Biology subjects

Yi, H.

Publications and source records attributed to Yi, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Coupled Effects of Airway Deformation and Carrier Gas on Pulmonary Gas-Particle Flow Dynamics Using an Euler-Lagrange Method with Dynamic Mesh

Physiologically realistic prediction of inhaled aerosol deposition in the airways requires simultaneous consideration of airway deformation kinematics and carrier-gas properties. This study developed a 3D deformable lung airway model (DLAM) using an experimentally validated computational fluid particle dynamics (CFPD) model coupled with a dynamic mesh method. Both the DLAM and a conventional static lung airway model (SLAM) were used to predict and compare inhaled gas-particle transport and deposition in a subject-specific mouth-to-generation 9 (G9) human respiratory system. To assess the significance of airway deformation, SLAM and DLAM were compared under identical 12 breaths per minute (BPM) inhalation-exhalation waveforms for both conventional air and heliox (70% He-30% O2), considering particle sizes of 1, 5, and 10 um. The effects of carrier gas and breathing frequency were further evaluated using the more physiologically realistic DLAM, comparing air and heliox at 12, 16, and 20 BPM for the same particle sizes. Results showed that the influence of airway deformation on aerosol transport is strongly dependent on particle size. Differences between SLAM and DLAM were negligible for 1 um particles. In contrast, for 5 and 10 um particles, SLAM consistently underpredicted beyond-G9 delivery and overpredicted G0-G9 local deposition for both air and heliox. For example, for 10 um particles at 12 BPM, accounting for airway deformation kinematics increased the beyond-G9 delivery fraction by an absolute 3.76% with air and 5.55% with heliox. After establishing the size-dependent deformation effect, DLAM was further used to evaluate the influence of carrier gas and breathing frequency on inhaled aerosol transport and deposition. Compared with air, heliox reduced turbulence kinetic energy, wall shear stress, and pressure drop, and enhanced beyond-G9 delivery of larger particles. Specifically, for 10 um particles, the absolute increases in beyond-G9 delivery were 16.82%, 20.25%, and 22.90% at 12, 16, and 20 BPM, respectively. Increasing breathing frequency reduced distal delivery, particularly for 5 and 10 um particles, whereas 1 um particles maintained approximately 99% beyond-G9 penetration across all investigated conditions. These findings indicate that DLAM is essential for accurate regional dosimetry of higher-inertia aerosols, for which the static airway simplification can significantly bias predicted deposition and distal delivery.

bioengineering↗

bioRxiv2026

Mechanisms underlying the defective development of xyloglucan-deficient root hairs in Arabidopsis thaliana

Hemicellulosic xyloglucan is abundant in the primary cell walls of eudicots and non-gramineous monocots and contributes to their mechanical integrity. Its deficiency results in morphologically abnormal root hairs in Arabidopsis mutants lacking either two XYLOGLUCAN XYLOSYLTRANSFERASE (xxt1 xxt2) or five CELLULOSE SYNTHASE-LIKE C (cslc-{triangleup}5) genes, but the cellular and biomechanical mechanisms underlying these abnormalities have remained unexplored. We combined multiple imaging modalities and quantitative modeling to unmask these mechanisms, first observing that xyloglucan-deficient mutants produced fewer and shorter root hairs that grew slower than wild type. Xyloglucan-deficient trichoblasts showed root hair initiation defects, helping to explain the sparseness of root hairs in these mutants. The mutant root hairs also had lower turgor pressure, thicker walls, and wider cross-sections, that explain lack of anisotropic growth, but cellulose organization appeared normal in xyloglucan-deficient root hairs. Brillouin microscopy suggested that xyloglucan-deficient root hairs were softer than wild-type root hairs. Finite element modeling of mutant root hairs predicted lower surface stress and growth rates, which were directly proportional to turgor pressure and inversely proportional to wall thickness. Stress patterns on models hint at potential mechanisms of bulging in xyloglucan-deficient root hairs. We propose that though cellulose orientation specifies the axis of cell expansion, it cannot enforce it without xyloglucan that interlinks microfibrils into a load-bearing network, identifying matrix-cellulose interaction as a distinct and necessary determinant of anisotropic plant cell growth.

cell biology↗

bioRxiv2026

Estimation of neuronal tuning for word meaning from passively recorded naturalistic speech

The ability to derive neural-level language coding models holds great scientific and clinical potential. Current approaches are limited by the scale and ethological validity of input data; applications requiring large, rare, or naturalistic samples in particular would benefit from the ability to infer neural coding from incidental everyday speech. Here we present a novel pipeline designed to leverage spontaneous and incidental naturalistic speech. This pipeline performs transcription, segmentation, and video-assisted diarization, as well as alignment and spike detection of neural data. We apply this pipeline to a dataset derived from 21 patients (6+ days each, over 800 hours and 5 million words total). We benchmark both encoding and decoding models against extensive and rare ground-truth control datasets consisting of human-curated word-level temporal alignment and manually sorted spikes. We further validate our approach by quantifying representational drift, effect of dataset size, and differences between six brain areas. Together, these findings demonstrate that incidental natural speech is sufficiently processed in the brain to enable the estimation neural-level embeddings.

neuroscience↗

bioRxiv2026

PanVariants: Best Practice for Pangenome-based Variant Calling Pipeline and Framework

BackgroundAlthough pangenome references offer richer population diversity compared to linear references, current mainstream pangenome-based variant callers are limited to detecting only known variants stored in the graph. To address this limitation, we developed PanVariants, a novel pipeline designed to improve the detection of both known and novel variants accurately. We systematically evaluated its performance against the traditional linear alignment solution (BWA+GATK/Manta) and the existing pangenome-aware solution (DRAGEN/PanGenie) in three contexts: small variants (SNVs/indels) and structural variants (SVs) accuracy in Genome in a Bottle samples, clinical detection on positive samples, and application in cohort-based joint calling. ResultsBy integrating k-mer-based and mapping-based methods, PanVariants significantly reduced variant errors (FPs + FNs), achieving a 73% reduction compared to BWA+GATK and a 45% reduction compared to DRAGEN for SNVs. Retraining the DeepVariant model with high-quality DNBSEQ data further decreased errors by 15%. For SVs detection, PanVariants attained an F1-score of 89.39%, markedly outperforming DRAGEN (68.18%) and BWA+Manta (58.33%), approaching long-read sequencing performance (95.22%). In validation using clinical positive samples, PanVariants successfully detected all expected pathogenic variants while PanGenie failed. In the cohort joint-calling analysis, PanVariants detected more variants, made fewer Mendelian inheritance errors, and gave better per-sample accuracy than GATK. ConclusionsPanVariants establishes a robust framework and best-practice pipeline for pangenome-based variant detection, achieving both sensitive novel variant discovery and high accuracy for SNVs, indels and SVs. Our systematic evaluation of optional processing steps and input variables offers practical guidance for users. Validated across diagnostic and population-based applications, our findings strongly support the transition from linear to pangenome references in future genomics.

bioinformatics↗

bioRxiv2026

Computational Fluid Particle Dynamics-Informed Machine Learning Prototype for a User-Centered Smart Inhaler Enabling Uniform Drug Delivery to Small Airways

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles to these regions is crucial to maximize treatment efficacy while minimizing side effects. However, conventional inhalation therapy approaches (i.e., full-mouth particle release and inhalation (FMD)) typically result in insufficient drug deposition in the small airways and an uneven distribution across the five lung lobes. To address such deficiencies, the goals of this study are triple folds: (1) to develop a fast and accurate framework to secure target drug delivery (TDD) nozzle diameter and location based on the conventional computational fluid particle dynamics (CFPD)-FMD simulations, (2) to develop a CFPD-informed machine learning (ML) inverse-design framework that predicts optimal inhaler nozzle parameters based on patient-specific breathing patterns and drug properties, and (3) to demonstrate the feasibility of embedding this framework into a user-centered smart inhaler prototype to improve uniform TTD to the small airways across all five lung lobes. Specifically, a subject-specific mouth-to-generation-10 human respiratory system was employed, and 108 high-fidelity CFPD-FMD simulations were performed under varied physiological and design parameters, including tidal volume, particle diameter, release location, and release timing. Particle release maps generated from those CFPD-FMD simulations via backtracking identified optimal nozzle diameters and locations that promote uniform multi-lobe drug delivery while limiting off-target deposition. Accordingly, a dataset was compiled with inputs (i.e., flow rate, particle size, release z-coordinate, release time) and targets (i.e., nozzle center x- and y-coordinates, nozzle diameter). These inputs and targets form the CFPD-TDD dataset, on which 16 ML models were trained to learn inverse mapping from patient- and drug-specific inputs to optimal nozzle design parameters. Performance was evaluated using mean squared error (MSE) and mean absolute error (MAE) overall and per target feature. Parametric analysis using CFPD-FMD simulations was conducted to determine how patient-specific and drug-specific factors affect pulmonary air-particle transport dynamics and to explain why achieving CFPD-TDD in small airways with CFPD-FMD strategies remains challenging. Furthermore, the ML evaluation in this feasibility study demonstrated robust learning of the inverse mapping from patient-specific inputs to optimal nozzle parameters. Four top-performing models showed consistently low MSE/MAE across cases, and an ensemble (i.e., mixed model (MixModel)) combining their strengths was formulated. Independent CFPD-TDD simulations beyond the training and testing datasets were used as the ground truth to validate ML-predicted nozzle configurations. Compared with conventional CFPD-FMD strategies, ML-guided nozzle designs significantly improved inter-lobar deposition uniformity and reduced off-target deposition in the upper airways, demonstrating the feasibility of ML-enabled TDD to the small airways. Overall, this study establishes a CFPD-informed ML inverse-design framework as a viable algorithmic foundation for user-centered smart inhalers, enabling adaptive, patient-specific TDD to the small airways with improved deposition uniformity across all five lung lobes. By integrating first-principle-based CFPD with ML, this work provides a methodological pathway toward next-generation smart inhalers for more effective treatment of small airway diseases.

bioengineering↗

bioRxiv2026

CellPheno: A High-throughput Computational Platform for Quantifying Cellular Resolution Whole Brain Microscopy Images

Advances in tissue clearing and light-sheet microscopy enable cellular resolution whole-brain 3D imaging. However, whole-brain quantification tools do not yet meet demands for efficiency or assess morphometry. Here we present CellPheno, a 3D nuclei instance segmentation framework for high-throughput cellular phenotyping. CellPheno quantifies an entire P4 mouse brain within 15 hours. We showcase whole-brain morphometry, enhanced stitching, and co-localization across multiple cell types in 53 brains.

neuroscience↗

bioRxiv2026

Chd8 haploinsufficiency leads to molecular layer heterotopias and age-dependent cortical expansion

Mutations in the chromatin remodeler CHD8 are associated with autism and macrocephaly. While mouse models of Chd8 haploinsufficiency recapitulate brain overgrowth, the specific cellular mechanisms and developmental timing that lead to these anatomical abnormalities remain poorly understood. Here, we conducted 3D imaging of Chd8V986*/+ mouse brains using magnetic resonance imaging followed by tissue clearing and cellular resolution light-sheet microscopy across embryonic and postnatal developmental stages. We found that brain overgrowth occurs postnatally, driven by an increase in non-neuronal cells prior to volumetric expansion. Unexpectedly, we identified prevalent molecular layer heterotopias (MLH) within the frontal cortex of Chd8V986*/+ mice composed of neurons breaking through the pial surface during embryonic development and persisting throughout life. Increased incidence of MLH, previously identified in individuals with idiopathic autism, was replicated across three independent Chd8+/- mouse models and present across multiple genomic backgrounds, establishing aberrant neuronal migration as a core feature of Chd8 haploinsufficiency.

neuroscience↗

bioRxiv2026

Optimal transport fate mapping resolves T cell differentiation dynamics across tissues

Immune responses evolve across time and tissues through coordinated programs of proliferation, differentiation, and migration, yet most single-cell measurements capture only static molecular snapshots. As a result, reconstructing how immune cells transition between alternative fates remains challenging, particularly for CD8 T cells, whose differentiation is highly dynamic and shaped by rapid expansion, contraction, and tissue trafficking. Here, we introduce an optimal transport-based fate mapping framework that reconstructs continuous CD8 T cell trajectories across time and tissues. Applied to longitudinal single-cell RNA-seq data from CD8 T cells responding to acute viral infection in mice, this approach accurately recapitulates population dynamics and resolves coherent effector and memory T cell differentiation trajectories. Extending the model to multiple tissues, we identify and experimentally validate temporally distinct waves of migration into the small intestine that give rise to divergent tissue-resident memory (TO_SCPLOWRMC_SCPLOW) fates, long-lived T cells crucial in immunosurveillance. By integrating optimal transport inference with time-resolved in vivo labeling, we demonstrate that CD52 marks recent tissue entrants and distinguishes them from TO_SCPLOWRMC_SCPLOW precursors. Finally, trajectory-guided analysis of transcription factor regulons reveals both shared and context-specific gene regulatory programs and identifies AP4 as a key regulator of circulating versus tissue-resident specification. These results establish optimal transport as a principled framework for reconstructing immune cell fate dynamics and provide a quantitative map of early events governing antiviral CD8 T cell differentiation across tissues.

bioinformatics↗

bioRxiv2025

Mechanical constraint causes lower turgor, thicker walls, and faster growth in Arabidopsis root hairs

Root hairs absorb water and mineral nutrients while anchoring growing root tips. They must navigate through soils of varying mechanical properties. Mechanical changes in their microenvironment affect root hair shape and growth rate, but what underlies these responses has remained elusive. To uncover these mechanisms, we grew seedlings of Arabidopsis thaliana (Col-0) in media with increasing mechanical stiffness. Col-0 seedlings showed both shorter and fewer root hairs with increasing mechanical resistance. We used incipient plasmolysis to estimate turgor pressure in trichoblasts and found that it also decreased with increasing media stiffness. Because cellulose is the major load-bearing polymer in the cell wall and influences cell expansion, we quantified cellulose orientation in root hairs. We found that cellulose fibrils were oriented at steeper angles relative to the growth axis in root hairs grown in stiffer media, suggesting a response to mechanical stress. Cell wall thickness also increased with increasing media stiffness. Microtubule orientations followed patterns that were similar to those of cellulose fibrils, but at a smaller angle relative to the growth axis, whereas microtubule density decreased with increasing media stiffness. Unexpectedly, we observed that root hairs grew faster in stiffer media, implying that their growth is misregulated, potentially triggering wall integrity signaling that causes early growth arrest. Finite element modeling of root hairs predicted decreased surface stress, explaining these growth phenotypes. These findings help establish mechanistic links between mechanotransduction, cytoskeletal dynamics, and cell wall assembly during root hair growth.

cell biology↗

bioRxiv2025

Midbrain microglia-integrated organoids as a next-generation tool for chronic morphine withdrawal research: A pilot study

In the midst of an opioid epidemic, opioid use disorder (OUD) can lead to significant clinical impairment or distress (such as opioid dependence and/or addiction) and overdose deaths. Therefore, it is significant to elucidate the exact molecular mechanisms of OUD and identify more effective therapies. Human induced pluripotent stem cell (hiPSC)-derived organoids are self-organized 3D tissues and mimic in vivo human organs. Increasingly, human-specific features of opioid abuse are being investigated using emerging brain organoids, which faithfully model the key functional, structural and biological complexities of neural tissues. However, a significant limitation of brain organoids is the absence of microglia which plays a fundamental role in OUD-related remodeling of neural circuits. In this pilot study, using hiPSC KOLF2.1J cell line, we developed our approach for generating both midbrain organoid and microglia and verified that our midbrain organoid was successfully integrated with microglia. We found that chronic exposure to morphine triggers key elements of immune responses including increased TNF signaling and downregulation of sirtuin3 (Sirt3, a mitochondrial protein, preventing oxidative stress) and manganese superoxide dismutase (MnSOD, located in the mitochondrial matrix) in microglia-organoid co-cultures. Our findings suggest that immunocompetent midbrain organoids provide excellent models with which to study human-specific mechanisms of neuroinflammation and neurodegeneration. By combining disease relevance with scalability, the model system can be utilized as an effective tool for drug screening and toxicity testing.

neuroscience↗

bioRxiv2025

Bridging Genomics and Clinical Medicine: RSVrecon Enhances RSV Surveillance with Automated Genotyping and Clinically-important Mutation Reporting

BackgroundRespiratory Syncytial Virus (RSV) causes significant respiratory infections, particularly in young children and elderly adults. Genetic variations in the fusion (F) protein can reduce the efficacy of vaccination and monoclonal antibody treatments, emphasizing the need for genomic surveillance of this virus. MotivationCurrent pipelines for RSV genome assembly focus on sequence reconstruction but often lack features for detecting genotypes, clinically relevant mutations, or presenting results in formats that are suitable for clinical researchers. ResultsWe introduce RSVrecon, an advanced bioinformatics pipeline for comprehensive RSV genome assembly and phylogenetic analysis. RSVrecon processes raw FASTQ files into annotated variant reports and delivers results in multiple formats (CSV, PDF, HTML) tailored to diverse end users. A key innovation of RSVrecon is not only its integrated detection of clinically critical features--including genotype classification and F protein mutation calling, capabilities absent in most analytical pipelines--but also its presentation of these results to clinicians via an integrated, graphical, and user-friendly interface. Its modular design, powered by Nextflows modern framework, ensures a scalable and robust workflow, while user-friendly reports enable seamless translation of genomic data into actionable clinical insights. Benchmarking against existing pipelines using clinical datasets revealed that RSVrecon achieves comparable genomic assembly accuracy while excelling in three key dimensions: (1) expanded functional capabilities, (2) intuitive biological interpretation of the results, and (3) superior user experience and accessibility. By seamlessly translating RSV genomic data into clinically meaningful information, RSVrecon empowers research breakthroughs, guides clinical care decisions, and strengthens surveillance systems. With these features, RSVrecon offers an enhanced approach to RSV surveillance and research. The tool is freely available at https://github.com/stjudecab/rsvrecon and https://github.com/stjudecab/RSVreconPy. Key PointsO_LIRSVrecon enables comprehensive clinical detection for RSV such as identifying genotypes and F protein mutations. C_LIO_LIRSVrecon delivers results through an integrated, graphical, and user-friendly interface for clinicians. C_LIO_LIRSVrecons Nextflow-based design ensures robust, scalable, and consistent performance. C_LIO_LIRSVrecon matches assembly accuracy while excelling in functionality, interpretation, and accessibility during benchmarking. C_LI

bioinformatics↗

bioRxiv2025

EcDNA-borne PVT1 fusion stabilizes oncogenic mRNAs

Extrachromosomal DNA (ecDNA) amplifications are prevalent drivers of human cancers. We show that ecDNAs exhibit elevated structural variants leading to gene fusions that produce oncogene fusion transcripts. The long noncoding RNA (lncRNA) gene PVT1 is the most recurrent structural variant across cancer genomes, with PVT1-MYC fusions arising most frequently on ecDNA. PVT1 exon 1 is the predominant 5 partner fused to MYC or other oncogenes on the 3 end. Mechanistic studies demonstrate that PVT1 exon 1 confers enhanced RNA stability for fusion transcripts, which requires PVT1 exon 1 interaction with SRSF1 protein. Genetic rescue of MYC-addicted cancer models and isoform-specific single-cell RNA sequencing of tumors reveal that PVT1-MYC better supports MYC dependency and better activates MYC target genes in vivo. Thus, the mutagenic landscape of ecDNA contributes to genome instability and generates chimeric fusions of lncRNA and mRNA genes, selecting PVT1 5 region as a stabilizer of oncogene mRNAs.

cancer biology↗

bioRxiv2024

Pre-Assembly NGS Correction of ONT Reads Achieves HiFi-Level Assembly Quality

BackgroundRecently developed hybrid assemblies can achieve Telomere-to-Telomere (T2T) completeness of some chromosomes. However, such approaches involve sequencing a large volume of both Pacific Biosciences high-fidelity (HiFi) and Oxford Nanopore Technologies (ONT) sequencing reads. Along with this, third-generation sequencing techniques are rapidly advancing, increasing the available length and accuracy. To reduce the final cost of genome assembly, here we investigated the possibility of assembly from low-coverage samples and with only ONT corrected by Next-Generation Sequencing (NGS) sequencing reads. We demonstrated that ONT-based assembly approaches corrected by NGS can achieve performance metrics comparable to more expensive hybrid approaches based on HiFi sequencing. ResultsWe investigated the assembly of different chromosomes of HG002 and the low-coverage performance of state-of-the-art hybrid assembly tools, including Verkko and Hifiasm, and ONT-based assemblers Shasta and Flye. Our study shows that even with the best NG50, Hifiasm has many misassemblies in the centromere region, raising doubts about its T2T capabilities. Additionally, ONT-based assemblies perform well at low coverage, offering a cost-effective solution. To achieve better assembly quality, we evaluated the performance of different NGS technologies across various aspects of hybrid genome assembly, including pre-assembly correction, haplotype phasing, and polishing, and found them to be similarly effective. For the NGS technology stLFR, we proposed two-round assembly methods that utilize stLFR linked-read data to achieve assembly phasing performance comparable to that obtained with trio data. ConclusionWe found that assemblies based on ONT corrected by NGS can match or exceed HiFi-based assemblies in accuracy. It appears that no assembly tool investigated here can achieve T2T completeness, even with 50X coverage. Therefore, we recommend using a combination of regular R9 or R10 simplex ONT reads and accurate NGS reads for assembly without aiming for T2T completeness. These ONT-based assemblies perform well even at low coverage, offering a cost-effective solution.

genomics↗

bioRxiv2024

MetaKSSD Enables Rapid and Online Metagenomic Taxonomic Profiling and Profile Searching

The rapid increase in genomes and metagenomic data presents major scalability and efficiency challenges for current metagenomic profilers. In response, we introduce MetaKSSD, which redefines reference taxonomic marker database (MarkerDB) construction and metagenomic profiling using sketch operations, offering efficiency improvements by orders of magnitude. MetaKSSD encompasses 85,202 species in its MarkerDB using just 0.17GB of storage and profiles 10GB of data within seconds, utilizing only 0.5GB of memory. Extensive benchmarking experiments demonstrated that MetaKSSD is among the top-performing profilers across various metrics. In a microbiome-phenotype association study, MetaKSSD identified significantly more effective associations than MetaPhlAn4. We profiled 382,016 metagenomic runs using MetaKSSD, conducted extensive sample clustering analyses, and suggested potential yet-to-be-discovered niches. Additionally, we developed functionality in MetaKSSD for instantaneous searching among large-scale profiles. The client-server architecture of MetaKSSD allows the swift transmission of metagenome sketches over the network and enables real-time online metagenomic analysis, facilitating use by non-expert users.

bioinformatics↗

bioRxiv2024

CoRAL accurately resolves extrachromosomal DNA genomestructures with long-read sequencing

Extrachromosomal DNA (ecDNA) is a central mechanism for focal oncogene amplification in cancer, occurring in approximately 15% of early stage cancers and 30% of late-stage cancers. EcDNAs drive tumor formation, evolution, and drug resistance by dynamically modulating oncogene copy-number and rewiring gene-regulatory networks. Elucidating the genomic architecture of ecDNA amplifications is critical for understanding tumor pathology and developing more effective therapies. Paired-end short-read (Illumina) sequencing and mapping have been utilized to represent ecDNA amplifications using a breakpoint graph, where the inferred architecture of ecDNA is encoded as a cycle in the graph. Traversals of breakpoint graph have been used to successfully predict ecDNA presence in cancer samples. However, short-read technologies are intrinsically limited in the identification of breakpoints, phasing together of complex rearrangements and internal duplications, and deconvolution of cell-to-cell heterogeneity of ecDNA structures. Long-read technologies, such as from Oxford Nanopore Technologies, have the potential to improve inference as the longer reads are better at mapping structural variants and are more likely to span rearranged or duplicated regions. Here, we propose CoRAL (Complete Reconstruction of Amplifications with Long reads), for reconstructing ecDNA architectures using long-read data. CoRAL reconstructs likely cyclic architectures using quadratic programming that simultaneously optimizes parsimony of reconstruction, explained copy number, and consistency of long-read mapping. CoRAL substantially improves reconstructions in extensive simulations and 9 datasets from previously-characterized cell-lines as compared to previous short-read-based tools. As long-read usage becomes wide-spread, we anticipate that CoRAL will be a valuable tool for profiling the landscape and evolution of focal amplifications in tumors.

cancer biology↗

bioRxiv2024

An Evaluation of the Tumor Microenvironment through CALR, IL1R1, IFNB1, and IFNG to Assess Prognosis and Immunotherapy Response in Bladder Cancer Patients

Immunogenic cell death (ICD) is a type of cell death sparking adaptive immune responses, can reshape the tumor microenvironment (TME). Exploring key ICD-related genes in bladder cancer (BLCA) could enhance personalized treatment. TCGA BLCA patients were divided into two ICD subtypes: ICD-high and ICD-low. High ICD expression linked to increased immune cell infiltration and longer survival, but with potentially suppressed immune function. The high ICD group responded better to PD1-targeted therapy. A risk-scoring model with four ICD-related genes (CALR, IL1R1, IFNB1, IFNG) was validated across TCGA, GEO datasets, and tissue samples, showing higher risk-score correlated with weaker anti-tumor immune function, more tumor-promoting elements, lower immunotherapy response rates, and shorter patient survival.This study connects ICD-related genes to BLCA prognosis and immune infiltration, offering a vital tool for personalized treatment guidance.

immunology↗

bioRxiv2024

Carbohydrate-active enzyme annotation in microbiomes using dbCAN

CAZymes or carbohydrate-active enzymes are critically important for human gut health, lignocellulose degradation, global carbon recycling, soil health, and plant disease. We developed dbCAN as a web server in 2012 and actively maintain it for automated CAZyme annotation. Considering data privacy and scalability, we provide run_dbcan as a standalone software package since 2018 to allow users perform more secure and scalable CAZyme annotation on their local servers. Here, we offer a comprehensive computational protocol on automated CAZyme annotation of microbiome sequencing data, covering everything from short read pre-processing to data visualization of CAZyme and glycan substrate occurrence and abundance in multiple samples. Using a real-world metagenomic sequencing dataset, this protocol describes commands for dataset and software preparation, metagenome assembly, gene prediction, CAZyme prediction, CAZyme gene cluster (CGC) prediction, glycan substrate prediction, and data visualization. The expected results include publication-quality plots for the abundance of CAZymes, CGCs, and substrates from multiple CAZyme annotation routes (individual sample assembly, co-assembly, and assembly-free). For the individual sample assembly route, this protocol takes [~]33h on a Linux computer with 40 CPUs, while other routes will be faster. This protocol does not require programming experience from users, but it does assume a familiarity with the Linux command-line interface and the ability to run Python scripts in the terminal. The target audience includes the tens of thousands of microbiome researchers who routinely use our web server. This protocol will encourage them to perform more secure, rapid, and scalable CAZyme annotation on their local computer servers.

bioinformatics↗

bioRxiv2023

CytoCellDB: A Resource Database For Classification and Analysis of Extrachromosomal DNA in Cancer

Extrachromosomal DNA (ecDNA), or double minute chromosomes, are established cytogenetic markers for malignancy and genome instability. More recently, the cancer community has gained a heightened awareness of the roles of ecDNA in cancer proliferation, drug resistance and epigenetic remodeling. A current hindrance to understanding the biological roles of ecDNA is the lack of available cell line model systems with experimental cytogenetic data that confirm ecDNA status. Although several recent landmark studies have identified common cell lines and tumor models with ecDNA, the current sample size limits our ability to detect ecDNA-driven molecular differences due to limitations in power. Increasing the number of model systems known to express ecDNA would provide new avenues for understanding the fundamental underpinnings of ecDNA biology and would unlock a wealth of potential targeting strategies for ecDNA-driven cancers. To bridge this gap, we created CytoCellDB, a resource that provides karyotype annotations and leverages publicly available global cell line data from the Cancer Dependency Map (DepMap) and the Cancer Cell Line Encyclopedia (CCLE). Here, we identify 139 cell lines that express ecDNA, which is a 200% increase from the current sample size. We expanded the total number of cancer cell lines with ecDNA annotations to 577, which is a 400% increase or 31% of cell lines in CCLE/ DepMap. We demonstrate that a strength of CytoCellDB is the ability to interrogate ecDNA, and a compendium of other chromosomal aberrations, in the context of cancer-specific vulnerabilities, drug sensitivities, and molecular data (genomics, transcriptomics, methylation, proteomics). We anticipate that CytoCellDB will advance cytogenomics research and population-scale discoveries related to ecDNA as well as provide insights into strategies and best practices for determining novel therapeutics that overcome ecDNA-driven drug resistance.

genomics↗