Massively parallel measurements of molecular interaction kinetics on a microfluidic platform

Abstract

Quantitative biology requires quantitative data. No high-throughput technologies exist capable of obtaining several hundred independent kinetic binding measurements in a single experiment. We present an integrated microfluidic device (k-MITOMI) for the simultaneous kinetic characterization of 768 biomolecular interactions. We applied k-MITOMI to the kinetic analysis of transcription factor (TF)-DNA interactions, measuring the detailed kinetic landscapes of the mouse TF Zif268, and the yeast TFs Tye7p, Yox1p, and Tbf1p. We demonstrated the integrated nature of k-MITOMI by expressing, purifying, and characterizing 27 additional yeast transcription factors in parallel on a single device. Overall, we obtained 2,388 association and dissociation curves of 223 unique molecular interactions with equilibrium dissociation constants ranging from 2 × 10 −6 M to 2 × 10 −9 M, and dissociation rate constants of approximately 6 s −1 to 8.5 × 10 −3 s −1 . Association rate constants were uniform across 3 TF families, ranging from 3.7 × 10 6 M −1 s −1 to 9.6 × 10 7 M −1 s −1 , and are well below the diffusion limit. We expect that k-MITOMI will contribute to our quantitative understanding of biological systems and accelerate the development and characterization of engineered systems.biochemistry | biophysics | systems biology S ystems and synthetic biology, as well as the computational models and engineering-based approaches they employ, rely heavily on quantitative data (1, 2). Thus far, efforts in systems biology have mainly focused on cataloging and mapping genomes and proteomes. Genome sequencing and gene expression analysis have provided insight into genome architecture (3-6), and functional genomics approaches, including high-throughput protein-based methods (7-15), mapped network topologies.Although the number of known protein-protein and protein-DNA interactions is already substantial, the information describing such networks is predominantly qualitative and binary in nature. It is also becoming clear that network topologies alone are not sufficient to model complex biological processes. Precise quantitative information describing every interaction in a network would be tremendously valuable (2), yet binding affinities are known for only a small fraction of interactions (16, 17) and kinetic information hardly exists at all. This dearth of quantitative interaction data is due to a lack of high-throughput technologies capable of measuring kinetic rates of biomolecular interactions. Current methods used for kinetic rate measurements are generally based on surface plasmon resonance (SPR) (18) such as BioRad's ProteOn XPR36 6 × 6 array system, which measures 36 interactions in a single run. SPR-based measurements have been integrated with microfluidic devices (19, 20) to achieve higher degrees of parallelization (21). Yet proof of concept demonstrations have made use of only a small fraction of the proposed throughput and often restrict their measurements to protein-antibody interactions with high affinities and long half-lives...

Two distinct promoter architectures centered on dynamic nucleosomes control ribosomal protein gene transcription

“…Although a potential Hmo1-binding motif (referred to as the IFHL motif) has been described based on motif searches of genome-wide ChIPchip binding data (Hall et al 2006;Lavoie et al 2010), Hmo1 was not thought to recognize this motif directly (Kasahara et al 2007) and was reported to show no binding to multimerized motifs in vivo (Hall et al 2006). However, using oligonucleotide pools constituting a De Bruijn representation of all possible 8-mer DNA sequences in a k-MITOMI (Geertz et al 2012b) analysis of Hmo1 binding, we identified a sequence that is quite similar to the IFHL motif (Supplemental Fig. S2A).…”

“…MatrixREDUCE software was used to calculate the position weight matrices (PWMs) for sequences with binding energies >3. The Hmo1 DNA-binding sequence motif was derived using a kinetic MITOMI approach (Geertz et al 2012b). A De Bruijn sequence covering all 8-mer sequence variants was calculated (Philippakis et al 2008) and then synthesized as overlapping oligonucleotide pairs that were converted to dsDNA using Cy3 and Cy5 extension primers (Geertz et al 2012a).…”

Designer protein delivery: From natural to engineered affinity-controlled release systems

“…Geertz et al (28) used mechanically induced trapping of molecular interactions in a microfluidic platform to simultaneously analyze the kinetics of transcription factors binding to their fluorescently labeled DNA ligands-a total of 223 unique interactions. These interactions have a range of K d values and tend to have high association rates and short half-lives, making them challenging to measure using standard techniques (28,29).…”

Protein–DNA force assay in a microfluidic format

“…26 Recently, the chip design has been improved for fast response times and the chip is now capable of recording association and dissociation traces. 28 In the present publication, we introduce a novel method, which for the first time combines the MFA measurement principle with a microfluidic design. In particular, the button valve of the MITOMI chip is used to apply the force necessary for bond rupture.…”