caused the German outbreak. More broadly, these findings highlight the way in which the plasticity of bacterial genomes facilitates the emergence of new pathogens.