Biology subjects

Chen, C.-Y.

Publications and source records attributed to Chen, C.-Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Adolescent prodromal phenotypes of psychiatric disorders: insights from a novel DISC1-perturbed monkey model

Neurodevelopmental deficits frequently lead to mild but significant adolescent social and cognitive changes, representing prodromal manifestations of adult-onset psychiatric disorders in a cross-disease manner. Although experimental medicine utilizes animal models to address the mechanisms, rodent brains lack critical structures for human higher brain functions such as the dorsolateral prefrontal cortex. To fill this translational gap, we generated monkeys with an impaired DISC1 gene locus, targeting the multifunctional protein essential for neurodevelopment and involved in transdiagnostic mental health risks. Here we analyze the F0 animals by minimizing mosaicism. At ages equivalent to human adolescence, these monkeys exhibited blunted responsiveness to social stimuli and increased internal drives, mirroring altered social behaviors in the prodromal stage in humans. Resting-state functional MRI data indicated disrupted global functional connectivity and reorganization of network topology, marked by a shift in the dominance of brain hubs from prefrontal regions to sensory areas. This primate model provides a unique platform for studying psychiatric disorders, such as schizophrenia, across the developmental trajectory.

neuroscience↗

bioRxiv2026

Lipid diffusion enhanced by molecular motors

An experimental study of myosin I motors on a supported lipid membrane with a nematic actin filament layer is presented. The activities of myosin I motors re-organize the actin layer, in particular the morphology of +1/2 nematic defects. Data from single-particle tracking (SPT) of lipid molecules reveal two interesting features. First, the distribution of average lipid speed over a track shows two groups of lipids (high-speed and low-speed), and the proportion of lipids to be high-speed is higher in regions rich in actin. Second, by classifying short-time movement of the lipids into normal diffusion, directed motion, confined motion, and subdiffusion states, we found that lipids in regions rich in actin have a lower probability to stay in the confined and subdiffusion states and more likely to perform normal diffusion. Furthermore, fluorescence recovery after photobleaching (FRAP) measurements show that the diffusion of lipids in the actin-rich regions is enhanced. The SPT and FRAP results suggest that myosin I motor activities turn low-speed lipids to high-speed ones, and affect the lipid moving state by reducing confined and subdiffusive motion. As a result, lipid diffusion is enhanced by motor activities.

biophysics↗

bioRxiv2026

Multiscale structural MRI differences among collegiate American football players, cross-country runners, and non-athletes

Structural differences observed in collision-sport athletes may reflect repetitive head-impact exposure, broader effects of athletic training, or pre-existing individual variation, making inclusion of a non-collision athlete group important for interpretation of any differences seen. Here, structural T1-weighted MRI data from 17 collegiate American football players, 16 cross-country runners, and 9 non-athletes were analyzed using FSL voxel-based morphometry (VBM), FIRST subcortical volumetry and vertex-wise shape analysis, and FAST whole-brain tissue segmentation. Spatial inference used permutation testing with threshold-free cluster enhancement and family-wise error correction, while scalar analyses used multiplicity-corrected procedures. FAST-derived absolute tissue volumes were also examined in a sensitivity analysis adjusting for FreeSurfer estimated total intracranial volume (eTIV). VBM showed recurrent brainstem-centered differences, with non-athletes showing greater local gray matter measures than both athlete groups, whereas football players showed greater brainstem measures than runners. FIRST vertex-wise analysis identified corrected differences involving the brainstem and selected subcortical structures, while no total FIRST structure volume survived false discovery rate correction. Unadjusted FAST analysis identified a CSF group effect, F(2, 24.38) = 11.46, p < .001, q = .001. After eTIV adjustment, the CSF group effect remained, F(2, 38) = 14.30, q < .001, and a white matter group effect was also identified, F(2, 38) = 9.59, q < .001; gray matter and total segmented tissue did not show corrected adjusted effects. These findings indicate that structural variation depends on anatomical scale, comparison group, and head-size adjustment and cannot be attributed uniquely to collision-sport exposure.

neuroscience↗

bioRxiv2026

Resolution-aware spectral parameterization reveals robust context-dependent aperiodic EEG dynamics during real-world table tennis

Mobile EEG can link neuroimaging to natural behavior, but effects recorded during movement, and sport in particular, are difficult to interpret because neural activity overlaps with signals from movement, muscle activity, and cable movement artifacts. We analyzed the OpenNeuro Real World Table Tennis dataset to test whether peri-hit spectral activity could be analyzed and how it differed between human-opponent and ball-machine play. Data from 25 participants comprising EEG, noise-layers, EMG and motion recordings was analyzed with pre-hit, impact, and post-hit windows. EEG power bands were residualized using trial-level root mean square from noise-layer, EMG, and motion/miscellaneous channels. Spectral parameterization modeled spectra as an aperiodic component plus periodic peaks. The principal short-window model was resolution-aware; sensitivity analyses reduced model complexity, used a 2.0-s peri-hit subwindow (-1.0 to +1.0 s), or disabled periodic fitting. Results showed residualized alpha and beta power was lower during ball-machine play, including pre-hit posterior alpha power. The clearest context difference was a higher ball-machine aperiodic exponent over posterior and parietal sensors, corresponding to a steeper broadband spectral slope. Posterior post-hit means were 0.451 and 0.606 for human-opponent and ball-machine play, respectively. A focal parietal effect persisted across specifications. The results support artifact-informed, resolution-aware spectral analysis as an effective approach for detecting robust spectral differences between naturalistic visuomotor contexts and suggest that EEG may provide useful information about the processes engaged by different practice environments, without establishing a single cognitive or physiological mechanism or demonstrating a training outcome.

neuroscience↗

bioRxiv2026

Integrated Proteo-Metabolomics of Urinary Extracellular Vesicles Reveals Early Molecular Divergence and Temporal Pathogenesis of Sepsis-Associated AKI

BackgroundSepsis-associated acute kidney injury (S-AKI) is a major contributor to morbidity and mortality in critically ill patients. However, the molecular mechanisms underlying its temporal progression remain poorly understood because conventional biomarkers primarily reflect renal dysfunction rather than disease pathogenesis. Urinary extracellular vesicles (uEVs), which carry kidney-derived molecular cargo, provide a promising platform for monitoring renal-specific biological alterations during disease progression. MethodsWe conducted a longitudinal multi-omics study of uEVs collected from 81 patients with sepsis, including 48 patients with S-AKI and 33 sepsis-only controls. Patients were randomly assigned to a discovery cohort (n = 52) and an independent validation cohort (n = 29). Urine samples were collected at Day 1, Day 4, and Day 8 after AKI diagnosis. High-resolution proteomic and metabolomic profiling was performed to characterize temporal molecular alterations. Enriched pathways identified in the discovery cohort were evaluated in the validation cohort using pathway-level concordance analysis. ResultsComparative analysis between S-AKI and sepsis-only patients identified distinct stage-specific molecular alterations throughout disease progression. At the early stage (Day 1), validated pathways included complement and coagulation cascades, ferroptosis, HIF-1 signaling, sphingolipid metabolism, and arachidonic acid metabolism, highlighting coordinated inflammatory, hypoxic, and lipid metabolic responses. During the mid-stage (Day 4), persistent activation of complement and coagulation cascades, ferroptosis, and HIF-1 signaling was accompanied by metabolic reprogramming involving alanine, aspartate and glutamate metabolism and tyrosine metabolism. Although limited sample availability reduced statistical power at Day 8, phenylalanine metabolism remained validated in the metabolomic analysis, suggesting persistent metabolic dysregulation during late-stage disease progression. ConclusionsThis study provides the first longitudinal, independently validated multi-omics characterization of human uEVs in S-AKI. By integrating proteomic and metabolomic profiling, we reveal the temporal evolution of renal-specific molecular pathways from early inflammatory and hypoxic responses to subsequent metabolic reprogramming. These findings establish uEV-based multi-omics as a promising strategy for molecular phenotyping of S-AKI beyond conventional clinical biomarkers and provide a valuable resource for future biomarker discovery and therapeutic target identification.

biochemistry↗

bioRxiv2026

Long-term memory performance optimization via Neural network-based curve fitting in Drosophila

Long-term memory (LTM) formation typically requires extensive training or highly salient experiences, limiting learning efficiency. Operant conditioning is generally thought to produce stronger memory than classical conditioning because of its active learning component. Unexpectedly, however, laser-based social conditioning in Drosophila melanogaster revealed that while classical paradigms yielded lower short-term memory (STM) scores but higher LTM retention, operant paradigms exhibited higher STM scores followed by rapid LTM decay. To resolve this discrepancy, we employed the AI Complex Systems Response (AI-CSR) framework, which reconstructs high-dimensional learning landscapes from sparse sampling and predicts globally optimal training conditions. Following AI-CSR optimization, operant conditioning produced a twofold increase in LTM scores, yielding the strongest 24-hour social memory performance reported in flies to date and revealing the expected superiority of active learning, which had conversely shown poorer performance under standard training protocols. In contrast, AI-CSR did not further enhance classical conditioning performance but reduced training time by 50%. Single-cell RNA sequencing revealed expanded neuronal recruitment marked by the activation and inhibition of various gene combinations. Together, these findings link circuit-level reorganization with molecular programs underlying efficient long-term memory and demonstrate how AI-guided optimization can uncover latent learning capacity in biological systems.

animal behavior and cognition↗

bioRxiv2026

A Unifying Mechanism for Synaptic Amyloid beta Toxicity Beta Adrenergic Potentiation of the Ca2+ Channel CaV1.2 by Amyloid beta

Amyloid {beta} peptides (A{beta}) trigger Alzheimers disease (AD) but how has remained elusive. A{beta} stimulates the {beta}2 adrenergic receptor ({beta}2AR), which forms a unique signaling complex with the L-type Ca2+ channel (LTCC) CaV1.2. LTCCs have been implicated in the etiology of dementia and AD. We show that A{beta} acutely potentiates CaV1.2 via the {beta}2AR, which triggers postsynaptic recruitment of Ca2+ permeable (CP) AMPARs in hippocampal cultures and impairs LTP in hippocampal slices within minutes. The long-term consequence is a loss of postsynaptic structure of glutamatergic synapses and neurotoxicity. Disrupting this signaling cascade with highly specific tools prevented all of these effects, unifying a number of currently divergent findings on A{beta} synaptotoxicity including dysregulation of AMPARs and synaptic plasticity. TEASERAmyloid {beta} peptide is the primary pathological agent in Alzheimers disease. It affects the nanoscale structure and function of glutamatergic synapses. The molecular mechanisms are largely unknown except for identification of several binding proteins including the {beta}2 adrenergic receptor. We show that this binding potently (EC50<100 nM) augments Ca2+ influx through the L-type Ca channel CaV1.2. This effect leads to improper recruitment of Ca2+-permeable glutamate receptors to postsynaptic sites (EC50<100 nM), synaptic dysfunction and ultimately neuronal death. This work identifies an essential mechanism in amyloid {beta} neurotoxicity and explains many of the observed postsynaptic alterations. HighlightsImmediate effects of A{beta}-induced stimulation of {beta}2AR on Cav1.2: O_LIA{beta} induces phosphorylation of Cav1.2 on S1928 by PKA C_LIO_LIA{beta} augments Cav1.2 activity via {beta}2AR-induced S1928 phosphorylation within seconds C_LI A{beta}-induced {beta}2AR - Cav1.2 signaling has the following synaptotoxic effects. O_LIA{beta} induces postsynaptic accumulation of Ca-permeable AMPARs via {beta}2AR - Cav1.2 signaling within 20 min C_LIO_LIA{beta} impairs long-term potentiation (LTP) via {beta}2AR - Cav1.2 signaling C_LIO_LIA{beta} impairs postsynaptic structure and neuronal viability over 24 h C_LIO_LIPotency of A{beta} in all the above effects is very high (100 nM A{beta} is saturating!) C_LIO_LIAll effects are prevented in S1928A KI mice and acute displaces {beta}2AR from Cav1.2 with tat-Pep1923 C_LI

neuroscience↗

bioRxiv2026

Multifaceted immune resistance landscapes in human oligodendrocytes protect against cytotoxic T cells and are dysregulated in MS brain cell subsets

Multiple sclerosis (MS) is a progressive neuroinflammatory demyelinating disease of the central nervous system (CNS) that remains incurable. Autoreactive myelin-specific T cells contribute to immunopathology by directly targeting and damaging oligodendrocytes in situ. In oligodendrocytes, several immune-modulatory functions have been described that can ameliorate immune damage. However, a systematic discovery of cell-intrinsic mechanisms that protect oligodendrocytes against T cell-derived cytotoxic mechanisms has not been performed. We used human MO3.13 oligodendrocytic cells and human antigen-specific cytotoxic T cells to conduct a high-throughput (HTP) RNAi-based screen with altogether 4155 genes to identify oligodendrocyte-intrinsic immune resistance genes (IRGs). The screen revealed 133 candidate IRGs. Among them, we validated 32, which exerted a strong immuno-protective phenotype. We studied IRG expression landscapes in human brain cell subsets from postmortem brain tissues of MS and control individuals. This revealed clustered expression of IRGs in a cell-type and oligodendrocyte subset-specific manner and differential IRG expression between MS patients and controls in distinct oligodendrocyte subclusters. ChEA3 analysis revealed cell type-specific expression of transcription factors that can drive expression of respective IRGs. Explorative molecular mode of action analyses of five selected IRGs, STK11, KCNH8, ABCA2, SLC1A3 and CHRNA1 revealed that these prevented death receptor-mediated apoptosis induced by T cell-derived cytotoxic molecules. In particular, they controlled TRAIL-induced apoptosis by suppressing JNK1 activation through interfering with several upstream pathways regulating metabolic, potassium, cholesterol, glutamate and acetylcholine homeostasis. In addition, STK11, ABCA2, and CHRNA1 regulated TRAIL-R2 surface expression contributing to increased TRAIL-sensitivity whereas KCNH8 expression in oligodendrocytes inhibited secretion of inflammatory cytokines by cytotoxic T cells. Taken together, we here demonstrate the existence of multiple co-expressed IRGs in human oligodendrocytes that regulate multifaceted mechanisms of T cell resistance and are dysregulated in oligodendrocyte subsets of MS patients.

immunology↗

bioRxiv2026

Toward Computationally Complete Spatial Omics

Multimodal spatial omics has transformed biology by mapping molecular complexity within intact tissues, yet current technologies remain limited in the number of modalities measured simultaneously and often produce lower-quality data than single-modality assays. We present COSIE, a computational framework that generates high-resolution, multilayered molecular landscapes across tissue sections, individuals, and platforms. COSIE integrates histology, epigenome, transcriptome, proteome, and metabolome into a unified representation. Applied to 12 datasets spanning 10 spatial technologies, eight modalities, and nine tissue types, ranging from thousands of spots to millions of cells, COSIE outperforms existing methods. It resolves tissue structures, enhances noisy measurements, predicts unmeasured modalities, and captures dynamic processes. In human tumors, COSIE identifies invasive subregions linked to clinical outcomes and predicts spatial gene expression in TCGA samples using only histology images. By transforming fragmented data into comprehensive spatial maps, COSIE advances computationally complete spatial omics and the creation of digital tissue twins for biomedicine.

genomics↗

bioRxiv2026

Adenine Base Editing Potently Suppresses Hepatitis B Surface Antigen Expression and Inhibits Hepatitis D Virus Release

Background and AimsNovel antiviral approaches capable of permanently inactivating the intrahepatic HBV DNA reservoir, the covalently closed circular DNA (cccDNA) and HBV DNA integrated into the host genome, are urgently needed. This study evaluated adenine base editing as a strategy to disrupt HBV replication by introducing mutations in the overlapping HBs/polymerase open reading frame (ORF). MethodsAn adenine base editor (ABE) and 3 guide RNAs (gS1-gS3) were designed to introduce missense mutations within the HBs/polymerase ORF. ABE mRNA and individual gRNAs were co-transfected into HBV-infected HepG2-hNTCP cells and primary human hepatocytes. Antiviral efficacy was further assessed in HepG2.2.15 and PLC/PRF/5 cells harboring integrated HBV DNA. In vivo, lipid nanoparticles (LNP)-mediated delivery of ABE mRNA and gRNAs was evaluated in HBVcircle DNA-transduced mice and in HBV-infected human liver-chimeric mice. The impact of HBs editing on hepatitis D virus (HDV) release was assessed using PLC/PRF/5 and Huh7 cell-based HDV replication models. ResultsAdenine base editing efficiently reduced HBsAg production and HBV replication in vitro by targeting both cccDNA and integrated HBV DNA. A single LNP injection of ABE-gS2 resulted in undetectable HBsAg in HBVcircle mice, while two injections achieved a 90% reduction in serum HBsAg in HBV-infected human liver chimeric mice. HBV DNA replication was also inhibited in vivo. Furthermore, HBs ORF base editing markedly suppressed HDV release in vitro. ConclusionsAdenine base editing of the HBs ORF effectively impairs HBV replication and HBsAg production in vitro and in vivo and concomitantly inhibits HDV release, highlighting its therapeutic potential.

molecular biology↗

bioRxiv2026

StomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment

Stomata are microscopic pores that play a vital role in transpiration and gaseous exchange from leaf surfaces in plants. The stomatal density and size directly influence photosynthesis and hydrodynamics capacity. Conventional approaches for counting and determining stomatal density is labour-intensive and lack scalability. Although there are several AI-based stomata finder tools that were published in the last decade, existing models were trained on model plants like wheat, barley and Arabidopsis. Stomata in such model plants are generally elliptical, but applying a universal model to all plant species is not feasible due to their diverse morphological characteristics. Previous studies have suggested using the stomatal index to quantify the ratio between epidermal cells and total stomatal count. However, this approach can be difficult to apply consistently, as epidermal cell shape and size vary across plant species. Instead, we propose measuring stomatal density based on the number of stomata per total imaged pixel area in the captured images. In this study, a comparison between YOLOv12 and RF-DETR models were made for real-time stomata detection in normal and difficult-to-image and out-of-focus occluded images. The in-house training dataset consisted of images of 300 rice,100 barley and 50 sugarcane leaves that were captured against a dark background. YOLOv12 outperformed RF-DETR with higher mAP50:95 score. The models were trained with image augmentation for 300 epochs and YOLOv12 achieved a peak mean average precision of 98.5% and exceled at detecting stomata across abaxial and adaxial surfaces of leaves of both monocot and dicot plants. StomaQuant has also been shown to be effective for both epidermal peel and ethanol decolorised samples. Thus, StomaQuant can be used to effectively and efficiently estimate the stomatal density and size in a wide range of host plant species.

bioinformatics↗

bioRxiv2025

A transcription factor-responsive enhancer discovery platform for targeted immunotherapy

Synthetic enhancers with high specificity are crucial for therapeutic gene control. However, experimental screens and machine learning-guided design typically require context-specific datasets, limiting generalizability. Because transcription factor (TF) activity reflects cellular state, TF-responsive enhancer libraries offer a universal starting point. Here, we developed TREND (transcription factor-responsive enhancer discovery), a massively parallel reporter assay of [~]2.7 million enhancer-barcode constructs representing 57,715 designs. TREND covers 1,068 motif-annotated proteins, including 729 confirmed TFs across 49 DNA-binding domain families. Applied to ovarian cancer, TREND identified enhancers that discriminate cancer from normal epithelial cells. These enhancers enabled protein-interaction-based AND-gate circuits with reduced OFF-state leakage and amplified ON-state output, driving tumor-restricted expression of combinatorial immune effectors and robust antitumor responses in murine ovarian cancer models. TREND also identified T-cell activation-responsive enhancers with greater inducibility and lower basal activity than conventional NFAT-motif-based elements. Together, TREND provides a generalizable framework for context-specific enhancer discovery and therapeutic gene regulation.

synthetic biology↗

bioRxiv2025

Lymphotoxin-driven cancer cell eradication by tumoricidal CD8+ tumor-infiltrating lymphocytes

Tumor-infiltrating lymphocyte (TIL) therapy is FDA-approved for patients with treatment-resistant advanced melanoma, but the TIL subpopulations critical for tumor eradication remains incompletely understood. Using patient-derived TIL-melanoma co-cultures, we identified and characterized a novel subset of CD8+ TIL, capable of class I HLA-independent cancer cell lysis. The lymphotoxin {beta} receptor (LT{beta}R) and interferon (IFN) sensing pathways were nominated as key determinants of TIL-mediated cancer cell killing from a whole-genome, loss-of-function CRISPR screen. Validation studies confirmed that dual LT{beta}R and IFN sensing is necessary and sufficient for cancer cell lysis, and that expanded CD8+ TIL express high lymphotoxin {beta} (LTB) and upregulate lymphotoxin (LTA) upon coculture with cancer cells. Leveraging paired scRNA-seq and scTCR-seq data, we confirmed that enrichment of LTB+CD8+ T cells is associated with clinical response to TIL, and that LTB+CD8+ TIL are expanded from putative neoantigen-reactive, LTBlo CD8+ T cells in resected tumors. SignificanceWe have uncovered a previously unrecognized mechanism of TIL-mediated tumor eradication, providing mechanistic insights into the role of LTBR/IFN signaling in TIL-mediated cancer cell killing, and potentially offering insights into novel strategies to isolate, enrich, and expand tumoricidal TIL or augment specific TIL functions to enhance tumor control.

cancer biology↗

bioRxiv2025

Integrating Longitudinal Metabolite Profiles Improves Trait Prediction in Pigs in a Trait- and Timepoint-Dependent Manner

BackgroundAccurate prediction of genetic merit is essential for accelerating genetic improvement in pigs, particularly for traits that are costly or difficult to measure directly. This study investigated the potential of integrating individual-level blood serum metabolite profiles sampled at two developmental stages (10-week and 20-week) into genomic prediction models for five economically important traits: average daily feed intake (DFI), feed conversion ratio (FCR), backfat thickness (BF), test daily gain (TDG), and loin depth (LD). Using a BayesC modeling framework, we analyzed 1,637 pigs from a single purebred population with complete phenotype, genotype, and metabolite-profile data. We evaluated seven models, including a genotype-only model (G), metabolite-only models using metabolite-profile data from 10-week (M1), 20-week (M2), or both timepoints (M1+M2), and combined models integrating genotypes with metabolite-profile data (G+M1, G+M2, G+M1+M2). ResultsHeritability estimates were generally low to moderate, ranging from 0.07 to 0.30 for the 10-week metabolite profile and from 0.04 to 0.28 for the 20-week metabolite profile. Prediction accuracy of phenotype consistently improved when metabolite-profile data were integrated into genotype-based models, although the magnitude of improvement varied depending on the trait and the timepoint of metabolite sampling. Prediction accuracy increased from 0.31 (G) to 0.41 (G+M2; G+M1+M2) for DFI, and from 0.27 (G) to 0.33 (G+M2; G+M1+M2) for FCR. The latter two models also delivered the largest gains over G for BF (from 0.41 to 0.45) and TDG (from 0.28 to 0.32). However, LD benefited the most when both 10-week and 20-week metabolite profiles were combined (G+M1+M2: 0.45 compared to G: 0.42). ConclusionsAcross all traits, models combining genotype data with metabolite profiles from one or multiple timepoints achieved the highest or equally high prediction accuracies compared to the genotype-only model, reflecting complementary biological insights captured by metabolite profiles. These findings highlight the potential value of metabolite-profile data as an intermediate omics layer to enhance genomic prediction, particularly when integration strategies are tailored to trait-specific biology and sampling timepoints.

genetics↗

bioRxiv2025

deCYPher: Star Allele-Resolution Computational Framework of Pharmacogenes for Haplotype-Resolved Long-Read Assemblies

Although existing next-generation sequencing (NGS) tools, such as Aldy and Cyrius, have been applied for allele typing, they cannot achieve complete accuracy due to various genomic challenges including pseudogenes, structural variations, hybrid genes, copy number variations, and gene deletions. These complexities make accurate pharmacogene interpretation more challenging, despite the crucial role pharmacogenomics plays in precision medicine. We developed deCYPher, a tool that generates personalized pharmacogenomic reports from haplotype-resolved assemblies. The tool enables analysis of all PharmVar 1A level genes, such as CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5, CYP4F2, DPYD, NUDT15, and SLCO1B1. Applied to all HPRC haplotypes (including both release 1 and release 2 data), deCYPher demonstrated high accuracy in resolving complex gene structures. In the case of CYP2D6, release 1 identified 6% gene multiplications, 6% full gene deletions, and 4% CYP2D6/CYP2D7 hybrids. By contrast, release 2 demonstrated an increased prevalence of multiplications (14%) and hybrids (11%), while the frequency of full gene deletions remained comparable at 5%. Comparison with pb-StarPhase revealed discrepancies in 12 of 94 assemblies in the release 1 dataset. For instance, in sample HG02257, Aldy, Cyrius, and deCYPher consistently identified the genotype as *2/*35, whereas pb-StarPhase reported *2/*2. Notably, the *35-defining variants were present in the BAM and VCF files in the pb-StarPhase pipeline, but the local read depth over the *35-specific region was only 5x in HG02257-p, suggesting that the misclassification likely resulted from insufficient coverage - a known limitation of pb-StarPhase under low-depth conditions.

bioinformatics↗

bioRxiv2025

gAIRR-wgs: An Algorithm to Genotype T Cell Receptor Alleles Using Whole Genome Sequencing Data

T cell receptor (TR) genes, including variable (TR_V), diversity (TR_D), and joining (TR_J) segments, exhibit allelic diversity that is critical to adaptive immunity. Growing evidence has identified associations between TR genes and immune-related diseases. Germline variants may influence TR gene function and subsequent usage, highlighting the importance of accurate TR allele profiling. However, accurately identifying germline TR from standard WGS data remains challenging due to short read lengths, limited depth, and high sequence similarity. To address these challenges, we developed gAIRR-wgs, for WGS-based TR allele typing. By incorporating novel alleles from HPRC individuals, gAIRR-wgs exhibited excellent performance in allele calling, with F1 scores of 100.0% for TR_D, 99.8% for TR_J, and 98.3% for TR_V. Applying this pipeline to 1,492 individuals from the Taiwan Biobank (TWB), we identified 449 novel TR alleles, 277 of which overlapped with HPRC release 1 data of mixed ethnicity and are absent in the IMGT database. Further population comparison analysis revealed significant TR allele distribution differences across global populations, showing population-specific patterns and diversity variations between ethnic groups. We also discovered TWB-specific deletion polymorphisms affecting contiguous TRGV and TRBV genes, which are not recorded in the gnomAD database and undetected by standard structural variant callers, highlighting the need for tailored approaches to resolve complex immune gene regions. In conclusion, gAIRR-wgs enables accurate TR allele calling from standard WGS data using feasible computational resources and reveals substantial immunogenetic diversity in population cohorts.

bioinformatics↗

bioRxiv2025

Environmental DNA Metabarcoding Effectively Detects Invasive Species, Pests, and Community Changes in Taiwan Rice Fields

Rice fields represent man-made semi-aquatic wetlands primed for invasive pests. Monitoring rice field biodiversity using conventional methods, however, is time-consuming and laborious. Environmental DNA (eDNA) methods can provide a fast and effective means to monitor rice field communities and inform management decisions. Our study provides proof-of-concept of rice field eDNA biodiversity assessments, with a focus on native and non-native pests across cultivation phases. We collected eDNA samples from locations in southern Taiwan during planting and harvesting, employing eDNA metabarcoding (COI) to detect diverse taxonomic groups. We assigned 77 ASVs across all sites to animal taxa, 34 of which were identified to species. Overall, 18 species were designated as native or non-native (83.3% and 16.6%, respectively), including three major rice pests, Chilo suppressalis (native), Coptotermes formosanus (native), and Pomacea canaliculata (non-native). Cultivation status affected overall diversity, with higher species richness during planting compared to harvesting. No significant differences were observed between native and non-native taxa between cultivation phases. Altogether, we detected a complex environment across trophic levels comprised of both native and non-native agricultural pests using limited sampling effort, demonstrating eDNA analysis as an efficient biomonitoring approach in rice agroecosystems with direct applications for pest, invasive species, and vector surveillance within Taiwan.

ecology↗

bioRxiv2025

TypeAssembly: Copy number estimation and allele typing for haplotype assemblies

Accurately annotating complex genes in the human genome, particularly from haplotype assemblies, remains a significant challenge. To overcome this, we developed TypeAssembly, a local alignment-based framework for copy number estimation and allele typing. Operating in two modes, mode-FASTA and mode-VCF, TypeAssembly can define alleles by either sequence or variant information. We successfully applied it to annotate 41 genes in the MHC locus, 17 KIR genes, and, for the first time, 15 pharmacogenes across 466 haplotype assemblies. This study establishes TypeAssembly as a robust method for accurately annotating complex genomic regions and provides an evaluation of existing gene annotations and callers.

bioinformatics↗