Biology subjects

Juan, H.-F.

Publications and source records attributed to Juan, H.-F..

10 recordsLinked to original sources

bioRxiv2026

Identification of gene kinetics patterns improves RNA velocity and cell fate prediction using JuloVelo

Single-cell RNA velocity has transformed our capacity to predict future cell states from sequencing snapshots, but existing methods rely on constant or binary kinetic assumptions that fail to capture the heterogeneous splicing dynamics of cellular differentiation. Here we present JuloVelo, a deep learning framework that demonstrates that identifying gene kinetics patterns to initialize neural network training dramatically improves RNA velocity model performance. By pre-determining whether each gene follows a Circle, Complete induction, or Complete repression pattern across cellular differentiation, JuloVelo initializes training with pattern-matched, pre-trained network weights, effectively guiding the cell-specific kinetics optimization away from biologically implausible parameter regimes. Additionally, JuloVelo demonstrates that multiome datasets can also be processed and modeled by utilizing promoter chromatin accessibility to guide kinetics pre-determination, and integrates an in silico perturbation framework to simulate transcription factor modulation on cell fate. Validated across eight datasets spanning erythrogenesis, neurogenesis, and endocrinogenesis, JuloVelo consistently outperformed constant-rate and cell-specific models, resolving the entire five-lineage differentiation program in hippocampal neurogenesis. Multiome processing improved both intrinsic and extrinsic velocity stability, and JuloVelo reduced computational execution time by approximately 70% compared to cellDancer. JuloVelo is available as an open-source package at https://github.com/jhlabtw/JuloVelo.jl.

bioinformatics↗

bioRxiv2025

scDock: Streamlining drug discovery targeting cell-cell communication via scRNA-seq analy-sis and molecular docking

SummaryIdentifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an integrated and user-friendly pipeline that seamlessly connects scRNA-seq data processing, cell-cell communication inference, and molecular docking-based drug discovery. Through a single configuration file, users can execute the complete workflow, from raw scRNA-seq data to ranked drug candidates, without programming skills. scDock automates the identification of disease-relevant ligand-receptor interactions from scRNA-seq data and perfoms structure-based virtual screening against these communication targets using Protein Data Bank (PDB) or AlphaFold-predicted protein structures. The pipeline generates comprehensive outputs at each stage, enabling users to explore intercellular signaling alterations and discover therapeutic compounds targeting specific cell-cell communications. scDock addresses a critical gap by providing an accessible end-to-end solution for communication-targeted drug discovery from single-cell data. Availability and ImplementationscDock is freely available at https://github.com/Andrewneteye4343/scDock. It is implemented in R, Python, shell scripts, and supports Linux systems, including Ubuntu and Debian.

bioinformatics↗

bioRxiv2025

Airway microbiome-host transcriptome networks link microbial dysbiosis to survival outcomes and therapeutic opportunities in severe COVID-19

The lower respiratory tract microbiome may critically influence outcomes in severe COVID-19, yet remains understudied. We analyzed bronchoalveolar lavage fluid from 86 severe patients requiring mechanical ventilation, stratified into two groups. We identified 213 bacteria with differential abundance, 11 bacteria associated with differentially expressed genes, and 39 of them were associated with patient survival. The more serious group exhibited reduced microbial network complexity, suggesting ecosystem disruption. To explore therapeutic potential, we employed our previously published model and predicted that compounds like palbociclib, XMD-1499, RS-17053, and RS-504393 may shift host gene expression favoring beneficial microbial signatures. These results highlight an important link between microbial dysbiosis and COVID-19 severity, offering promising directions for microbiome-informed therapeutic strategies. Our findings underscore the importance of the lung microbiome in modulating host responses and clinical outcomes, and suggest that modulating microbial-host interactions may serve as a novel adjunctive approach in the treatment of severe COVID-19. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/661228v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@2fc879org.highwire.dtl.DTLVardef@1128d5dorg.highwire.dtl.DTLVardef@fef0eaorg.highwire.dtl.DTLVardef@17aa499_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Spatial Transcriptomic Profiling Of Advanced Ovarian Clear Cell Carcinoma Reveals Intra-Tumor Heterogeneity In Epithelial-Mesenchymal Gradient

Intratumoral heterogeneity is intrinsically comprised of molecular alterations of tumor cells and extrinsically from interconnections with microenvironments. This study explores the spatial heterogeneity of ovarian clear cell carcinoma (OCCC), a rare cancer with significance to East Asian women. We profile 21 primary-metastatic tumor pairs in a discovery cohort and 16 tumors in two validation cohorts using spatial transcriptomic (ST) platforms. Our integrative analysis revealed an inverse relationship between OXPHOS and inflammation along the EMT gradient. OCCC cells undergoing partial EMT have metabolic shifts and lose LCN2 expression, possibly via concomitant down-regulation of SOX9. Conversely, LCN2 expression correlated with OXPHOS-enriched tumor signature, low EMT, and better outcomes in OCCC. Single-cell ST profiling using CosMx further identifies nine spatially distinct cancer cell populations including the LCN2-high cancer subclone with a high epithelial score. SOX9 induction could partially restore epithelial-ness in LCN2-low cells suggesting that plasticity in OCCC is achieved via transcriptional reprogramming. Our findings provide further insights into epithelial-mesenchymal plasticity and the adaptive interactions between cancer cells and their microenvironments in OCCC.

cancer biology↗

bioRxiv2023

PGSbuilder: An end-to-end platform for human genome association analysis and polygenic risk score predictions

Understanding the genetic basis of human complex diseases is increasingly important in the development of precision medicine. Over the last decade, genome-wide association studies (GWAS) have become a key technique for detecting associations between common diseases and single nucleotide polymorphisms (SNPs) present in a cohort of individuals. Alternatively, the polygenic risk score (PRS), which often applies results from GWAS summary statistics, is calculated for the estimation of genetic propensity to a trait at the individual level. Despite many GWAS and PRS tools being available to analyze a large volume of genotype data, most clinicians and medical researchers are often not familiar with the bioinformatics tools and lack access to a high-performance computing cluster resource. To fill this gap, we provide a publicly available web server, PGSbuilder, for the GWAS and PRS analysis of human genomes with variant annotations. The user-friendly and intuitive PGSbuilder web server is developed to facilitate the discovery of the genetic variants associated with complex traits and diseases for medical professionals with limited computational skills. For GWAS analysis, PGSbuilder provides the most renowned analysis tool PLINK 2.0 package. For PRS, PGSbuilder provides six different PRS methods including Clumping and Thresholding, Lassosum, LDPred2, GenEpi, PRS-CS, and PRSice2. Furthermore, PGSbuilder provides an intuitive user interface to examine the annotated functional effects of variants from known biomedical databases and relevant literature using advanced natural language processing approaches. In conclusion, PGSbuilder offers a reliable platform to aid researchers in advancing the public perception of genomic risk and precision medicine for human disease genetics. PGSbuilder is freely accessible at http://pgsb.tw23.org.

bioinformatics↗

bioRxiv2021

Connecting MHC-I-binding motifs with HLA alleles via deep learning

The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned.

bioinformatics↗

bioRxiv2020

Mitochondrial ATP Synthase Trafficking along Microtubules to Cell Surface Depends on KIF5B and DRP1

Ectopic adenosine triphosphate (eATP) synthase, located on the cell surface instead of the mitochondrial inner membrane, has been discovered to be a novel target for the treatment of various types of cancer. However, the mechanism of eATP synthase trafficking toward the cell surface remains unclear. In this study, we used an integrative approach incorporating multiomics, super-resolution imaging, real-time live-cell tracing, and various functional analyses to derive a transport model of eATP synthase from the mitochondria to the plasma membrane in cancer cells. We determined that the ATP synthase complex is first assembled in the mitochondria and subsequently delivered to the cell surface through the microtubule-mediated mitochondrial transport pathway. We also found that dynamin-related protein 1 (DRP1) enhances mitochondrial fission and subsequently associates with microtubule motor protein kinesin family member 5B (KIF5B) to promote the subcellular transportation of eATP synthase. Consequently, our work provides a blueprint for eATP synthase trafficking from the mitochondria toward the cell surface.

systems biology↗

bioRxiv2020

Redirected tropisms of extracellular vesicles and exomeres yield distinct biodistribution profiles

Extracellular particles (EP) including extracellular vesicles (EVs) and exomeres have been shown to play significant roles in diseases and therapeutic applications. However, their spatiotemporal dynamics in vivo have remained largely unresolved in detail due to the lack of a suitable method. We therefore created a bioluminescence resonance energy transfer (BRET)-based reporter, PalmGRET, to enable pan-EP labelling ranging from exomeres (< 50 nm) to small (< 200 nm) and medium and large (> 200 nm) EVs. PalmGRET emits robust, sustained signals and allows the visualization, tracking and quantification of the EPs from whole-animal to nanoscopic resolutions under different imaging modalities, including bioluminescence, BRET and fluorescence. Using PalmGRET, we show that EPs released by lung metastatic hepatocellular carcinoma (HCC) exhibit lung tropism with varying distributions to other major organs in immunocompetent mice. We further demonstrate that gene knockdown of lung-tropic membrane proteins, solute carrier organic anion transporter family member 2A1 (Slco2a1), alanine aminopeptidase (Cd13) and chloride intracellular channel (Clic1) decreases HCC-EP distribution to the lungs and yields distinct biodistribution profiles. We anticipate that EP-specific imaging, quantitative assays and detailed in vivo characterization to be a starting point for more accurate and comprehensive in vivo models of EP biology and therapeutic design.

cell biology↗

bioRxiv2019

Characterizing the Cancer-Associated Microbiome with Small RNA Sequencing Data

The microbiome is recognized as a quasi-organ in the human body. In particular, the gut microbiome is correlated with immune function, metabolism, and tumorigenesis. When dysbiosis of the microbiome occurs, this variation may contribute to alterations in the microenvironment, potentially inducing an inflammatory immune response and providing a niche for neoplastic growth. However, there is limited evidence regarding the correlation and interaction between the microbiome and tumorigenesis. By utilizing microRNA sequencing data of patients with colon and rectal cancer from The Cancer Genome Atlas, we designed a novel analytical process to extract non-human small RNA sequences and align them with the microbial genome to obtain a comprehensive view of the cancer-associated microbiome. In the present study, we identified > 1000 genera among 630 colorectal samples and clustered these samples into three distinctive colorectal enterotypes. Each cluster has its own distinctive microbial composition and interactions. Furthermore, we found 12 genera from these clusters that are associated with cancer stages and revealed their putative functions. Our results indicate that the proposed analytical approach can effectively determine the cancer-associated microbiome. It may be readily applied to explore other types of cancer, in which specimens of the microbiome are difficult to collect.

bioinformatics↗

bioRxiv2017

Identification of Cell States Using Super-Enhancer RNA

A new class of regulatory elements called super-enhancers, comprised of multiple neighboring enhancers, have recently been reported to be the key transcriptional drivers of cellular, developmental, and disease states. Here, we propose to define super-enhancer RNA as highly expressed enhancer RNAs that are transcribed from a cluster of localized genomic regions. Using the cap analysis of gene expression sequencing data from FANTOM5, we systematically explored the enhancer and messenger RNA landscapes in hundreds of different cell types in response to various environments. Applying non-negative matrix factorization (NMF) to super-enhancer RNA profiles, we found that different cell types were well classified. In addition, through the NMF of individual time-course profiles from a single cell-type, super-enhancer RNAs were clustered into several states with progressive patterns. We further investigated the enriched biological functions of the proximal genes involved in each pattern, and found that they were associated with the corresponding developmental process. The proposed super-enhancer RNAs can act as a good alternative, without the complicated measurement of histone modifications, for identifying important regulatory elements of cell type specification and identifying dynamic cell states.

systems biology↗