Yuxi Ke

All Publications

• Memory encoding reprograms neuronal transcriptional responses via durable chromatin remodeling. bioRxiv : the preprint server for biology Ke, Y., Sun, X., Greenleaf, W. J., Schnitzer, M. J. 2026

  Abstract

  The mammalian brain's long-term memory circuits integrate information from prior and new experiences. The medial prefrontal cortex (mPFC) has a crucial role in this process and can reliably store information for weeks to months in rodents and over years in humans. To maintain information over these extended timescales, the neural encoding of remote memories involves persistent synaptic, transcriptional, and epigenetic changes that outlast the more transient forms of molecular activation that occur in the initial minutes to hours of memory storage. However, whether these persistent effects include long-lasting changes to chromatin structure and whether chromatin states mainly reflect prior episodes of neural activation or retune transcriptional responses to future bouts of activation remain unknown. Here we show that mPFC neurons engaged during the initial formation of memory undergo progressive changes in chromatin accessibility over the first four weeks of memory storage, evincing long-lasting modifications to the genetic programs activated during subsequent memory retrieval. Our experiments involved genetic trapping and single-cell multiomic sequencing analyses of mouse mPFC engram neurons activated during contextual fear conditioning. In the absence of subsequent memory recall, memory storage-related changes to chromatin structure were modestly reflected in gene expression patterns at 7 and 28 days after fear conditioning. However, upon memory recall, the genetically trapped engram neurons executed distinct transcriptional programs from those of other neurons of the same genetic types, suggesting that chromatin rearrangements arising during remote memory storage alter the transcriptional control logic by which engram neurons respond to new experiences. These metaplastic changes to transcriptional programs preferentially affect gene-regulatory and post-transcriptional control mechanisms, show substantial enrichment for transcription factor motifs related to neural development and cell-state regulation, and downregulate the neuron's transcriptional responses to future excitation. Thus, rather than merely preserving a molecular record of prior learning, chromatin architectural changes in engram neurons occur over timescales of weeks and appear, in part, to repurpose conserved regulatory machinery to dampen the extent to which these neurons will engage in further information storage. Based on these findings, we propose that chromatin structural changes provide a slow-timescale component of neural computation that reduces interference between the representations of different memories.

  View details for DOI 10.64898/2026.07.29.741555

  View details for PubMedID 42619820

  View details for PubMedCentralID PMC13484195

• High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics. Nature communications Ke, Y., Sharma, E., Wayment-Steele, H. K., Becker, W. R., Ho, A., Marklund, E., Greenleaf, W. J. 2025; 16 (1): 5572

  Abstract

  DNA folding thermodynamics are central to many biological processes and biotechnological applications involving base-pairing. Current methods for predicting stability from DNA sequence use nearest-neighbor models that struggle to accurately capture the diverse sequence dependence of secondary structural motifsbeyond Watson-Crick base pairs, likely due to insufficient experimental data. In this work, we introduce a massively parallel method, Array Melt, that uses fluorescence-based quenching signals to measure the equilibrium stability of millions of DNA hairpins simultaneously on a repurposed Illumina sequencing flow cell. By leveraging this dataset of 27,732 sequences with two-state melting behaviors, we derive a NUPACK-compatible model (dna24), a rich parameter model that exhibits higher accuracy, and a graph neural network (GNN) model that identifies relevant interactions within DNA beyond nearest neighbors. All models show improved accuracy in predicting DNA folding thermodynamics, enabling more effective in silico design of qPCR primers, oligo hybridization probes, and DNA origami.

  View details for DOI 10.1038/s41467-025-60455-4

  View details for PubMedID 40593545

• High-throughput biochemistry in RNA sequence space: predicting structure and function. Nature reviews. Genetics Marklund, E., Ke, Y., Greenleaf, W. J. 2023

  Abstract

  RNAs are central to fundamental biological processes in all known organisms. The set of possible intramolecular interactions of RNA nucleotides defines the range of alternative structural conformations of a specific RNA that can coexist, and these structures enable functional catalytic properties of RNAs and/or their productive intermolecular interactions with other RNAs or proteins. However, the immense combinatorial space of potential RNA sequences has precluded predictive mapping between RNA sequence and molecular structure and function. Recent advances in high-throughput approaches in vitro have enabled quantitative thermodynamic and kinetic measurements of RNA-RNA and RNA-protein interactions, across hundreds of thousands of sequence variations. In this Review, we explore these techniques, how they can be used to understand RNA function and how they might form the foundations of an accurate model to predict the structure and function of an RNA directly from its nucleotide sequence. The experimental techniques and modelling frameworks discussed here are also highly relevant for the sampling of sequence-structure-function space of DNAs and proteins.

  View details for DOI 10.1038/s41576-022-00567-5

  View details for PubMedID 36635406

• Design of Tunable Oscillatory Dynamics in a Synthetic NF-kappa B Signaling Circuit CELL SYSTEMS Zhang, Z., Wang, Q., Ke, Y., Liu, S., Ju, J., Lim, W. A., Tang, C., Wei, P. 2017; 5 (5): 460-+

  Abstract

  Although oscillatory circuits are prevalent in transcriptional regulation, it is unclear how a circuit's structure and the specific parameters that describe its components determine the shape of its oscillations. Here, we engineer a minimal, inducible human nuclear factor κB (NF-κB)-based system that is composed of NF-κB (RelA) and degradable inhibitor of NF-κB (IκBα), into the yeast, Saccharomyces cerevisiae. We define an oscillation's waveform quantitatively as a function of signal amplitude, rest time, rise time, and decay time; by systematically tuning RelA concentration, the strength of negative feedback, and the degradation rate of IκBα, we demonstrate that peak shape and frequency of oscillations can be controlled in vivo and predicted mathematically. In addition, we show that nested negative feedback loops can be employed to specifically tune the frequency of oscillations while leaving their peak shape unchanged. In total, this work establishes design principles that enable function-guided design of oscillatory signaling controllers in diverse synthetic biology applications.

  View details for DOI 10.1016/j.cels.2017.09.016

  View details for Web of Science ID 000416533900007

  View details for PubMedID 29102361