Biology subjects

Lee, H.

Publications and source records attributed to Lee, H..

At least 289 records · Page 16Linked to original sources

bioRxiv2023

Predicting the efficacy of BH3 mimetics through the profiling of multiple protein complexes

BH3 mimetics are protein-protein interaction (PPI) inhibitors that saturate anti-apoptotic proteins in the BCL2 family to induce apoptosis in cancer cells, a prominent example of protein complex-targeting therapies. Despite the remarkable success of the BH3 mimetic ABT-199 in treating hematological malignancies, only a fraction of patients respond to ABT-199 and eventually develop resistance, necessitating the predictive biomarkers for both initial responses and resistance development. We here used the single-molecule pull-down and co-immunoprecipitation platform to quantify more than 20 different types of PPI complexes using [~]1.2x106 cells in total, revealing the rewired status of the BCL2 family PPI network. By comparing the obtained multi-dimensional data with BH3 mimetic efficacies determined ex vivo, we constructed an analysis model for ABT-199 efficacy that designates the BCL2-BAX and BCLxL-BAK complexes as the primary mediators of drug effectiveness and resistance. We then applied this model to assist in therapeutic decision-making for acute myeloid leukemia patients in a prospective manner. Our work demonstrates a capability for the extensive characterization of PPI complexes in clinical specimens, potentially opening a new avenue of precision medicine for protein complex-targeting therapies.

molecular biology↗

bioRxiv2023

GRIP: Graph representation of immune repertoire using graph neural network and transformer

The immune repertoire is a collection of immune receptors that has emerged as an important biomarker for both the diagnostic and therapeutic of cancer patients. In terms of deep learning, analyzing immune repertoire is a challenging multiple-instance learning problem in which the immune repertoire of an individual is a bag, and the immune receptor is an instance. Although several deep learning methods for immune repertoire analysis are introduced, they consider the immune repertoire as a set-like structure that doesnt take into account the nature of the immune response. When the immune response occurs, mutations are introduced to the immune receptor sequence sequentially to optimize the immune response against the pathogens that enter our body. As a result, immune receptors for the specific pathogen have the lineage of evolution; thus, the immune repertoire is better represented as a graph-like structure. In this work, we present our novel method, graph representation of immune repertoire (GRIP), which analyzes the immune repertoire as a hierarchical graph structure and utilize the collection of graph neural network followed by graph pooling and transformer to efficiently represents the immune repertoire as an embedding vector. We show that GRIP predicts the survival probability of cancer patients better than the set-based methods, and graph-based structure is critical for performance. Also, GRIP provides interpretable results, which prove that GRIP adequately uses the prognosis-related immune receptor and gives the further possibility to use the GRIP as the novel biomarker searching tool.

bioinformatics↗

bioRxiv2023

What humans learn from corrective feedback for perceptual decision-making

Corrective feedback received on perceptual decisions is crucial for adjusting decision-making strategies to improve future choices. However, its complex interaction with other decision components, such as previous stimuli and choices, challenges a principled account of how it shapes subsequent decisions. One popular approach, based on animal behavior and extended to human perceptual decision-making, employs reinforcement learning, a principle proven successful in reward-based decision-making. The core idea behind this approach is that decision-makers, although engaged in a perceptual task, treat corrective feedback as rewards from which they learn choice values. Here, we explore an alternative idea, which is that humans consider corrective feedback on perceptual decisions as evidence of the actual state of the world rather than as rewards for their choices. By implementing these feedback-as-reward and feedback-as-evidence hypotheses on a shared learning platform, we show that the latter outperforms the former in explaining how corrective feedback adjusts the decision-making strategy along with past stimuli and choices. Our work suggests that humans learn about what has happened in their environment rather than the values of their own choices through corrective feedback during perceptual decision-making.

neuroscience↗

bioRxiv2023

Molecular identification of ALDH1A1 and SIRT2 in the astrocytic putrescine-to-GABA metabolic pathway

GABA ({gamma}-aminobutyric acid) is the primary inhibitory neurotransmitter in the CNS. In astrocytes, GABA is synthesized by degradation of putrescine by monoamine oxidase B (MAO-B), a process which is known to mediate tonic inhibition of neuronal excitability. This astrocytic tonic GABA and related enzymes are also reported to be involved in memory impairment in Alzheimers Disease, and therefore are potential therapeutic targets to rescue memory in AD patients. However, the enzymes downstream of MAO-B in this pathway have not been elucidated yet. To fill this gap in knowledge, we performed transcriptomic and literature database analysis and identified Aldehyde dehydrogenase 1 family, member A1 (ALDH1A1) and a histone deacetylase enzyme Sirtuin2 (SIRT2) as plausible candidate enzymes in primary cultured astrocytes. Immunostaining, metabolite analyses, and sniffer patch clamp performed in the presence or absence of suitable inhibitors, or with genetic ablation of the candidate enzymes recapitulated their participation in GABA production. We propose ALDH1A1 and SIRT2 as potential therapeutic targets against Alzheimers Disease.

neuroscience↗

bioRxiv2023

Neural evidence for boundary updating as the source of the repulsive bias in classification

Binary classification, an act of sorting items into two classes by setting a boundary, is biased by recent history. One common form of such biases is repulsive bias, a tendency to sort an item into the class opposite to its preceding items. Sensory-adaptation and boundary-updating are considered as two contending sources of the repulsive bias, yet no neural support has been provided for either source. Here we explored human brains, using fMRI, to find such supports by relating the brain signals of sensory-adaptation and boundary-updating to human classification behavior. We found that the stimulus-encoding signal in the early visual cortex adapted to previous stimuli, yet its adaptation-related changes were dissociated from current choices. Contrastingly, the boundary-representing signals in the inferior-parietal and superior-temporal cortices shifted to previous stimuli and covaried with current choices. Our exploration points to boundary-updating, rather than sensory-adaptation, as the origin of the repulsive bias in binary classification.

neuroscience↗

bioRxiv2023

Why do cell-level test methods for detecting differentially expressed genes fail in single-cell RNA-seq data?

Large-scale multi-subject single-cell data have become very common. For differential gene expression (DGE) analysis of these datasets, a common practice is to choose any of the two: pseudobulk methods or cell-wise methods. However, multi-subject single-cell studies have highly heterogeneous study designs. Some studies have case/control samples to be compared and tested, and some make within-sample perturbations. In this work, we report that to prevent severely inflated errors, we must treat the two categories of study designs differently in the DGE analysis. In studies with case/control labels, pseudobulk methods work the best, and cell-wise methods produce severe inflation of type II errors. Cell-wise methods work best in studies with within-sample perturbations, and pseudobulk methods produce severe inflation of type I errors. We provide mathematical proofs to support this argument. Surprisingly, many existing studies, even published ones, often choose an inappropriate DGE method. The most common temptation is to use cell-wise methods for case/control studies, which will only make p-values falsely look significant. Our analyses and proofs warn that choosing an appropriate DGE method is not an option but a requirement.

genomics↗

bioRxiv2023

Mapping multidimensional content representations to neural and behavioral expressions of episodic memory

Human neuroimaging studies have shown that the contents of episodic memories are represented in distributed patterns of neural activity. However, these studies have mostly been limited to decoding simple, unidimensional properties of stimuli. Semantic encoding models, in contrast, offer a means for characterizing the rich, multidimensional information that comprises episodic memories. Here, we extensively sampled four human fMRI subjects to build semantic encoding models and then applied these models to reconstruct content from natural scene images as they were viewed and recalled from memory. First, we found that multidimensional semantic information was successfully reconstructed from activity patterns across visual and lateral parietal cortices, both when viewing scenes and when recalling them from memory. Second, whereas visual cortical reconstructions were much more accurate when images were viewed versus recalled from memory, lateral parietal reconstructions were comparably accurate across visual perception and memory. Third, by applying natural language processing methods to verbal recall data, we showed that fMRI-based reconstructions reliably matched subjects verbal descriptions of their memories. In fact, reconstructions from ventral temporal cortex more closely matched subjects own verbal recall than other subjects verbal recall of the same images. Fourth, encoding models reliably transferred across subjects: memories were successfully reconstructed using encoding models trained on data from entirely independent subjects. Together, these findings provide evidence for successful reconstructions of multidimensional and idiosyncratic memory representations and highlight the differential sensitivity of visual cortical and lateral parietal regions to information derived from the external visual environment versus internally-generated memories.

neuroscience↗

bioRxiv2023

GolpHCat (TMEM87A): a unique voltage-gated and pH-sensitive cation channel in the Golgi

The Golgi apparatus is a critical intracellular organelle that is responsible for modifying, packaging, and transporting proteins to their destinations. Golgi homeostasis involving the acidic pH, ion concentration, and membrane potential, is critical for proper functions and morphology of the Golgi. Although transporters and anion channels that contribute to Golgi homeostasis have been identified, the molecular identity of cation channels remains unknown. Here we identify TMEM87A as a novel Golgi-resident cation channel that contributes to pH homeostasis and rename it as GolpHCat (Golgi pH-sensitive Cation channel). The genetic ablation of GolpHCat exhibits an impaired resting pH in the Golgi. Heterologously expressed GolpHCat displays voltage- and pH-dependent, non-selective cationic, and inwardly rectifying currents, with potent inhibition by gluconate. Furthermore, reconstitution of purified GolpHCat in liposomes generates functional channel activities with unique voltage-dependent gating and ion permeation. GolpHCat is expressed in various cell types such as neurons and astrocytes in the brain. In the hippocampus, GolpHCat-knockout mice show dilated Golgi morphology and altered glycosylation and protein trafficking, leading to impaired spatial memory with significantly reduced long-term potentiation. We elucidate that GolpHCat, by maintaining Golgi membrane potential, regulates ionic and osmotic homeostasis, protein glycosylation/trafficking, and brain functions. Our results propose a new molecular target for Golgi-related diseases and cognitive impairment.

molecular biology↗

bioRxiv2022

Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups

We present 4,157 whole-genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest whole genomic resource of Koreans. Korea4K provides 45,537,252 variants and encompasses most of the common and rare variants in Koreans. We identified 1,356 new geno-phenotype associations which were not found by the previous Korea1K dataset. Phenomics analyses revealed 24 genetic correlations, 1,131 pleiotropic variants, and 127 causal relationships from Mendelian randomization. Moreover, the Korea4K imputation reference panel showed a superior imputation performance to Korea1K. Collectively, Korea4K provides the most extensive genomic and phenomic data resources for discovering clinically relevant novel genome-phenome associations in Koreans.

genomics↗

bioRxiv2022

Anesthesia alters cortical spike sequences in rodent visual cortex

Recurring spike sequences are thought to underlie cortical computations and may be essential for information processing in the conscious state. How anesthesia at graded levels may influence spontaneous and stimulus-related spike sequences in visual cortex has not been systematically examined. We recorded extracellular single-unit activity in the rat primary visual cortex in vivo during wakefulness and three levels of anesthesia produced by desflurane. The latencies of spike sequences within 0~200ms from the onset of spontaneous UP states and visual flash-evoked responses were compared. During wakefulness, spike latency patterns linked to the local field potential theta cycle were similar to stimulus-evoked patterns. Under anesthesia, spontaneous UP state sequences differed from flash-evoked sequences due to the recruitment of low-firing excitatory neurons to the UP state. Flash-evoked spike sequences showed higher reliability and longer latency when stimuli were applied during DOWN states compared to UP states. At deeper levels, anesthesia altered both UP state and flash-evoked spike sequences by selectively suppressing inhibitory neuron firing. The results reveal anesthesia-induced complex changes in cortical firing sequences that may influence visual information processing.

neuroscience↗

bioRxiv2022

Atmospheric hydrogen oxidation extends to the domain archaea

Diverse aerobic bacteria use atmospheric hydrogen (H2) and carbon monoxide (CO) as energy sources to support growth and survival. Though recently discovered, trace gas oxidation is now recognised as a globally significant process that serves as the main sink in the biogeochemical H2 cycle and sustains microbial biodiversity in oligotrophic ecosystems. While trace gas oxidation has been reported in nine phyla of bacteria, it was not known whether archaea also use atmospheric H2. Here we show that a thermoacidophilic archaeon, Acidianus brierleyi (Thermoproteota), constitutively consumes H2 and CO to sub-atmospheric levels. Oxidation occurred during both growth and survival across a wide range of temperatures (10 to 70{degrees}C). Genomic analysis demonstrated that A. brierleyi encodes a canonical carbon monoxide dehydrogenase and, unexpectedly, four distinct [NiFe]-hydrogenases from subgroups not known to mediate aerobic H2 uptake. Quantitative proteomic analyses showed that A. brierleyi differentially produced these enzymes in response to electron donor and acceptor availability. A previously unidentified group 1 [NiFe]-hydrogenase, with a unique genetic arrangement, is constitutively expressed and upregulated during stationary phase and aerobic hydrogenotrophic growth. Another archaeon, Metallosphaera sedula, was also found to oxidize atmospheric H2. These results suggest that trace gas oxidation is a common trait of aerobic archaea, which likely plays a role in their survival and niche expansion, including during dispersal through temperate environments. These findings also demonstrate that atmospheric H2 consumption is a cross-domain phenomenon, suggesting an ancient origin of this trait, and identify previously unknown microbial and enzymatic sinks of atmospheric H2 and CO.

microbiology↗

bioRxiv2022

A Photonic Resonator Interferometric Scattering Microscope for Label-free Detection of Nanometer-Scale Objects with Digital Precision in Point-of-Use Environments

Label-free detection and digital counting of nanometer-scaled objects such as nanoparticles, viruses, extracellular vesicles, and protein molecules enable a wide range of applications in cancer diagnostics, pathogen detection, and life science research. The contrast of interferometric scattering microscopy is amplified through a photonic crystal surface, upon which scattered light from an object combines with illumination from a monochromatic plane wave source. The use of a photonic crystal substrate for interference scattering microscopy results in reduced requirements for high-intensity lasers or oil-immersion objectives, thus opening a pathway toward instruments that are more suitable for environments outside the optics laboratory. Here, we report the design, implementation, and characterization of a compact Photonic Resonator Interferometric Scattering Microscope (PRISM) designed for point-of-use environments and applications. The instrument incorporates two innovative elements that facilitate operation on a desktop in ordinary laboratory environments by users that do not have optics expertise. First, because scattering microscopes are extremely sensitive to vibration, we incorporated an inexpensive but effective solution of suspending the instruments main components from a rigid metal framework using elastic bands, resulting in an average of 28.7 dBV reduction in vibration amplitude compared to an office desk. Second, an automated focusing module based on the principle of total internal reflection maintains the stability of image contrast over time and spatial position, facilitating automated data collection. In this work, we characterize the systems performance by measuring the contrast from gold nanoparticles with diameters in the 10-40 nm range and by observing various biological analytes, including HIV virus, SARS-CoV-2 virus, exosomes, and ferritin protein.

bioengineering↗

bioRxiv2022

ROTACs leverage signaling-incompetent R-spondin for targeted protein degradation

Proteolysis-targeting chimeras (PROTACs) are an emerging technology for therapeutic intervention but options to target cell surface proteins and receptors remain limited. Here we introduce ROTACs, bispecific WNT- and BMP signaling-disabled R-spondin (RSPO) chimeras, which leverage the specificity of these stem cell growth factors for ZNRF3/RNF43 E3 transmembrane ligases, to target degradation of transmembrane proteins. As proof of concept, we targeted the immune checkpoint protein programmed death ligand 1 (PD-L1), a prominent cancer therapeutic target, with a bispecific RSPO2 chimera, R2PD1. The R2PD1 chimeric protein bound PD-L1 and at picomolar concentration induced its lysosomal degradation. In three melanoma cell lines, R2PD1 induced between 50-90% PD-L1 protein degradation. PD-L1 degradation was strictly dependent on ZNRF3/RNF43. We conclude that signaling-disabled ROTACs represent a novel strategy to target cell surface proteins for degradation.

bioengineering↗

bioRxiv2022

CD244 regulates both innate and adaptive immune axes in melanoma by inhibiting autophagy-mediated M1 macrophage maturation

Accumulating data have highlighted the role of monocytes/macrophages in immune escape by generating immunologically "cold" tumors that do not respond to immunotherapy. CD244 (SLAMF4, 2B4), a member of the signaling lymphocyte activation molecule family, is expressed on myeloid cells, but its precise role has not been elucidated. Using monocyte lineage-specific CD244-deficient (LysM-cre+/-CD244fl/fl;cKO) mice challenged with B16F10 melanoma, we report for the first time that CD244 negatively regulates tumor immunity by inhibiting the differentiation and functional maturation of CD11b+Ly6ChiF4/80lo monocytes into CD11b+Ly6CloF4/80hi macrophages within the tumor microenvironment. CD244-deficient macrophages more effectively activated antigen-specific T cell responses compared to WT macrophages, thus delaying tumor growth in the B16F10 melanoma model. Moreover, combinatorial intervention of anti-PD-L1 antibodies with CD244-KO BMDM markedly improved tumor rejection compared to the anti-PD-L1 antibody alone or in combination with WT BMDM. Consistent with the murine data, transcriptome analysis of human melanoma tissue single-cell RNA-sequencing dataset (SCP398 from single-cell portal), revealed 221 differentially expressed genes of CD244- monocytes/macrophages were associated with phagocytosis, antigen presentation, and autophagy. Additionally, cell type deconvolution analysis within melanoma patients bulk RNA-seq datasets from TCGA database, revealed presence of CD244- monocytes/macrophages significantly increased patient survival in primary and metastatic tumors. Hence, we proposed that CD244 serve as a critical immune checkpoint receptor on macrophages, and CD244-deficient macrophages may represent a novel therapeutic modality to convert immunologically "cold" tumors to "hot" tumors, which can function synergistically with checkpoint blockade therapies.

immunology↗

bioRxiv2022

STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data

Unraveling the spatial configuration of the tumor microenvironment (TME) is key to understanding tumor-immune interactions to translate them into immuno-oncology. With the advent of spatially resolved transcriptomics (SRT), the TME could be dissected for whole cell types across numerous RNAs. We suggest a novel approach, STopover, which performs topological analysis to compute the colocalization patterns between cell types and map the location of cell{square}cell interactions. While gradually lowering the threshold for the feature, the connected components (CCs) were extracted based on the spatial distance between the unit tissue region and the persistence of the CCs. Local and global Jaccard indices were calculated between the CCs of a feature pair to measure the extent of spatial overlap. The STopover was applied to various lung cancer data obtained from SRT platforms, both barcode and image-based SRT, and could explain the infiltration patterns of immune and stromal cells in the TME. Moreover, the method predicted the top cell{square}cell communication based on the ligand{square}receptor database and highlighted the main region of the interaction. STopover is a tool to decipher spatial interaction in the tissue and shed light on the pathophysiology underlying the microenvironment.

genomics↗

bioRxiv2022

Cell-subtype specific effects of genetic variation in the aging and Alzheimer cortex

The relationship between genetic variation and gene expression in individual brain cell types and subtypes has remained elusive. Here, we generated single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex of 424 individuals of advanced age; analyzing 1.5 million nuclear transcriptomes, we assessed the effect of genetic variants on RNA expression in cis (cis-eQTL) for 7 cell types and 81 cell subtypes. This effort identified 10,004 eGenes at the cell type level and 8,138 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer pathology, accounting for the effect of APOE{varepsilon}4, providing mechanistic insights into both pathologies. While eQTLs are readily detected, only a TMEM106B variant robustly affects the proportion of cell subtypes. Integration of these results with GWAS highlighted the targeted cell type and likely causal gene within susceptibility loci for Alzheimers, Parkinsons, schizophrenia, and educational attainment.

genomics↗

bioRxiv2022

Adding meaning to memories: How parietal cortex combines semantic content with episodic experience

Neuroimaging studies of human memory have consistently found that univariate responses in parietal cortex track episodic experience with stimuli (whether stimuli are old or new). More recently, pattern-based fMRI studies have shown that parietal cortex also carries information about the semantic content of remembered experiences. However, it is not well understood how memory-based and content-based signals are integrated within parietal cortex. Here, we used voxel-wise encoding models and a recognition memory task to predict the fMRI activity patterns evoked by complex natural scene images based on (a) the episodic history and (b) the semantic content of each image. Models were generated and compared across distinct subregions of parietal cortex and for occipitotemporal cortex. We show that parietal and occipitotemporal regions each encode memory and content information, but they differ in how they combine this information. Among parietal subregions, angular gyrus was characterized by robust and overlapping effects of memory and content. Moreover, subject-specific semantic tuning functions revealed that successful recognition shifted the amplitude of tuning functions in angular gyrus but did not change the selectivity of tuning. In other words, effects of memory and content were additive in angular gyrus. This pattern of data contrasted with occipitotemporal cortex where memory and content effects were interactive: memory effects were preferentially expressed by voxels tuned to the content of a remembered image. Collectively, these findings provide unique insight into how parietal cortex combines information about episodic memory and semantic content.

neuroscience↗

bioRxiv2022

COOBoostR: an extreme gradient boosting-based tool for robust tissue or cell-of-origin prediction of tumors

We here present COOBoostR (https://github.com/SWJ9385/COOBoostR), a computational method designed for the putative prediction of tissue-or cell-of-origin of various cancer types. COOBoostR leverages regional somatic mutation density information and chromatin mark features to be applied to an extreme gradient boosting-based machine-learning algorithm. COOBoostR ranks chromatin marks from various tissue and cell types which best explain the somatic mutation density landscape of any sample of interest. Through integrating either ChIP-seq based chromatin data or bulk/single cell chromatin accessibility data along with regional somatic mutation density data derived from normal cells/tissue, precancerous lesions, and cancer types, we show that COOBoostR outperforms existing random forest-based methods in prediction speed with comparable or better tissue or cell-of-origin prediction performance. In addition, our results suggest a dynamic somatic mutation accumulation at the normal tissue or cell stage which could be intertwined with the changes in open chromatin marks and enhancer sites. These results further represent chromatin marks shaping the somatic mutation landscape at the early stage of mutation accumulation, possibly even before the initiation of precancerous lesions or neoplasia.

bioinformatics↗