Honors & Awards
Graduate Research Fellowship Program, National Science Foundation (9/2016-9/2019)
Trainee, ChEM-H CBI Predoctoral Program, ChEM-H (9/2015-)
Education & Certifications
Master of Science, Stanford University, BIOE-MS (2018)
BS, UC Berkeley, Bioengineering (2015)
Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2018; 115 (16): E3702–E3711
Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.
View details for DOI 10.1073/pnas.1715888115
View details for Web of Science ID 000430191900015
View details for PubMedID 29588420
View details for PubMedCentralID PMC5910820
BET-seq: Binding energy topographies revealed by microfluidics and high-throughput sequencing.
Methods in cell biology
2018; 148: 229–50
Biophysical models of transcriptional regulation rely on energetic measurements of the binding affinities between transcription factors (TFs) and target DNA binding sites. Historically, assays capable of measuring TF-DNA binding affinities have been relatively low-throughput (measuring ~103 sequences in parallel) and have required significant specialized equipment, limiting their use to a handful of laboratories. Recently, we developed an experimental assay and analysis pipeline that allows measurement of binding energies between a single TF and up to 106 DNA species in a single experiment (Binding Energy Topography by sequencing, or BET-seq) (Le et al., 2018). BET-seq employs the Mechanically Induced Trapping of Molecular Interactions (MITOMI) platform to purify DNA bound to a TF at equilibrium followed by high coverage sequencing to reveal relative differences in binding energy for each sequence. While we have previously used BET-seq to refine the binding affinity landscapes surrounding high-affinity DNA consensus target sites, we anticipate this technique will be applied in future work toward measuring a wide variety of TF-DNA landscapes. Here, we provide detailed instructions and general considerations for DNA library design, performing BET-seq assays, and analyzing the resulting data.
View details for DOI 10.1016/bs.mcb.2018.09.011
View details for PubMedID 30473071
Host Actin Polymerization Tunes the Cell Division Cycle of an Intracellular Pathogen
2015; 11 (4): 499-507
Growth and division are two of the most fundamental capabilities of a bacterial cell. While they are well described for model organisms growing in broth culture, very little is known about the cell division cycle of bacteria replicating in more complex environments. Using a D-alanine reporter strategy, we found that intracellular Listeria monocytogenes (Lm) spend a smaller proportion of their cell cycle dividing compared to Lm growing in broth culture. This alteration to the cell division cycle is independent of bacterial doubling time. Instead, polymerization of host-derived actin at the bacterial cell surface extends the non-dividing elongation period and compresses the division period. By decreasing the relative proportion of dividing Lm, actin polymerization biases the population toward cells with the highest propensity to form actin tails. Thus, there is a positive-feedback loop between the Lm cell division cycle and a physical interaction with the host cytoskeleton.
View details for DOI 10.1016/j.celrep.2015.03.046
View details for Web of Science ID 000353902600001
View details for PubMedID 25892235
View details for PubMedCentralID PMC4417095
D-Amino Acid Chemical Reporters Reveal Peptidoglycan Dynamics of an Intracellular Pathogen
ACS CHEMICAL BIOLOGY
2013; 8 (3): 500-505
Peptidoglycan (PG) is an essential component of the bacterial cell wall. Although experiments with organisms in vitro have yielded a wealth of information on PG synthesis and maturation, it is unclear how these studies translate to bacteria replicating within host cells. We report a chemical approach for probing PG in vivo via metabolic labeling and bioorthogonal chemistry. A wide variety of bacterial species incorporated azide and alkyne-functionalized d-alanine into their cell walls, which we visualized by covalent reaction with click chemistry probes. The d-alanine analogues were specifically incorporated into nascent PG of the intracellular pathogen Listeria monocytogenes both in vitro and during macrophage infection. Metabolic incorporation of d-alanine derivatives and click chemistry detection constitute a facile, modular platform that facilitates unprecedented spatial and temporal resolution of PG dynamics in vivo.
View details for DOI 10.1021/cb3004995
View details for Web of Science ID 000316375500003
View details for PubMedID 23240806
View details for PubMedCentralID PMC3601600