Biology subjects

Lee, D. G.

Publications and source records attributed to Lee, D. G..

12 recordsLinked to original sources

bioRxiv2026

Electrostatic Self-Assembly of Protein Nanoparticles from Intrinsically Disordered Polypeptide and Globular Protein Pairs

Intracellular delivery of folded, functional proteins remains a critical barrier to realizing the full potential of protein therapeutics. We present a fully aqueous, genetically encoded platform for assembling micellar protein nanoparticles via electrostatic coacervation between anionic globular proteins and multi-domain intrinsically disordered proteins (IDPs) composed of a neutral elastin-like polypeptide (ELP) domain and a cationic disordered histone-derived domain (H5). This biosynthetic system allows modular control over charge density, neutral domain length, and stoichiometry, enabling the formation of homogeneous micellar nanoparticles with high protein payload retention under mild, biologically relevant conditions without organic solvents or covalent modification. Unlike amphiphilic micelle systems, globular proteins in this approach act as both cargo and assembly drivers, providing a direct handle to tune encapsulation. Nanoparticle assembly was characterized with a suite of complementary techniques including DLS, FCS, TEM, and SAXS, enabling detailed analysis of nanoparticle structure, size, and composition. The system exhibits remarkable tolerance to different formulations, consistently producing well-defined particles with low dispersity. This expanded design space arises from the use of structured, charge-regulating proteins and confers exceptional versatility in formulation. Different charge fractions modulate not only nanoparticle size but also physicochemical properties such as core density and cargo loading, which may be critical for tuning nanoparticle stability and performance. Together, these features establish a robust and programmable platform for protein-based nanoparticle engineering with potential application in intracellular protein delivery.

bioengineering↗

bioRxiv2025

Choice-induced preference change under a sequential-sampling model framework

Sequential sampling models of choice, such as the drift-diffusion model (DDM), are frequently fit to empirical data to account for a variety of effects related to accuracy/consistency, response time (RT), and sometimes confidence. However, no model in this class has been shown to account for the phenomenon known as choice-induced preference change, wherein decision makers tend to rate options higher after they choose them and lower after they reject them (and often choose the option that they had initially rated lower). Studies have reported choice-induced preference change for many decades, and the principal findings are robust. The resulting spreading of alternatives (SoA) in terms of their subjective value ratings is incompatible with traditional sequential sampling models, which consider the rated values of the options to be stationary throughout choice deliberation. Here, we propose that extending the basic DDM to incorporate independent attributes into the drift rate can allow this class of model to account for SoA. Critically, the extended model assumes that choice deliberation does not always involve the same set of attributes as is involved in individual evaluations of the same options. We show that this model can generate SoA (while simultaneously accounting for consistency and RT), as well as the relationships between SoA and choice difficulty, attribute disparity, and RT previously reported in the literature.

neuroscience↗

bioRxiv2024

Human-mouse proteomics reveals the shared pathways in Alzheimer's disease and delayed protein turnover in the amyloidome

Murine models of Alzheimers disease (AD) are crucial for elucidating disease mechanisms but have limitations in fully representing AD molecular complexities. We comprehensively profiled age-dependent brain proteome and phosphoproteome (n > 10,000 for both) across multiple mouse models of amyloidosis. We identified shared pathways by integrating with human metadata, and prioritized novel components by multi-omics analysis. Collectively, two commonly used models (5xFAD and APP-KI) replicate 30% of the human protein alterations; additional genetic incorporation of tau and splicing pathologies increases this similarity to 42%. We dissected the proteome-transcriptome inconsistency in AD and 5xFAD mouse brains, revealing that inconsistent proteins are enriched within amyloid plaque microenvironment (amyloidome). Determining the 5xFAD proteome turnover demonstrates that amyloid formation delays the degradation of amyloidome components, including A{beta}-binding proteins and autophagy/lysosomal proteins. Our proteomic strategy defines shared AD pathways, identify potential new targets, and underscores that protein turnover contributes to proteome-transcriptome discrepancies during AD progression.

biochemistry↗

bioRxiv2024

Cyclic Electromagnetic DNA Simulation (CEDS) influences the functions of Long Oligo-dsDNAs and Plasmid DNAs

Hydrogen bonding magnetic resonance-based cyclic electromagnetic DNA simulation (CEDS) was proposed to play a role in regulating the function of target short oligo-dsDNA in the previous study, as the decagonal CEDS was found to induce the hybridization and conformational changes of target short oligo-dsDNA in a sequence-specific manner. This study applied dodecagonal CEDS on 24 bps oligo-dsDNAs and plasmid DNAs. CEDS can affect 24 bps oligo-dsDNAs intercalated by ethidium bromide (EtBr) or condensed by spermidine by stimulating them to partially remove EtBr or spermidine, respectively, depending on CEDS time. When the multiple cloning site of pBluescript was digested with eight restriction endonucleases (RE) under CEDS separately, all RE digests were enhanced depending on CEDS time compared to the negative and positive controls both in EtBr intercalated electrophoresis and HPLC analysis. In vitro RNA transcription from human VEGFA, vWF or elafin cDNA subcloned into pBluescript DNA was increased by CEDS using each promoter sequence depending on CEDS time compared to negative and positive controls. In addition, the productions of green fluorescent protein (GFP) and {beta}-galactosidase were enhanced by CEDS using each promoter sequence from pE-GFP-1 and pBluescript, respectively. In the survival assay using E. coli transfected with pBluescript containing ampicillin resistance gene, the number of surviving E. coli was increased by CEDS using a T3 promoter sequence compared to the control during two days of culture, while it was decreased by CEDS using a T7 promoter, non-specific 12A, 6(TA), or mutation-2 (GG18,19CC) T3 promoter sequence. Therefore, it is suggested that dodecagonal CEDS can target 24 bps oligo-dsDNAs and promoter sequences of plasmid DNAs in a sequence-specific manner and influence their functions.

molecular biology↗

bioRxiv2024

Hydrogen Bonding Magnetic Resonance (HBMR)-based Cyclic Electromagnetic DNA Simulation (CEDS) can affect DNA Hybridization and Conformation

The proton magnetic resonance is commonly used in MRI machines with a strong magnetic field of over 1 T, while this study hypothesized that a weak magnetic field below 0.01 T can induce the proton magnetic resonance in hydrogen bonds of double-stranded DNA (dsDNA). This study found the hydrogen bonding magnetic resonance (HBMR) in dsDNA can modulate the conformations and functions of dsDNAs (1). Decagonal or dodecagonal cyclic electromagnetic DNA simulation (CEDS) was designed to target three-dimensional structures of randomly oriented dsDNAs with about 25% efficiency. This study found that the most effective magnetic exposure times for inducing an electric potential in A-T and G-C base pairs were 280 and 480 msec, respectively. Decagonal CEDS using a target sequence at 20-25 Gauss for 30 min was able to induce sequence-specific hybridization of target short oligo-dsDNAs in 0.005M NaCl solution and their unique conformation in 0.1M NaCl solution. There was a tendency that Pyu oligo-dsDNAs showed more hybridization and unique conformational changes by CEDS using a target sequence than Puy oligo-dsDNAs. This CEDS effect on Pyu ds6(2C2A) increased with CEDS time up to 90 min and gradually decreased to about half (51.8%) of the increase at 240 min resting time. CEDS influenced the oligo-dsDNAs to increase their infrared (IR) absorbance at approximately 3700-2800 cm-1 band compared to the positive and negative controls, which was more dominant in Pyu oligo-dsDNAs than in Puy oligo-dsDNAs. Therefore, it is postulated that HBMR-based CEDS using a weak magnetic field can increase the hybridization potential of oligo-dsDNAs and subsequently lead to unique DNA conformation required for the initiation of various DNA functions. Therefore, it is suggested that decagonal CEDS play a role in regulating the function of target short oligo-dsDNA.

molecular biology↗

bioRxiv2024

DNA Base Pair Polarities of Pyrimidine(s)-Purine(s) (Pyu) dsDNA induce stronger Hybridization and unique DNA Conformation compared to Purine(s)-Pyrimidine(s) (Puy) dsDNA

The present study introduces a concept of DNA base pair polarity in double-stranded DNA (dsDNA), which has led the serial investigation of hydrogen bonding magnetic resonance-based gene regulation. DNA base pair polarity is characterized by hydrogen bonding between electrostatically positive purine and negative pyrimidine, exerting the stabilization and organization of dsDNA. Comparison of DNA hybridization potential between Pyu (pyrimidine(s)-purine(s)) and Puy (purine(s)-pyrimidine(s)) oligo-dsDNAs in EtBr intercalated electrophoresis showed that Pyu ds(nTnA) (n=3, 4,5,6,7,8,10,12) and ds(nCnA) (n=7,8,10,12) exhibit stronger hybridization than Puy ds(nAnT) and ds(nAnC), respectively. In the HPLC for DNA conformation change in 0.1M NaCl solution, Pyu oligo-dsDNA tended to have a larger peak area than Puy oligo-dsDNA, indicating that the former had a more extended and organized DNA conformation than the latter. FT-IR results showed that Pyu ds3T3A, ds3C3G, dsCTCGAG, and dsTCTAGA showed higher IR absorbance at 3400-3200 cm-1 than Puy ds3A3T, ds3G3C, dsGAGCTC, and dsAGATCT, respectively, indicating Pyu oligo-dsDNAs have increased N-H stretching in hybridization than Puy oligo-dsDNAs. In vitro RNA transcription from template plasmid DNAs inserted with six segments of Pyu ds6(TCTGA) and positive ds14(GA) produced more RNA than those with seven segments of palindromic Pyu ds(CATG) and negative ds14(CT), respectively, indicating the former plays a stimulating role for RNA transcription while the latter plays an inhibitory role. When comparing PCR primers between Pyu ds12/n(nCnA) and Puy ds12/n(nAnC) (n=1,2,3,4,6,12), Pyu primers can easily hybridize on template DNA and subsequently produce abundant DNA compared to Puy primers. The data suggest that Pyu oligo-dsDNAs hybridize strongly and form distinctive conformation, thereby stimulating RNA transcription more than Puy oligo-dsDNAs. Since Pyu oligo-dsDNA appears to preserve DNA signal and conformation relevant to DNA function more efficiently than Puy dsDNA, it is proposed that the entire DNA code can be divided into Pyu dsDNAs with unique DNA base pair polarities.

molecular biology↗

bioRxiv2023

Perirhinal Cortex Learns A Predictive Map of The Task Environment

Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associated outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying sequential whisker stimuli over multiple training stages. Chemogenetic inactivation demonstrated that Prh is involved in task learning. Chronic two-photon calcium imaging, population analysis, and computational modeling revealed that Prh encodes stimulus features as sensory prediction errors. Prh forms stable stimulus-outcome associations that expand in a retrospective manner and generalize as animals learn new contingencies. Stimulus-outcome associations are linked to prospective network activity encoding possible expected outcomes. This link is mediated by cholinergic signaling to guide task performance, demonstrated by acetylcholine imaging and perturbation. We propose that Prh combines error-driven and map-like properties to acquire a predictive map of learned task behavior.

neuroscience↗

bioRxiv2023

The online metacognitive control of decisions

Difficult decisions typically involve mental effort, which scales with the deployment of cognitive (e.g., mnesic, attentional) resources engaged in processing decision-relevant information. But how does the brain regulate mental effort? A possibility is that the brain optimizes a resource allocation problem, whereby the amount of invested resources balances its expected cost (i.e. effort) and benefit. Our working assumption is that subjective decision confidence serves as the benefit term of the resource allocation problem, hence the "metacognitive" nature of decision control. Here, we present a computational model for the online metacognitive control of decisions or oMCD. Formally, oMCD is a Markov Decision Process that optimally solves the ensuing resource allocation problem under agnostic assumptions about the inner workings of the underlying decision system. We demonstrate how this makes oMCD a quasi-optimal control policy for a broad class of decision processes, including -but not limited to- progressive attribute integration. We disclose oMCDs main properties (in terms of choice, confidence and response time), and show that they reproduce most established empirical results in the field of value-based decision making. Finally, we discuss the possible connections between oMCD and most prominent neurocognitive theories about decision control and mental effort regulation.

animal behavior and cognition↗

bioRxiv2022

Choice-Induced Preference Change under a Sequential Sampling Model Framework

Sequential sampling models of choice, such as the drift-diffusion model (DDM), are frequently fit to empirical data to account for a variety of effects related to accuracy/consistency, response time (RT), and sometimes confidence. However, no model in this class can account for the phenomenon known as choice-induced preference change, where decision makers tend to rate options higher after they choose them and lower after they reject them. Studies have reported choice-induced preference change for many decades, and the principal findings are robust. The resulting spreading of alternatives (SoA) in terms of their subjective value ratings is incompatible with traditional sequential sampling models, which consider the rated values of the options to be stationary throughout choice deliberation. Here, we propose an extension of the basic DDM that allows the drift rate to vary across deliberation time depending on which attributes are attended to at which points in time. Critically, the model assumes that choice deliberation commences based only on the more salient attributes of the options, and that additional attributes eventually come into consideration when the decision cannot be resolved based on the initial attributes alone. We show that this model can account for SoA (in addition to choice consistency and RT), as well as all previously reported relationships between SoA and choice difficulty, attribute disparity, and RT.

neuroscience↗

bioRxiv2021

Evidence Accumulates for Individual Attributes during Value-Based Decisions

When choosing between different options, we tend to consider specific attribute qualities rather than deliberating over some general sense of the options overall values. The importance of each attribute together with its quality will determine our preference rankings over the available alternatives. Here, we test the hypothesis that the most prominent class of model for simple decisions - sequential sampling or evidence accumulation to bound - can be bolstered by explicitly incorporating variables related to individual attributes in addition to the standard usage of overall value estimates. We examine six datasets in which participants evaluated snack foods both in terms of overall value and individual attributes, then chose between pairs of the same snacks, and show that only models that explicitly incorporate information about the individual attributes are able to reproduce fundamental patterns in the choice data, such as the influence of attribute disparity on decisions, and such models provide quantitatively better fits to the choice outcomes, response times, and confidence ratings compared to models based on overall value alone. Our results provide important evidence that incorporating attribute-level information into computational models helps us to better understand the cognitive processes involved in value-based decision- making.

neuroscience↗

bioRxiv2021

Evidence or Confidence: What Really Accumulates During a Decision?

Assessing our confidence in the choices we make is of paramount importance to making adaptive decisions, and it is thus no surprise that we excel in this ability. However, standard models of decision-making, such as the drift-diffusion model (DDM), treat confidence assessment as a post-hoc or parallel process that does not directly influence the choice, which depends only on accumulated evidence. Here, we pursue the alternative hypothesis that what is monitored during a decision is an evolving sense of confidence (that the to-be-selected option is the best) rather than raw evidence. Monitoring confidence has the appealing consequence that the decision threshold corresponds to a desired level of confidence for the choice, and that confidence improvements can be traded off against the resources required to secure them. We show that most previous findings on perceptual and value-based decisions traditionally interpreted from an evidence-accumulation perspective can be explained more parsimoniously from our novel confidence-driven perspective. Furthermore, we show that our novel confidence-driven DDM (cDDM) naturally generalizes to decisions involving any number of alternative options - which is notoriously extemporaneous using traditional DDM or related models. Finally, we discuss future empirical evidence that could be useful in adjudicating between these alternatives.

neuroscience↗

bioRxiv2019

Is choice-induced preference change due to pre- or post-decision cognitive dissonance resolution?

For more than 60 years, it has been known that people report higher (lower) subjective values for items after having selected (rejected) them during a choice task. This phenomenon is coined \"choice-induced preference change\" or CIPC, and its established interpretation is that of \"cognitive dissonance\" theory. In brief, if people feel uneasy about their choice, they later convince themselves, albeit not always consciously, that the chosen (rejected) item was actually better (worse) than they had originally estimated. While this might make sense from a pragmatic psychological standpoint, it is challenging from a theoretical evolutionary perspective. This is because such a cognitive mechanism might yield irrational biases, whose adaptive fitness would be unclear. In this work, we assume that CIPC is mostly driven by the refinement of option value representations that occurs during (and not after) difficult choices. This makes CIPC the epiphenomenal outcome of a cognitive process that is instrumental to the decision. Critically, our hypothesis implies novel predictions about how observed CIPC should relate to two specific meta-cognitive processes, namely: choice confidence and subjective certainty regarding pre-choice value judgments. We test these predictions in a behavioral experiment where participants rate the subjective value of food items both before and after choosing between equally valued items; we augment this traditional design with reports of choice confidence and subjective certainty about value judgments. The results confirm our predictions and provide evidence against the standard post-choice cognitive dissonance reduction explanation. We then discuss the relevance of our work in the context of the existing debate regarding the putative cognitive mechanisms underlying cognitive dissonance reduction.

neuroscience↗