Biology subjects

Bae, J.

Publications and source records attributed to Bae, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Inhaled black carbon induces depressive-like behavior and enhances stress-related blood-brain molecular vulnerability in mice

Black carbon (BC), a combustion-derived component of fine particulate matter, has been linked to depressive symptoms, but controlled experimental evidence remains limited. We established a controlled BC inhalation model combined with chronic restraint stress (CRS) to determine whether inhaled BC alone induces depressive-like behavior and whether concurrent stress enhances behavioral and molecular vulnerability. Male C57BL/6J mice were assigned to Control, CRS, BC, or BC+CRS groups and exposed for 21 consecutive days, followed by behavioral testing and molecular analyses of plasma-depleted whole blood and stress-related brain regions. BC exposure alone induced depressive-like behavior, and the combined BC+CRS condition showed the most pronounced phenotype. These findings indicate that inhaled BC is sufficient to influence stress-relevant behavior and may heighten vulnerability under chronic stress. At the molecular level, BC shifted peripheral responses toward a stress- and inflammation-associated state with reduced plasticity-related signaling, whereas CRS preferentially engaged glucocorticoid-responsive regulation. Combined BC+CRS exposure further altered plasticity- and transcription-related regulatory programs in blood and stress-related brain regions, with prominent changes in the nucleus accumbens. These condition-dependent molecular patterns suggest that BC engages blood-brain stress-related pathways in a context- and region-specific manner. Together, these findings identify inhaled BC as a neurobehaviorally relevant environmental hazard.

neuroscience↗

bioRxiv2026

Thal-Kak: unifying biomolecular structure predictors reveals a sampling-selection gap

Complementary all-atom structure predictors sample different solutions, but how to allocate a fixed sampling budget across them and select the best output remains unclear. Thal-Kak unifies five released predictors under shared upstream inputs and a common schema. Across FoldBench and CASP16, model mixing improves oracle sampling over single-model runs, but selection remains a bottleneck because confidence scores do not transfer across models and existing quality-assessment methods cannot resolve this gap.

bioinformatics↗

bioRxiv2026

Spatial transcriptomics reveals D2-associated synaptic transcriptional attenuation in the chronically stressed dorsal striatum

Chronic stress alters striatal functions involved in motivation, action selection, and behavioral adaptation, yet cell-type-associated transcriptional organization in the dorsal striatum remains unclear. We used RNAscope-guided GeoMx spatial transcriptomics to compare D1 and D2 neuronal compartments in matched dorsal striatal regions after chronic restraint stress (CRS). CRS engaged both populations and produced comparable numbers of differentially expressed genes. Gene set enrichment analysis revealed partially overlapping CRS-associated pathway attenuation in D1 and D2 neurons, indicating stress-responsive transcriptional organization in both populations. However, D2 responses showed more coherent convergence around receptor-trafficking and synaptic signaling programs, including AMPA receptor trafficking and EPHB-mediated signaling. Moreover, under the same threshold-defined DEG criteria, CRS-downregulated D2 genes resolved into synapse-centered functional annotation categories, including glutamatergic synapse, postsynaptic organization, and dendritic spine, whereas D1 gene sets did not show a comparable pattern. These findings provide a framework for comparing stress-associated D1/D2 transcriptional organization in the dorsal striatum. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/737112v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@a57a73org.highwire.dtl.DTLVardef@a2cf8org.highwire.dtl.DTLVardef@e6ea0org.highwire.dtl.DTLVardef@180f997_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Healthy-to-Stroke Translation of EEG-Based BMIs: EEG Characterization and Reinforcement Learning-Based Decoder Evaluation

PurposeEEG-based brain-machine interfaces (BMIs) may support assistive technologies for individuals with stroke-related motor impairment by translating neural activity into control commands for external devices. However, post-stroke neural reorganization and interindividual EEG variability challenge reliable decoding. This study characterized motor imagery EEG features in healthy and acute stroke participants and evaluated whether population-trained Q-learning Kernel Temporal Difference (Q-KTD) decoders could improve individual stroke decoding through transfer learning. These analyses assess the feasibility of healthy-to-stroke translation for EEG-based BMI neural decoding. Materials and MethodsPublicly available motor imagery EEG datasets from healthy participants (n = 109) and individuals with acute stroke (n = 50) were analyzed using left- and right-hand motor imagery trials. The datasets were selected because of their relatively large sample sizes and comparable motor imagery tasks. EEG characterization included baseline and motor imagery-period band power, ERD/ERS, hemispheric asymmetry, and time-frequency representations. For Q-learning Kernel Temporal Difference (Q-KTD) decoding, filtered time-domain EEG from 0- 0.5 s after motor imagery onset was used as the neural-state input. A Q-KTD model trained on the healthy population was transferred to individual stroke participants, and repeated Monte Carlo simulations compared decoding performance with and without transfer learning across multiple learning epochs. ResultsHealthy and acute stroke participants showed shared motor imagery-related EEG structure, including post-onset mu-band suppression, while the stroke group exhibited greater interparticipant variability, more diffuse time- frequency modulation, and altered hemispheric asymmetry. No channel-level healthy-stroke differences in windowed band power remained significant after false discovery rate correction. Healthy-source transfer learning improved first-epoch Q-KTD success rates in 29 of 50 stroke participants (58%). Across all participants, mean success rate increased from 49.46% without transfer learning to 51.82% with transfer learning. Among participants showing positive transfer, the mean gain was 7.34% and the maximum gain was 18.75%. However, 21 participants showed negative transfer, demonstrating substantial subject-level variability. ConclusionHealthy-source Q-KTD transfer learning improved first-epoch motor imagery BMI decoding for a majority of acute stroke participants, supporting the offline feasibility of population-informed Q-KTD decoding in stroke. These early performance gains may reduce subject-specific calibration burden, although substantial interparticipant variability and negative transfer indicate the need for individualized transfer-selection or adaptation strategies. Assistive Technology ImplicationsO_LIEEG-based brain-machine interfaces may support assistive technologies for individuals with stroke-related motor impairment by translating motor imagery-related neural activity into control commands for external devices. C_LIO_LIHealthy-to-stroke transfer learning may improve early BMI neural-decoder performance and potentially reduce the amount of subject-specific calibration required. C_LIO_LIThe findings support the offline feasibility of Q-KTD for motor imagery BMI neural decoding in individuals with acute stroke. C_LIO_LISubstantial interparticipant variability and negative transfer suggest that individualized source-model selection or adaptation strategies may be needed for reliable post-stroke BMI implementation. C_LIO_LIPhysiological EEG characteristics, including ERD/ERS and hemispheric asymmetry, may provide candidate markers for future transfer-selection strategies, although their predictive value requires direct validation. C_LI

bioengineering↗

bioRxiv2026

Microenvironmental Determinants of Reaction Kinetics in Biomolecular Condensates Probed with Protein Ligation

Cells utilize liquid-liquid phase separation to organize biochemical reactions within biomolecular condensates, which function as membraneless organelles. Although these assemblies are known to enhance reaction rates by concentrating reactants, the mechanisms beyond simple mass-action effects remain poorly understood. Here, we examined how the physicochemical microenvironment within condensates modulates reaction kinetics using spontaneous protein ligation as a model reaction, conducting a systematic analysis across various condensates, ranging from structured scaffolds (PRM-SH3 systems) to intrinsically disordered protein (IDP)-based scaffolds such as LAF, TAF, and FUS. We designed a FRET-based proximity-sensitive client probe to quantify increases in effective local concentration arising from excluded-volume effects. In parallel, we measured internal hydrophilicity and water activity, revealing them as additional key determinants of reaction acceleration. Together, the findings presented here elucidate how phase-separated compartments regulate biochemical reactions through the interplay of physical (effective concentration) and chemical (hydrophilicity and water activity) microenvironments and provide mechanistic insights for engineering condensates with tunable reactivity.

biochemistry↗

bioRxiv2026

Microfluidic Agarose Microdroplets for DNA-Encoded Chemical Library Screening

DNA-encoded library (DEL) technology enables high-throughput small-molecule discovery but is typically performed using purified proteins under in vitro conditions that do not reflect native intracellular environments. Here, we present a microfluidic agarose -droplet platform for cellular-context DEL screening. The porous hydrogel droplets provide mechanically stable yet permeable microenvironments that protect weak protein-ligand interactions while enabling efficient buffer exchange and ligand diffusion. Importantly, mild cell permeabilization within droplets selectively retained chromatin-associated proteins, allowing screening directly in a cellular context. Using BRD4 as a model target, we validated intracellular ligand engagement by fluorescence imaging and super-resolution microscopy. Small-scale DEL screening selectively enriched JQ1 in both bead-based and cell-based formats, and large-scale DEL screening across millions of encoded compounds successfully identified hit molecules by sequencing. This agarose -droplet-based strategy expands DEL technology toward biologically relevant and chromatin-associated targets under near-native conditions.

bioengineering↗

bioRxiv2025

A machine learning framework for predicting and modulating condition-dependent protein phase separation

Protein phase separation is a fundamental process in organizing membraneless organelles and is implicated in a wide range of pathological conditions. Importantly, rather than being a static feature of specific proteins, phase separation is a condition-dependent phenomenon governed by environmental parameters, including protein concentration, temperature, and solvent composition. However, most existing machine learning models infer phase-separation propensity solely from amino-acid sequences, failing to capture these context-dependent behaviors. Here, we present LLPSense, a machine learning framework that integrates pre-trained protein language model embeddings with environmental parameters to achieve accurate, condition-aware predictions of protein phase separation. We demonstrate LLPSenses predictive power and utility through three key experimental demonstrations. First, the model revealed that SGTA, previously unrecognized as a phase-separating protein, exhibits complex, temperature-dependent reentrant phase behavior. Second, LLPSense accurately predicted mutations in -synuclein that either enhance or suppress phase separation, enabling systematic mapping of residues potentially relevant to Parkinsons disease. Third, using model-guided mutagenesis, we inverted the phase behavior of UBQLN4, shifting it from high-temperature to low-temperature separation. Collectively, LLPSense provides a robust computational tool for interrogating the condition-dependent landscape of protein phase separation, enabling mechanistic studies of disease-associated phase separation and the rational design of programmable condensates.

bioinformatics↗

bioRxiv2025

MeCP2 regulates cell type-specific functions of depressive-like symptoms in the nucleus accumbens

MeCP2 (methyl CpG binding protein 2) is a transcriptional regulator that modulates gene expression in response to environmental stimuli. Although recent studies have implicated MeCP2 in stress responses and depression, its precise role is not completely understood. In this study, we identify a cell type-specific function of MeCP2 in the regulation of depression-like symptoms within the nucleus accumbens (NAc), a key brain region for emotional and stress processing. We observed differential MeCP2 expression in distinct cell populations of the NAc following chronic restraint stress (CRS) and investigated the behavioral and electrophysiological consequences of cell type-specific MeCP2 manipulation. We also explored the molecular mechanisms by which MeCP2 alleviates depression-like symptoms in the NAc and associated neural circuit regions through cell type-specific profiling of the spatial transcriptome. Our findings demonstrate that MeCP2 contributes to synaptic and circuit-level regulation in a cell type-specific manner within the NAc and ultimately mitigates CRS-induced depression-like behaviors. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/666523v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1ae5875org.highwire.dtl.DTLVardef@1319231org.highwire.dtl.DTLVardef@174ef60org.highwire.dtl.DTLVardef@fb061_HPS_FORMAT_FIGEXP M_FIG Graphical abstract.Schematic Summary of the functional role of accumbal MeCP2 C_FIG

neuroscience↗

bioRxiv2025

A Large Electroencephalogram Database of Freewill Reaching and Grasping Tasks for Brain Machine Interfaces

Brain machine interfaces (BMIs) offer great potential to improve the quality of life for individuals with neurological disorders or severe motor impairments. Among various neural recording modalities, electroencephalogram (EEG) is particularly favorable for BMIs due to its noninvasive nature, portability, and high temporal resolution. Existing EEG datasets for BMIs are often limited to experimental settings that fail to address subjects freewill in decision making. We present a large EEG dataset, containing a total of 6808 trials, recorded from 23 healthy young adults (8 females and 15 males with an age range from 18 to 24 years) while performing reaching and grasping tasks, where the target object is freely chosen at their desired pace according to their own will. This EEG dataset provides a realistic representation of reaching and grasping movement, making it useful for developing practical BMIs.

neuroscience↗

bioRxiv2024

Impact of exogenous aminoacyl-tRNA synthetase and tRNA on temperature sensitivity in Escherichia coli: towards a genetic code-expanded biological logic gate

Genetic code expansion (GCE) is a powerful strategy that expands the genetic code of an organism for incorporating non-canonical amino acids (ncAAs) into proteins using engineered tRNAs and aminoacyl-tRNA synthetases (aaRSs). While GCE has opened up new possibilities for synthetic biology, little is known about the potential side effects of exogenous aaRS/tRNA pairs. In this study, we investigated the impact of exogenous aaRS and amber suppressor tRNA on gene expression in Escherichia coli. We discovered that in DH10{beta} {Delta}cyaA, transformed with the F1RP/F2P two-hybrid system, high consumption rate of cellular ATP by exogenous aaRS/tRNA at elevated temperatures induces temperature sensitivity in the expression of genes regulated by the catabolite activator protein. We harnessed this temperature sensitivity to create a novel biological AND gate in E. coli, responsive to both p-benzoylphenylalanine (BzF) and low temperature, using a BzF-dependent variant of E. coli chorismate mutase and split subunits of Bordetella pertussis adenylate cyclase. Our study provides new insights into the unexpected effects of exogenous aaRS/tRNA pairs and offers a new approach for constructing a biological logic gate.

synthetic biology↗

bioRxiv2024

Frontal engagement in perceptual integration under low subjective visibility

We investigated how spatiotemporal neural dynamics underlying perceptual integration changed with the degree of conscious access to a set of backward-masked pacman-shaped inducers that generated the percept of an illusory triangle. We kept the stimulus parameters at a fixed near-threshold level throughout the experiment and recorded electroencephalography from participants who reported the orientation and subjective visibility of the illusory triangle on each trial. Our multivariate pattern analysis revealed that posterior and central regions initially used dynamic neural code and later switched to stable neural code. The transition from dynamic to stable neural code in posterior region occurred increasingly later and eventually disappeared with decreasing conscious access. Anterior region primarily used stable neural code which waned with decreasing conscious access, but increased at below-median visibility and remained even when stimulus awareness was minimal. These results demonstrate differential spatiotemporal neural dynamics underlying perceptual integration depending on conscious access and emphasize a unique role of anterior region in processing integrated shape information especially under low subjective visibility.

neuroscience↗

bioRxiv2024

Targeted Degradation of METTL3 Against Acute Myeloid Leukemia and Gastric Cancer.

Accumulating evidence reveals the oncogenic role of methyltransferase-like 3 (METTL3) in a variety of cancer types, either dependent or independent of its m6A methyl transferase activity. We have designed proteolysis-targeting chimeras (PROTACs) targeting METTL3 and identified KH12 as a potent METTL3 degrader. Treatment of KH12 on MOLM-13 cells causes more than 80% degradation of METTL3 with a half-maximal degradation concentration (DC50) of 220 nM in a dose-, time- and ubiquitin-dependent fashion. In addition, KH12 reverses differentiation and possesses anti-proliferative effects surpassing the reported inhibitors in MOLM-13 cells. Furthermore, KH12 significantly suppresses the growth of various gastric cancer (GC) cells, where the m6A-independent activity of METTL3 plays a crucial role in tumorigenesis. The anti-GC effect of KH12 was further confirmed in patient-derived organoids (PDOs). This study highlights the therapeutic potential of targeted degradation of epitranscriptomic writer METTL3 as an anti-cancer strategy.

biochemistry↗

bioRxiv2023

Integrated Proteomics Identifies Troponin I Isoform Switch as a Regulator of a Sarcomere-Metabolism Axis During Cardiac Regeneration

Adult mammalian cardiomyocytes have limited proliferative potential, and after myocardial infarction (MI), injured cardiac tissue is replaced with fibrotic scar rather than with functioning myocardium. In contrast, the neonatal mouse heart possesses a regenerative capacity governed by cardiomyocyte proliferation; however, a metabolic switch from glycolysis to fatty acid oxidation during postnatal development results in loss of this regenerative capacity. Interestingly, a sarcomere isoform switch also takes place during postnatal development where slow skeletal troponin I (ssTnI) is replaced with cardiac troponin I (cTnI). In this study, we first employ integrated quantitative bottom-up and top-down proteomics to comprehensively define the proteomic and sarcomeric landscape during postnatal heart maturation. Utilizing a cardiomyocyte-specific ssTnI transgenic mouse model, we found that ssTnI overexpression increased cardiomyocyte proliferation and the cardiac regenerative capacity of the postnatal heart following MI compared to control mice by histological analysis. Our global proteomic analysis of ssTnI transgenic mice following MI reveals that ssTnI overexpression induces a significant shift in the cardiac proteomic landscape. This shift is characterized by an upregulation of key proteins involved in glycolytic metabolism. Collectively, our data suggest that the postnatal TnI isoform switch may play a role in the metabolic shift from glycolysis to fatty acid oxidation during postnatal maturation. This underscores the significance of a sarcomere-metabolism axis during cardiomyocyte proliferation and heart regeneration.

developmental biology↗

bioRxiv2023

mTORC1 Regulates the Metabolic Switch of Postnatal Cardiomyocytes During Regeneration

The metabolic switch from glycolysis to fatty acid oxidation in postnatal cardiomyocytes contributes to the loss of the cardiac regenerative potential of the mammalian heart. However, the mechanisms that regulate this metabolic switch remain unclear. The protein kinase complex mechanistic target of rapamycin complex 1 (mTORC1) is a central signaling hub that regulates cellular metabolism and protein synthesis, yet its role during mammalian heart regeneration and postnatal metabolic maturation is undefined. Here, we use immunoblotting, rapamycin treatment, myocardial infarction, and global proteomics to define the role of mTORC1 in postnatal heart development and regeneration. Our results demonstrate that the activity of mTORC1 is dynamically regulated between the regenerating and the non-regenerating hearts. Acute inhibition of mTORC1 by rapamycin or everolimus reduces cardiomyocyte proliferation and inhibits neonatal heart regeneration following injury. Our quantitative proteomic analysis demonstrates that transient inhibition of mTORC1 during neonatal heart injury did not reduce protein synthesis, but rather shifts the cardiac proteome of the neonatal injured heart from glycolysis towards fatty acid oxidation. This indicates that mTORC1 inhibition following injury accelerates the postnatal metabolic switch, which promotes metabolic maturation and impedes cardiomyocyte proliferation and heart regeneration. Taken together, our results define an important role for mTORC1 in regulating postnatal cardiac metabolism and may represent a novel target to modulate cardiac metabolism and promote heart regeneration.

developmental biology↗

bioRxiv2023

Stereotypic persistent B cell receptor clonotypes in Alzheimer's Disease

We constructed B cell receptor (BCR) repertoires in silico using peripheral blood (PB) samples collected from 44 Alzheimers Disease (AD) patients at baseline and 37 patients at follow-up. For the control group (CG), we used BCR repertoire data from the chronologically collected PB samples of 55 healthy volunteers vaccinated with SARS-CoV-2 mRNA. The AD patients shared 3,983 stereotypic non-naive BCR clonotypes not found in CG, and their degree of overlap between patient pairs were significantly higher than that of CG pairs, even with the SARS-CoV-2 spike protein triggering a concerted BCR response. Twenty stereotypic non-naive AD patient-specific BCR clonotypes co-existed in more than four patients and persisted throughout two sampling points. One of these BCR clonotypes encoded an antibody reactive to the A{beta}42 peptide. Our findings strongly suggest that AD patients are exposed to common (auto)antigens associated with disease pathology, and their BCR repertoires show unique signatures with diagnostic potential.

immunology↗

bioRxiv2023

Defining Cardiac Nerve Architecture During Development, Disease, and Regeneration

Cardiac nerves regulate neonatal mouse heart regeneration and are susceptible to pathological remodeling following adult injury. Understanding cardiac nerve remodeling can lead to new strategies to promote cardiac repair. Our current understanding of cardiac nerve architecture has been limited to two-dimensional analysis. Here, we use genetic models, whole-mount imaging, and three-dimensional modeling tools to define cardiac nerve architecture and neurovascular association during development, disease, and regeneration. Our results demonstrate that cardiac nerves sequentially associate with coronary veins and arteries during development. Remarkably, our results reveal that parasympathetic nerves densely innervate the ventricles. Furthermore, parasympathetic and sympathetic nerves develop synchronously and are intertwined throughout the ventricles. Importantly, the regenerating myocardium reestablishes physiological innervation, in stark contrast to the non-regenerating heart. Mechanistically, reinnervation during regeneration is dependent on collateral artery formation. Our results reveal how defining cardiac nerve remodeling during homeostasis, disease, and regeneration can identify new therapies for cardiac disease.

developmental biology↗

bioRxiv2021

Duplex-Repair enables highly accurate sequencing, despite DNA damage

Accurate DNA sequencing is crucial in biomedicine. Underlying the most accurate methods is the assumption that a mutation is true if altered bases are present on both strands of the DNA duplex. We now show that this assumption can be wrong. We establish that current methods to prepare DNA for sequencing, via End Repair/dA-Tailing, may substantially resynthesize strands, leading amplifiable lesions or alterations on one strand to become indiscernible from true mutations on both strands. Indeed, we discovered that 7-17% and 32-57% of interior duplex base pairs from cell-free DNA and formalin-fixed tumor biopsies, respectively, could be resynthesized in vitro and potentially introduce false mutations. To address this, we present Duplex-Repair, and show that it limits interior duplex base pair resynthesis by 8- to 464-fold, rescues the impact of induced DNA damage, and affords up to 8.9-fold more accurate duplex sequencing. Our study uncovers a major Achilles heel in sequencing and offers a solution to restore high accuracy.

genomics↗

bioRxiv2021

Experimental Confirmation of Multiple Co-Existent DNA Secondary Structures using Low-Yield Bisulfite Sequencing

Predicting DNA secondary structures is critical to a broad range of applications involving single-stranded DNA (ssDNA), yet remains an open problem. Existing prediction models are limited by insufficient experimental data, due to a lack of high-throughput methods to study DNA structures, in contrast to RNA structures. Here, we present a method for profiling DNA secondary structures using multiplexed low-yield bisulfite sequencing (MLB-seq), which examines the chemical accessibility of cytosines in thousands of different oligonucleotides. By establishing a probability-based model to evaluate the consensus probability between MLB-seq data and structures proposed using NUPACK software, we identified the secondary structures of individual ssDNA molecules and estimated the distribution of multiple secondary structures in solution. We studied the structures of 1,057 human genome subsequences and experimentally confirmed that 84% adopted two or more structures. MLB-seq thus enables high-throughput ssDNA structure profiling and will benefit the design of probes, primers, aptamers, and genetic regulators.

molecular biology↗