Biology subjects

Lee, S.

Publications and source records attributed to Lee, S..

At least 289 records · Page 16Linked to original sources

bioRxiv2024

Context-Aware Hierarchical Fusion for Drug Relational Learning

Drug relational learning, focused on understanding drug-pair relationships within specific contexts of interest, has emerged as a critical area of investigation for its pivotal role in enhancing the efficacy of disease treatment. The nature of drug relationships exhibits significant variations across diverse contexts, such as different types of cancer cell lines. Existing methods often encounter limitations by either neglecting the incorporation of context information or lacking explicit modeling of the intricate connections within drug-drug-context triplets, due to the difficulty in handling heterogeneous relationships between drugs and context. In this study, we present a novel context-aware hierarchical cross-fusion architecture tailored for drug relational learning. By formulating the problem as the label prediction of drug-drug-context triplets, we explicitly calculate all the relations among the triplets. Considering drugs often function as causes and contexts serve as results, our model enhances the learning of intricate drug pair relations hierarchically fusing the information from drug to context through the learned relations. Empirical results across multiple prediction tasks, including synergy, polypharmacy side effects, and drug-drug interactions, highlight the models capability to capture essential information relevant to drug relational learning. Notably, our model demonstrates robust predictive performance even in scenarios of heightened contextual complexity, demonstrating its adaptability in learning context-aware drug relations. CCS CONCEPTS* Applied computing [->] Bioinformatics; Molecular structural biology; * Computing methodologies [->] Artificial intelligence. ACM Reference FormatYijingxiu Lu, Yinhua Piao, Sangseon Lee, and Sun Kim. 2024. Context-Aware Hierarchical Fusion for Drug Relational Learning. In Proceedings of Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (BIOKDD 24). ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

bioinformatics↗

bioRxiv2024

Mixture-of-Experts Approach for Enhanced Drug-Target Interaction Prediction and Confidence Assessment

In recent years, numerous deep learning models have been developed for drug-target interaction (DTI) prediction. These DTI models specialize in handling data with distinct distributions and features, often yielding inconsistent predictions when applied to unseen data points. This inconsistency poses a challenge for researchers aiming to utilize these models in downstream drug development tasks. Particularly in screening potential active compounds, providing a ranked list of candidates that likely interact with the target protein can guide scientists in prioritizing their experimental efforts. However, achieving this is difficult as each current DTI model can provide a different list based on its learned feature space. To address these issues, we propose EnsDTI, a Mixture-of-Experts architecture designed to enhance the performance of existing DTI models for more reliable drug-target interaction predictions. We integrate an inductive conformal predictor to provide confidence scores for each prediction, enabling EnsDTI to offer a reliable list of candidates for a specific target. Empirical evaluations on four benchmark datasets demonstrate that EnsDTI not only improves DTI prediction performance with an average accuracy improvement of 2.7% compared to the best performing baseline, but also offers a reliable ranked list of candidate drugs with the highest confidence, showcasing its potential for ranking potential active compounds in future applications. CCS CONCEPTS* Applied computing [->] Bioinformatics; * Computing methodologies [->] Artificial intelligence.

bioinformatics↗

bioRxiv2024

Extensively acquired antimicrobial resistant bacteria restructure the individual microbial community in post-antibiotic conditions

In recent years, the overuse of antibiotics has led to the emergence of antimicrobial resistant (AMR) bacteria. To evaluate the spread of AMR bacteria, the reservoir of AMR genes (resistome) has traditionally been identified from environmental samples, hospital environments, and human populations; however, the functional role of AMR bacteria in the human gut microbiome and their persistency within individuals has not been fully investigated. Here, we performed a strain-resolved in-depth analysis of the resistome changes by reconstructing a large number of metagenome-assembled genomes (MAGs) of antibiotics- treated individuals gut microbiome. Interestingly, we identified two bacterial populations with different resistome profiles, extensively acquired antimicrobial resistant bacteria (EARB) and sporadically acquired antimicrobial resistant bacteria (SARB), and found that EARB showed broader drug resistance and a significant functional role in shaping individual microbiome composition after antibiotic treatment. Furthermore, longitudinal strain analysis revealed that EARB bacteria were inherently carried by individuals and can reemerge through strain switching in the human gut microbiome. Our data on the presence of AMR bacteria in the human gut microbiome provides a new avenue for controlling the spread of AMR bacteria in the human community.

genomics↗

bioRxiv2024

Development of the novel amylin receptor activators with nanomolar potency by peptide mutagenesis

Amylin peptide hormone activates amylin receptors in brains and controls blood glucose and appetite. An amylin receptor activator pramlintide was developed for diabetes treatment. Currently, the amylin receptor activator with once-weekly injection has been tested for body weight reduction to treat obesity. Human amylin peptide was reported to form aggregates, while rat amylin has been shown soluble in aqueous solution. Here, multiple peptide activators for human amylin receptors were developed by introducing comprehensive mutagenesis to rat amylin peptide. The rat amylin peptide C-terminal fragment is known for interacting with human amylin receptor extracellular domain. The rat amylin peptide C-terminal fragment with eleven amino acids was used to screen for affinity-enhancing mutations. Up to twelve mutational combinations were found to significantly increase peptide affinity for amylin receptor extracellular domains by over 100-fold. Using these affinity-enhancing mutations, three representative rat amylin analogs with thirty-seven amino acids were made to test the potency increase for amylin receptor activation. All three mutated rat amylin analogs showed significant potency increases by 5- to 10-fold compared to endogenous rat amylin. These mutated peptide activators also showed higher potency for human amylin receptor activation than a clinical drug pramlintide. These amylin receptor activators developed in this study can be useful for the drug development targeting diabetes/obesity treatment.

biochemistry↗

bioRxiv2024

ChromBERT: Uncovering Chromatin State Motifs in the Human Genome Using a BERT-based Approach

Chromatin states, which are defined by specific combinations of histone post-translational modifications, are fundamental to gene regulation and cellular identity. Despite their importance, comprehensive patterns within chromatin state sequences, which could provide insights into key biological functions, remain largely unexplored. In this study, we introduce ChromBERT, a BERT-based model specifically designed to detect distinct chromatin state patterns as "motifs." We pre-trained ChromBERT on 15-state chromatin annotations from 127 human cell and tissue types from the ROADMAP consortium. This pre-trained model can be fine-tuned for various downstream tasks, and obtained high-attention chromatin state patterns are extracted as motifs. To account for the variable-length nature of chromatin state motifs, ChromBERT uses Dynamic Time Warping to cluster similar motifs and identify meaningful representative patterns. In this study, we evaluated the performance of the model on several tasks, including binary and quantitative gene expression prediction, cell type classification, and three-dimensional genome feature classification. Our analyses yielded biologically grounded results and revealed the associated chromatin state motifs. This workflow facilitates the discovery of specific chromatin state patterns across different biological contexts and offers a new framework for exploring the dynamics of epigenomic states.

bioinformatics↗

bioRxiv2024

Vortex light field microscopy: 3D spectral single-molecule imaging with a twist

We introduce vortex light field microscopy (VLFM), a novel method for snapshot 3D spectral single-molecule localization microscopy. Inspired by the azimuthal phase profile of optical vortices, we place an azimuthally oriented prism array immediately after the microlens array in a Fourier light field microscope (FLFM). This innovative arrangement causes the axial position and spectral peak for a point emitter to be encoded in the radial and azimuthal displacement of point-spread-function (PSF) respectively. This enables simultaneous detection of 3D position and emission peak of individual fluorophores with 25 nm spatial precision and 3 nm spectral precision over a 4 m depth of field (DOF). We illustrate the spectral scalability of our method by performing four-color 3D single particle tracking of freely diffusing fluorescent beads, and two-color 3D dSTORM imaging of microtubules and mitochondria in fixed COS-7 cells, without the need for spectrally distinct fluorophores.

biophysics↗

bioRxiv2024

Establishing a Male-Positive Genetic Sexing Strain in the Asian Malaria Vector Anopheles stephensi

Genetic biocontrol interventions targeting mosquito-borne diseases require the release of male mosquitoes exclusively, as only females consume blood and transmit human pathogens. This reduces the risk of spreading pathogens while enabling effective population control. Robust sex sorting methods to enable early larval sorting in mosquitoes need to be developed to allow for scalable sex sorting for genetic biocontrol interventions. This study applies the SEPARATOR (Sexing Element Produced by Alternative RNA-splicing of A Transgenic Observable Reporter) system, previously developed for Aedes aegypti, to the Asian malaria vector Anopheles stephensi. We hypothesized that the intron from the doublesex gene in Anopheles gambiae would function in An. stephensi due to evolutionary conservation. Our results confirm that the splicing module from An. gambiae operates effectively in An. stephensi, demonstrating evolutionary conservation in sex-specific splicing events between these species. This system enables reliable positive male selection from first instar larval to pupal stages. RT-PCR analysis demonstrates that male-specific EGFP expression is dependent on doublesex sex-specific splicing events. The SEPARATOR systems independence from sex-chromosome linkage confers resistance to meiotic recombination and chromosomal rearrangements. This approach may facilitate the mass release of males, and the cross-species portability of SEPARATOR establishes it as a valuable tool for genetic biocontrol interventions across various pest species.

bioengineering↗

bioRxiv2024

Social Status as a Latent Variable in the Amygdala of Observers of Social Interactions

Successful integration into a hierarchical social group requires knowledge of the status of each individual and of the rules that govern social interactions within the group. In species that lack morphological indicators of status, social status can be inferred by observing the signals exchanged between individuals. We simulated social interactions between macaques by juxtaposing videos of aggressive and appeasing displays where two individuals appeared in each others line of sight and their displays were timed to suggest the reciprocation of dominant and subordinate signals. Viewers of these videos successfully inferred the social status of the interacting characters. Dominant individuals attracted more social attention from viewers even when they were not engaged in social displays. Neurons in the viewers amygdala signaled the status of both the attended (fixated) and the unattended individuals suggesting that in third party observers of social interactions, the amygdala signals jointly the status of interacting parties. HighlightsO_LIMonkeys infer the social status of conspecifics from videos of simulated dyadic interactions C_LIO_LIDuring fixations neural populations signal the social status of the attended individuals C_LIO_LINeurons in the amygdala jointly encode the status of interacting individuals C_LI In briefThird party-viewers of pairwise dominant-subordinate interactions infer social status from the observed behaviors. Neurons in the amygdala are tuned to the inferred dominant/subordinate status of both individuals.

animal behavior and cognition↗

bioRxiv2024

The neonatal Fc receptor and DPP4 are human astrovirus receptors

Human astroviruses (HAstV) are major global causes of gastroenteritis, but little is known about host factors required for their cellular entry. Here, we utilized complementary CRISPR-Cas9-based knockout and activation screening approaches and identified neonatal Fc receptor (FcRn) and dipeptidyl-peptidase IV (DPP4) as entry factors for HAstV infection of human intestinal epithelial cells. Disruption of FcRn or DPP4 reduced HAstV infection in permissive cells and, reciprocally, overexpression of these factors in non-permissive cells was sufficient to promote infection. We observed direct binding between FcRn and HAstV virions as well as purified spike protein. Finally, inhibitors for DPP4 and FcRn currently in clinical use prevent HAstV infection in cell lines and primary human enteroids. Thus, our results reveal mechanisms of HAstV entry as well as druggable targets. One-Sentence SummaryTargeting FcRn or DPP4 using available therapies effectively prevents human astrovirus infection in human enteroid cultures.

microbiology↗

bioRxiv2024

Dynamic stress- and inflammatory-based regulation of psychiatric risk loci in human neurons

The immune environment influences neurodevelopment and subsequent clinical trajectories for psychiatric outcomes in childhood and adolescence. Yet it remains unclear if the impact of maternal and fetal immune activation varies with distinct polygenic risk profiles. Therefore, here we catalogue genotype and environment (GxE) interactions, contrasting allele-specific regulatory activity between inflammatory cues. We report a cue-specific neuronal massively parallel reporter assay (MPRA) of 152 loci from genome-wide association study (GWAS) linked to ten brain traits/disorders, empirically dissecting the impact of interleukin-6 (IL-6) and interferon-alpha (IFN) on transcriptional activity. In human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons, 1,156 active candidate regulatory risk sequences (MPRA-active CRSs) are resolved, including 267 with variant-specific effects (MPRA-emVars) and 61 with variant-by-cytokine interaction effects (interaction MPRA-emVars). Broadly, neuronal immune-mediated regulatory activity is associated with differences in transcription factor binding and chromatin accessibility, the gene targets of which show pleiotropic enrichments for brain, metabolic, and immune disorders. Dynamic genetic regulation mediates neuroimmune effects, informing our understanding of the genomics of psychiatric and neurological traits, mechanisms governing pleiotropy across disorders, and how immune mechanisms mediate genetic risk.

genetics↗

bioRxiv2024

Multivariate analysis of multimodal brain structure predicts individual differences in risk and intertemporal preference

Large changes to brain structure (e.g., from damage or disease) can explain alterations in behavior. It is therefore plausible that smaller structural differences in healthy samples can be used to better understand and predict individual differences in behavior. Despite the brains multivariate and distributed structure-to-function mapping, most studies have used univariate analyses of individual structural brain measures. Here we used a multivariate approach in a multimodal data set composed of volumetric, surface-based, diffusion-based, and functional resting-state MRI measures to predict reliable individual differences in risk and intertemporal preferences. We show that combining twelve brain structure measures led to better predictions across tasks than using any individual measure, and by examining model coefficients, we visualize the relative contribution of different brain measures from different brain regions. Using a multivariate approach to brain structure-to-function mapping that combines across many brain structure properties, along with reliably measured behavior phenotypes, may increase out-of-sample prediction accuracies and insight into neural underpinnings. Furthermore, this methodological approach may be useful to improve predictions and neural insight across basic, translational, and clinical research fields.

neuroscience↗

bioRxiv2024

Insights into non-crossover recombination from long-read sperm sequencing

Meiotic recombination is a fundamental process that generates genetic diversity by creating new combinations of existing alleles. Although human crossovers have been studied at the pedigree, population and single-cell level, the more frequent non-crossover events that lead to gene conversion are harder to study, particularly at the individual level. Here we show that single high-fidelity long sequencing reads from sperm can capture both crossovers and non-crossovers, allowing effectively arbitrary sample sizes for analysis from one male. Using fifteen sperm samples from thirteen donors we demonstrate variation between and within donors for the rates of different types of recombination. Intriguingly, we observe a tendency for non-crossover gene conversions to occur upstream of nearby PRDM9 binding sites, whereas crossover locations have a slight downstream bias. We further provide evidence for two distinct non-crossover processes. One gives rise to the vast majority of non-crossovers with mean conversion tract length under 50bp, which we suggest is an outcome of standard PRDM9-induced meiotic recombination. In contrast [~]2% of non-crossovers have much longer mean tract length, and potentially originate from the same process as complex events with more than two haplotype switches, which is not associated with PRDM9 binding sites and is also seen in somatic cells.

genetics↗

bioRxiv2024

Acidotolerant soil nitrite oxidiser 'Candidatus Nitrobacter laanbroekii' NHB1 alleviates constraints on growth of acidophilic soil ammonia oxidisers

Nitrobacter strain NHB1 is a nitrite-oxidising bacterium previously co-enriched with the neutrophilic ammonia-oxidising bacterium Nitrosospira AHB1, a consortium that nitrifies under acidic conditions. Here we characterise the growth of isolated Nitrobacter strain NHB1 as a function of pH and nitrite (NO2-) concentration, and its influence on the activity of acidophilic soil ammonia-oxidising archaea (AOA) in co-culture. NHB1 is acidotolerant and grows optimally at pH 6.0 (range 5.0 - 7.5) at initial NO2- concentrations of 500 {micro}M. However, the optimum decreases to pH 5.0 at lower initial NO2- concentrations closer to those found in soil, with detectable growth down to pH 3.5. NHB1 has a comparatively high affinity for NO2- with an apparent-half-saturation constant (54 {micro}M) one order of magnitude lower than its closest relative, the neutrophilic strain Nitrobacter hamburgensis X14. In co-culture, NHB1 enhances the growth of acidophilic AOA. Specifically, Nitrosotalea devaniterrae Nd1 and Nitrosotalea sinensis Nd2 are sensitive to NO2--derived compounds and only oxidise [~]200-300 {micro}M ammonia (NH3) in batch cultures. However, in co-culture with NHB1, pH ranges were lowered by [~]0.5 pH units and both strains could oxidise up to 2.7-2.9 mM NH3, only limited by buffering capacity. NHB1 possesses a cyanase facilitating reciprocal cross-feeding via generating cyanate-derived NH3 and utilising AOA-derived NO2-. Removal of NO2- is likely crucial for nitrifier growth in acidic soils and this study highlights the importance of considering substrate and metabolic product concentrations when characterising physiology. Genome analysis reveals that NHB1 is distinct from validated species and the name Nitrobacter laanbroekii is proposed.

microbiology↗

bioRxiv2024

Cell crowding induces TRPV4 inhibition and its relocation to plasma membranes, implicating pro-invasive cell volume reduction mechanotransduction pathway

Cell crowding is a common microenvironmental factor influencing various disease processes, but its role in promoting cell invasiveness remains unclear. This study investigates the biomechanical changes induced by cell crowding, focusing on pro-invasive cell volume reduction in ductal carcinoma in situ (DCIS). Crowding specifically enhanced invasiveness in high-grade DCIS cells through significant volume reduction compared to hyperplasia-mimicking or normal cells. Mass spectrometry revealed that crowding selectively relocated ion channels, including TRPV4, to the plasma membrane in high-grade DCIS cells. TRPV4 inhibition triggered by crowding decreased intracellular calcium levels, reduced cell volume, and increased invasion and motility. During this process, TRPV4 membrane relocation primed the channel for later activation, compensating for calcium loss. Analyses of patient-derived breast cancer tissues confirmed that plasma membrane- associated TRPV4 is specific to high-grade DCIS and indicates the presence of a pro-invasive cell volume reduction mechanotransduction pathway. Hyperosmotic conditions and pharmacologic TRPV4 inhibition mimicked crowding-induced effects, while TRPV4 activation reversed them. Silencing TRPV4 diminished mechanotransduction in high-grade DCIS cells, reducing calcium depletion, volume reduction, and motility. This study uncovers a novel pro-invasive mechanotransduction pathway driven by cell crowding and identifies TRPV4 as a potential biomarker for predicting invasion risk in DCIS patients.

cell biology↗

bioRxiv2024

Predicting the physiological effects of multiple drugs using electronic health record

Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effective drug treatments. However, they mostly focus on interactions of only two drugs, and do not consider the patient information. To address this challenge, we use publicly available electronic health record (EHR), MIMIC-IV, to develop machine learning models that predict the physiological effects of two or more drugs. This study involves extensive preprocessing of laboratory measurement data, prescription data and patient data. The resulting machine learning models predict potential abnormalities across 20 selected measurement items (e.g., concentrations of metabolites and blood cells) in the form of a sentence. The models showed an average AUROC of 0.75, and age, specific active pharmaceutical ingredients, and gender appeared to be the most influential features. The model development process showcased in this study can be extended to other measurement items for a target EHR.

systems biology↗

bioRxiv2024

An intricate balancing act: Upstream and downstream frameshift co-regulatory elements

Targeting ribosomal frameshifting has emerged as a potential therapeutic intervention strategy against Covid-19. During ribosomal translation, a fraction of elongating ribosomes slips by one base in the 5' direction and enters a new reading frame for viral protein synthesis. Any interference with this process profoundly affects viral replication and propagation. For Covid-19, two RNA sites associated with ribosomal frameshifting for SARS-CoV-2 are positioned on the 5' and 3' of the frameshifting residues. Although much attention has been on the 3' frameshift element (FSE), the 5' stem-loop (attenuator hairpin, AH) can play a role. The formation of AH has been suggested to occur as refolding of the 3' RNA structure is triggered by ribosomal unwinding. However, the attenuation activity and the relationship between the two regions are unknown. To gain more insight into these two related viral RNAs and to further enrich our understanding of ribosomal frameshifting for SARS-CoV-2, we explore the RNA folding of both 5' and 3' regions associated with frameshifting. Using our graph-theory-based modeling tools to represent RNA secondary structures, "RAG" (RNA-As-Graphs), and conformational landscapes to analyze length-dependent conformational distributions, we show that AH coexists with the 3-stem pseudoknot of the 3' FSE (graph 3_6 in our dual graph notation) and alternative pseudoknot (graph 3_3) but less likely with other 3' FSE alternative folds (such as 3-way junction 3_5). This is because an alternative length-dependent Stem 1 (AS1) can disrupt the FSE pseudoknots and trigger other folds. In addition, we design four mutants for long lengths that stabilize or disrupt AH, AS1 or FSE pseudoknot to illustrate the deduced AH/AS1 roles and favor the 3_5, 3_6 or stem-loop. These mutants further show how a strengthened pseudoknot can result from a weakened AS1, while a dominant stem-loop occurs with a strengthened AS1. These structural and mutational insights into both ends of the FSE in SARS-CoV-2 advance our understanding of the SARS-CoV-2 frameshifting mechanism by suggesting a sequence of length-dependent folds, which in turn define potential therapeutic intervention techniques involving both elements. Our work also highlights the complexity of viral landscapes with length-dependent folds, and challenges in analyzing these multiple conformations.

biochemistry↗

bioRxiv2024

Improving positively tuned voltage indicators for brightness and kinetics

Existing positively tuned ASAP-family voltage indicators such as ASAP4e exhibit superior photostability compared to the negatively tuned ASAP3 and ASAP5, but signal-to-noise ratios for spike detection remained similar to ASAP3. To improve spike detection by positively tuned ASAP indicators, we performed multiple rounds of structure-guided saturation mutagenesis of an ASAP4e predecessor while screening directly for faster responses. One variant, ASAP6c, demonstrated [~]100% increases in fluorescence in response to single action potentials in both one-photon and two-photon imaging in vivo, achieving a 3-fold improvement in signal-to-noise ratios over ASAP4e.

neuroscience↗

bioRxiv2024

Optimized genome-wide CRISPR screening enables rapid engineering of growth-based phenotypes in Yarrowia lipolytica

CRISPR-Cas9 functional genomic screens uncover gene targets linked to various phenotypes for metabolic engineering with remarkable efficiency. However, these genome-wide screens face a number of design challenges, including variable guide RNA activity, ensuring sufficient genome coverage, and maintaining high transformation efficiencies to ensure full library representation. These challenges are prevalent in non-conventional yeast, many of which exhibit traits that are well suited to metabolic engineering and bioprocessing. To address these hurdles in the oleaginous yeast Yarrowia lipolytica, we designed a compact, high-activity genome-wide sgRNA library. The library was designed using DeepGuide, a sgRNA activity prediction algorithm, and a large dataset of [~]50,000 sgRNAs with known activity. Three guides per gene enables redundant targeting of 98.8% of genes in the genome in a library of 23,900 sgRNAs. We deployed the optimized library to uncover genes essential to the tolerance of acetate, a promising alternative carbon source, and various hydrocarbons present in many waste streams. Our screens yielded several gene knockouts that improve acetate tolerance on their own and as double knockouts in media containing acetate as the sole carbon source. Analysis of the hydrocarbon screens revealed genes related to fatty acid and alkane metabolism in Y. lipolytica. The optimized CRISPR gRNA library and its successful use in Y. lipolytica led to the discovery of alternative carbon source-related genes and provides a workflow for creating high-activity, compact genome-wide libraries for strain engineering. HighlightsO_LIDesigned a compact, high activity CRISPR sgRNA knockout library for Yarrowia lipolytica. C_LIO_LIDeveloped an efficient pipeline for discovering genes involved in alternative carbon-source utilization. C_LIO_LIIdentified single and double gene knockouts that improve growth on acetate. C_LIO_LIIdentified genes with improved fitness and essentiality for hydrocarbon growth. C_LI

synthetic biology↗