Biology subjects

Zou, F.

Publications and source records attributed to Zou, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Testing and Estimating Causal Treatment Effect Heterogeneity in Observational Studies via Revised Deep Semiparametric Regression: A Lung Transplant Case Study

Lung transplantation programs must decide when bilateral lung transplantation (BLT) offers meaningful functional benefit over single lung transplantation (SLT). Because donor and recipient characteristics jointly shape outcomes, the BLT-SLT contrast may differ across patients. However, analyzing observational registries poses a statistical challenge: apparent subgroup differences can be artifacts of complex confounding, while true heterogeneity can be missed or poorly quantified. Using a large national registry, we investigate whether the BLT effect varies across recipients and identify clinically relevant profiles of benefit using post-transplant lung function measured by forced expiratory volume in 1 second (FEV1). We develop deepHTL, a framework that tests for treatment effect heterogeneity and estimates how the BLT-SLT effect varies with patient features. In extensive simulations designed to resemble registry-like confounding, deepHTL controls false positives for detecting heterogeneity and yields more accurate individualized effect estimates than common machine learning methods. In the lung transplant cohort, we find strong evidence of heterogeneity in the BLT-SLT effect on FEV1: younger, lower risk recipients with better baseline status show the largest FEV1 gains from BLT, whereas older, higher risk candidates exhibit diminished marginal benefit. These findings provide statistically grounded guidance for patient selection and allocation of scarce donor organs.

bioinformatics↗

bioRxiv2026

Condition-matched in silico prediction of drug transcriptional responses enables mechanism-guided screening and combination discovery

Perturbational transcriptomics links therapeutic compounds to cellular mechanisms and provides a powerful framework for drug discovery, but experimentally profiling transcriptional responses across diverse cell states, doses and durations is costly and often infeasible. Here we present DEPICT (Drug rEsponse Prediction in transCriptomics with Transformers), a deep learning framework that predicts condition-matched drug-induced transcriptional responses from baseline gene expression, perturbation settings and complementary drug representations. Using the LINCS L1000 dataset, DEPICT generalized to unseen drugs and cell types and outperformed five baseline strategies and two recent deep learning models. In the most challenging unseen-cell evaluation, DEPICT was the only model to surpass all baselines, improving differential-expression prediction accuracy and reducing perturbed-expression prediction error by 30.3% and 36.8%, respectively, relative to the next-best deep model. In a non-small cell lung cancer (NSCLC) case study, DEPICT-enabled virtual screening prioritized compounds predicted to reverse disease-associated transcriptional signatures. Notably, 13 of the top 20 prioritized compounds had either previously entered NSCLC-related clinical trials or been validated in NSCLC studies, supporting the translational relevance of the predicted perturbational profiles. DEPICT further enabled condition-matched drug synergy prediction and mechanistic exploration when experimentally matched profiles were unavailable. Together, these results show that accurate, condition-matched in silico perturbation profiling can scale transcriptomics-driven hypothesis generation for drug repurposing and combination discovery.

bioinformatics↗

bioRxiv2026

The not-so-great speciator: Systematics and species limits in a rapid radiation, the Asiatic white-eye complex (Zosterops spp.)

Abstract/SummaryHere we untangle the systematics of the Asiatic white-eye complex (Zosterops spp.) to better understand the early stages of a recent island radiation. We adopt an integrative approach involving allelic data, genome-scale single nucleotide polymorphisms (SNPs), and museum-based morphometrics coupled with a comprehensive sampling to provide the most holistic understanding of the group to date. The island lineages of Asiatic white-eyes across Indonesia, the Philippines, East Asia, the adjacent oceanic islands of the Western Pacific underwent a deep split separating Zosterops everetti and Z. nigrorum in the Phillippines from a very rapid radiation including Z. japonicus, Z. meyeni, and Z. montanus in the Philippines, Japan, and Indonesia. Z. nigrorum catarmanensis on Camiguin South in the Philippines was found to be nested within Z. montanus and a species limit between Z. nigrorum populations on Panay and Luzon was strongly supported. Phylogenetic splits and population structure were detected within the clade containing Z. japonicus, Z. meyeni, and Z. montanus. A well-supported split separates a northern group including Northern Philippines Z. montanus subspecies, Z. meyeni, and Z. japonicus from the southerly Z. montanus taxa. This creates a paraphyletic Z. montanus. However, based on speciation rates within the broader Asiatic white-eye clade this break likely does not yet represent evolutionarily independent species lineages. Morphological evolution is taking place within the Asiatic white-eyes especially within the robust, large-billed subspecies of Z. japonicus on the oceanic islands of Japan and in the newly identified yellow-morph of Z. montanus on Camiguin South.

evolutionary biology↗

bioRxiv2025

Genetic diversity of Collaborative Cross mice enables the establishment of a novel Chlamydia muridarum female genital tract infection model

Chlamydial infection in women displays wide variation in bacterial burden, persistence, and risk of upper genital tract pathology, yet the host genetic factors underlying this heterogeneity remain poorly defined. We evaluated genital tract infection with Chlamydia muridarum across 20 Collaborative Cross (CC) strains, a recombinant inbred mouse panel that captures broad genetic diversity with high within-strain reproducibility. CC strains exhibited striking differences in early bacterial burden, time to clearance, and oviduct pathology, including prolonged low-inflammatory infections and burden-pathology discordance that mirror key features of human disease. Heritability analyses demonstrated that host genetics accounted for most of the variation observed in Day 7 bacterial burden and pathological outcomes. Genome-wide scans identified suggestive quantitative trait loci associated with both traits. Genes within the burden-associated locus converged on host pathways implicated in chlamydial intracellular growth, including membrane dynamics and lipid metabolism, ubiquitin signaling, host cell survival mechanisms, and immune regulatory signaling. In contrast, genes within the pathology-associated locus were enriched for pathways regulating inflammatory cell recruitment, inflammasome activation, and tissue remodeling, processes central to genital tract damage following infection. Cervical transcriptional profiling further revealed strain-dependent innate and adaptive immune programs associated with bacterial burden and disease phenotype. Together, these findings establish the CC as a powerful platform for dissecting the genetic architecture of chlamydial immunopathogenesis, and for improving preclinical evaluation of vaccines and therapeutics.

immunology↗

bioRxiv2025

GPTAnno: Ontology-tree-guided hierarchical cell type annotation based on GPT models for single-cell data

Cell type annotation is critical for interpreting single-cell transcriptomic data but remains challenging due to uncertain cellular clustering granularity and inconsistent labeling across studies. Here we present GPTAnno, an automated, ontology-tree-guided, uncertainty-aware, hierarchical cell type annotation method based on GPT models. GPTAnno directly handles gene expression matrices, integrates multi-resolution clustering with large language model reasoning constrained by the cell ontology to produce standardized, ontology-aware, and reproducible annotations with automatic resolution selection. GPTAnno selects optimal clustering resolutions based on the annotation distance on the ontology tree and quantifies annotation uncertainty to flag ambiguous clusters for expert review. Benchmarking across twelve large-scale datasets demonstrates GPTAnnos superior accuracy on annotating cell types across various species, tissues, and disease contexts against existing methods. Implemented in R and Python with Seurat and Scanpy compatibility, GPTAnno allows simple inputs to streamline the reproducible annotation, considerably reducing human efforts in repeated reclustering, assigning and examining the labels.

genomics↗

bioRxiv2025

An Interpretable Deep Learning Framework for Biomarker Discovery in Complex Disease Survival Outcomes

Identification of important biomarkers associated with complex disease survival outcomes is fundamental for gaining an in-depth understanding of disease mechanisms and advancing precision medicine in conditions such as cancer and cardiovascular disorders. However, these tasks are complicated by the unique nature of time-to-event data, which captures both the occurrence and timing of clinical events. Notably, complex associations such as the non-linear and non-additive biomarker interactions and the high-dimensionality challenge conventional survival data modeling approaches. To address these difficulties, we propose SurvDNN, an enhanced deep neural network framework specifically designed for survival outcomes modeling. SurvDNN incorporates a bootstrapping-based regularization strategy to mitigate overfitting and a novel stability-driven filtering algorithm to improve model robustness. To enable interpretable biomarker discovery, we extend the Permutation-based Feature Importance Test (PermFIT) to survival settings, allowing rigorous quantification of individual biomarker contributions under complex biomarker-outcome associations. Through extensive simulations and applications to real-world datasets, SurvDNN consistently outperforms existing machine learning approaches in both biomarker identification and predictive accuracy. Our results demonstrate the potential of SurvDNN coupled with PermFIT as an interpretable, robust, and powerful tool for biomarker-driven survival modeling in complex diseases. An open-source R package implementing SurvDNN is publicly available on GitHub (https://github.com/BZou-lab/SurvDNN).

bioinformatics↗

bioRxiv2025

Hippocampal-guided reconstruction of an event's prior temporal context

Leading theories of episodic memory argue that when events from the past are remembered, temporally-adjacent events are also reinstated ( temporal context reinstatement). However, direct evidence for temporal context reinstatement is surprisingly limited. Here, we tested for temporal context reinstatement in a human fMRI continuous recognition memory experiment in which natural scene images were repeatedly encountered. For the original encounter with each scene, we defined its temporal context as the visual content of temporally-adjacent scenes. Using voxelwise encoding models, we tested whether fMRI activity patterns evoked when a scene was re-encountered carried information about the original encounters temporal context. Indeed, we found robust temporal context reinstatement within high-level visual cortex (lateral occipitotemporal cortex; LOTC), despite the fact that reinstated content was entirely incidental to task demands. However, temporal context reinstatement only occurred when stimuli were successfully recognized, indicating that reinstatement was behaviorally-relevant, even if incidental. Finally, the strength of temporal context reinstatement in LOTC was predicted by the similarity of hippocampal activity patterns across encounters, demonstrating distinct, but complimentary, roles for the hippocampus and neocortex in reinstating temporal context information.

neuroscience↗

bioRxiv2025

MITScan: Score-Based Genome-Wide Association Analysis of the Microbiome and Host Transcriptome

Microbiome research explores how microbial communities interact with the human body to influence health and well-being, offering insights for advancing personalized medicine and targeted treatments. Microbiome profiles can be measured, for example, by 16S rRNA sequencing, where sequencing reads are clustered into genus-or species-level operational taxonomic units (OTUs), yielding count-based abundance data. In addition, these data are often characterized by sparsity with extra zeros. Emerging evidence suggests that imbalances in microbial composition, potentially regulated by host gene expression, have been associated with various diseases. Investigating the associations between the microbiome and host transcriptome can help uncover the mechanisms underlying human health and disease. Current existing approaches for evaluating their associations largely rely on Pearson or Spearman correlations, ignoring the fact that microbiome data are sparse count data, leading to potentially biased results. To overcome this issue, we present MITScan, a genome-wide MI crobiome-host T ranscriptome Score-based association an alysis, employing zero-inflated negative binomial models to accommodate extra zeros commonly observed in microbiome data. To address the large number of paired association tests arising from two high-dimensional omics datasets, we utilize score tests and matrix calculations for computational efficiency. We further apply an empirical permutation method with genomic control to effectively control the family-wise error rate in studies with small sample sizes. With real datasets, we demonstrate that MITScan achieves computational gains of three orders of magnitude compared to commonly used Wald or t tests, while identifying similar significant OTU-gene pairs. The MITScan R package is accessible on GitHub at https://github.com/yajing-hao/MITScan.

genomics↗

bioRxiv2025

Unbiased genetics identifies a glutamatergic signaling network as a mediator of daily sleep patterns.

Sleep is a fundamental, conserved behavior important for survival. In many species, sleep behavior is controlled by poorly understood interactions between a system of circadian rhythms (CR), which promote sleep at ecologically appropriate times, and a homeostatic sleep drive that accumulates with time awake. The CR is a cellular phenomenon, driven by molecular oscillations of "clock genes" in nearly all cells. Emerging evidence indicates neuronal synapses are a key locus for the accumulation and resolution of sleep need, supporting a cellular basis of sleep need. Indeed, efforts to understand the genetic basis of sleep need identified Homer1a, a regulator of synapse homeostasis. To develop further insight into the genetic basis of sleep regulation, we measured daily sleep patterns in genetically diverse strains of mice from the Collaborative Cross. Strains with 1) highly consolidated light-phase sleep, or 2) fragmented, arrhythmic sleep, were identified for genetic analysis using quantitative trait loci (QTL) mapping. Excitingly, in F2 hybrids, 19 of 32 metrics of sleep and circadian behavior mapped to a narrow QTL containing GRM5, a postsynaptic glutamate receptor and binding partner of Homer1a, and GCPII, an astrocytic enzyme that regulates NAAG, a peptide agonist for the presynaptic/astrocytic glutamate receptor GRM3. Collectively, these genes form a coordinated glutamatergic signaling network across the tri-partite synapse. Pharmacology targeting GRM5, GCPII, and GRM3 strongly modulated sleep, functionally validating them as sleep-regulating genes. Our findings support a model in which synapses act as a cellular site for integration of circadian and sleep-need signals to regulate daily sleep patterns. Significance StatementSleep is a fundamental pillar of human health. Restorative sleep is supported by circadian rhythms (CR). Sleep and CR disruptions are prevalent in neuropsychiatric conditions and are increasingly understood as causal drivers of altered brain function and behavior. Therefore, sleep and CR are attractive therapeutic targets to improve health. However, the basic biological mechanisms that couple our need to sleep with the CR are not understood. Genetic discovery using diverse strains of mice identifies a glutamatergic signaling network coordinated across the synapse as an underlying genetic basis for daily sleep patterns. This work builds upon a growing body showing that excitatory synapses are a cellular site for the resolution of sleep need and interaction with the CR.

neuroscience↗

bioRxiv2025

Condensin Accelerates Long-Range Intra-Chromosomal Interactions

The 3D genome organization plays a key role in regulating interactions among chromosomal loci. While Chromosome Conformation Capture (3C)-based methods have provided static snapshots of chromatin architecture, the kinetics of chromosomal encounters in live cells remain poorly characterized. In this study, we employ Chemically Induced Chromosomal Interaction (CICI) to measure encounter times between multiple loci pairs in G1-arrested budding yeast. Our results show that chromosome motion closely follows the Rouse polymer model, with similar diffusion parameters at all tested loci. Surprisingly, we find that long-range intra-chromosomal encounters occur significantly faster than inter-chromosomal encounters at similar 3D distances. Using targeted depletion experiments, we identify condensin, but not cohesin, as the complex responsible for these rapid intra-chromosomal interactions. This is further supported by Hi-C analysis, which reveals that condensin promotes long-distance intra-chromosomal interactions in G1 yeast. Through polymer simulations, we estimate that condensin extrudes chromatin at [~]2 kb/s with a density of one complex per 1-2 Mb and a processivity of 120-220 kb. These findings uncover a novel role for condensin in shaping the interphase genome organization and provide new insights into chromosomal search dynamics in vivo.

molecular biology↗

bioRxiv2025

PETScan: Score-Based Genome-Wide Association Analysis of RNA-Seq and ATAC-Seq Data

High-dimensional sequencing data, such as RNA-Seq for gene expression and ATAC-Seq for chromatin accessibility, are widely used in studying systems biology. Accessible chromatin allows transcription factors and regulatory elements to bind to DNA, thereby regulating transcription through the activation or repression of target genes. The association analysis of RNA-Seq and ATAC-Seq data provides insights into gene regulatory mechanisms. Most existing analytic tools exclusively focus on cis-associations, despite regulatory elements being able to physically interact with distant target genes. Furthermore, conventional approaches often utilize Pearson or Spearman correlations, which ignore the count-based nature of RNA-Seq data. To address these limitations, we introduce PETScan, a computationally efficient genome-wide PEak-Transcript Score-based association analysis, utilizing negative binomial models to better accommodate RNA-Seq data. We leverage score tests and matrix calculations for improved computational efficiency, and combine an empirical permutation method with genomic control to ensure valid p-value calculations in studies with limited sample sizes. In real-world datasets, PETScan achieved three orders of magnitude faster than Wald tests, while identifying similar significant gene-peak pairs. The PETScan R package is available on GitHub at https://github.com/yajing-hao/PETScan.

genomics↗

bioRxiv2024

Hi-C Calibration by Chemically Induced Chromosomal Interactions

The genome-wide chromosome conformation capture method, Hi-C, has greatly advanced our understanding of genome organization. However, its quantitative properties, including sensitivity, bias, and linearity, remain challenging to assess. Measuring these properties in vivo is difficult due to the heterogenous and dynamic nature of chromosomal interactions. Here, using Chemically Induced Chromosomal Interaction (CICI) method, we create stable intra- and inter-chromosomal interactions in G1-phase budding yeast across a broad range of contact frequencies. Hi-C analysis of these engineered cell populations demonstrates that static intra-chromosomal loops do not generate Topologically Associated Domains (TADs) and only promote 3D proximity within [~]50kb flanking regions. At moderate sequencing depth, Hi-C is sensitive enough to detect interactions occurring in 5-10% of cells. It also shows no inherent bias toward intra-versus inter-chromosomal interactions. Furthermore, we observe a linear relationship between Hi-C signal intensity and contact frequency. These findings illuminate the intrinsic properties of the Hi-C assay and provide a robust framework for its calibration.

genomics↗

bioRxiv2024

High-dimensional Biomarker Identification for Scalable and Interpretable Disease Prediction via Machine Learning Models

Omics data generated from high-throughput technologies and clinical features jointly impact many complex human diseases. Identifying key biomarkers and clinical risk factors is essential for understanding disease mechanisms and advancing early disease diagnosis and precision medicine. However, the high-dimensionality and intricate associations between disease outcomes and omics profiles present significant analytical challenges. To address these, we propose an ensemble data-driven biomarker identification tool, Hybrid Feature Screening (HFS), to construct a candidate feature set for downstream advanced machine learning models. The pre-screened candidate features from HFS are further refined using a computationally efficient permutation-based feature importance test, forming the comprehensive High-dimensional Feature Importance Test (HiFIT) framework. Through extensive numerical simulations and real-world applications, we demonstrate HiFITs superior performance in both outcome prediction and feature importance identification. An R package implementing HiFIT is available on GitHub (https://github.com/BZou-lab/HiFIT).

bioinformatics↗

bioRxiv2024

Temporal High-Resolution Atlas of Human Blood Leukocyte Composition in Response to Respiratory Virus Inoculation

BackgroundRespiratory viral infections produce highly variable clinical outcomes, even after controlled exposure. Human viral challenge studies are well suited to study this variation because they capture immune profiles before inoculation and across defined post-challenge intervals. However, how circulating immune-cell populations change over time, and which responses are shared or virus-specific across respiratory viruses, remains incompletely defined. MethodsWe leveraged HR-VILAGE-3K3M, a curated human respiratory viral challenge transcriptomic resource, to analyze longitudinal whole-blood profiles from H1N1, H3N2, RSV, and HRV challenge studies. Using statistical deconvolution and xCell-based digital sorting, we estimated changes in major leukocyte populations and 28 immune-cell subtypes in symptomatic infected (Sx/Inf) and asymptomatic uninfected (Asx/Uninf) participants. Across viruses, symptomatic infection was marked by a broad shared pattern of innate immune activation together with reduced circulating lymphocyte enrichment. Activated dendritic cells, monocytes, and neutrophils increased following challenge, whereas strong pDC activation was observed primarily in influenza studies. Most T-cell and B-cell populations decreased after challenge. Plasma-cell enrichment increased progressively over time. Despite this shared architecture, response timing differed by virus: H3N2 showed the earliest changes, followed by H1N1, whereas RSV responses were delayed and HRV showed more distinct immune-cell trajectories. Migration-associated transcriptional programs were detected across both innate and adaptive immune-cell populations, supporting coordinated immune-cell trafficking during infection. Baseline immune composition also differed between outcome groups, suggesting that pre-existing immune-state differences may influence infection outcomes. ConclusionsThese findings define a cellular atlas of human respiratory viral challenge responses and show that respiratory viruses induce a broadly conserved immune-response program with distinct virus-specific kinetics. This framework may help clarify immune features associated with natural resistance, symptomatic infection, and vaccine-relevant antiviral immunity.

genomics↗

bioRxiv2024

Evaluating Cross-Platform Normalization Methods for Integrated Microarray and RNA-seq Data Analysis

Integrated analysis of gene expression data across studies is important for understanding complex traits, but combining microarray and RNA-seq data remains challenging because of substantial platform-related technical differences. In this study, we evaluated ten commonly used batch-effect correction methods for cross-platform integration and classified them into three groups representing a spectrum from least to most data processing: unsupervised sample-wise methods, unsupervised gene-wise methods, and supervised methods. Unlike the first two groups, supervised methods use outcome information to guide correction. Performance was assessed for distribution alignment, clustering, outcome prediction, and differential expression (DE) analysis using three paired real microarray-RNA-seq datasets and simulation studies. Supervised methods achieved strong distribution alignment and clustering by biological group, but they introduced information leakage and inflated Type I error, making them unsuitable for unbiased discovery. Among unsupervised methods, sample-wise approaches were often conservative under balanced settings. They showed inflated Type I error under imbalanced settings. In contrast, gene-wise methods generally maintained appropriate Type I error control and achieved higher power, with limma showing the strongest DE performance. Meta-analysis that combines p-values also controlled Type I error well and provided competitive power. For outcome prediction, both sample-wise QN and gene-wise limma performed well, with limma providing the strongest overall performance and QN offering the practical advantage that new samples can be normalized to an existing reference distribution without refitting. Overall, limma is recommended as the best general-purpose method for cross-platform integration.

genetics↗

bioRxiv2024

A curated collection of transcriptome datasets to study the transcriptional response in blood and nasal samples following viral respiratory inoculation and vaccination

Our understanding of the human immune systems response to viral respiratory tract infections (VRTIs) and vaccines, including the molecular mechanisms and correlates of protection, remains incomplete. Extensive transcriptomic data from inoculation and vaccination studies have been deposited in publicly available databases. However, these studies are often separate and difficult to locate. We provide a curated compendium of public gene expression data repositories for researchers to reanalyze transcriptomes from human whole blood, peripheral blood mononuclear cells (PBMCs), and nasal swab samples. This enables the study of transcriptional responses to viral inoculation or vaccination. This collection includes 18 datasets from inoculation studies and 37 datasets from vaccination studies, sourced from the NCBI Gene Expression Omnibus (GEO), ImmPort Shared Data, and ArrayExpress.

genomics↗

bioRxiv2024

Analysis of hepatic lentiviral vector transduction; implications on preclinical studies and clinical gene therapy protocols.

Lentiviral vector-transduced T-cells were approved by the FDA as gene therapy anti-cancer medications. Little is known about the host genetic variation effects on the safety and efficacy of the lentiviral vector gene delivery system. To narrow this knowledge-gap, we characterized hepatic gene delivery by lentiviral vectors across the Collaborative Cross (CC) mouse genetic reference population. For 24 weeks, we periodically measured hepatic luciferase expression from lentiviral vectors in 41 CC mouse strains. Hepatic and splenic vector copy numbers were determined. We report that CC mouse strains showed highly diverse outcomes following lentiviral gene delivery. For the first time, moderate correlation between mouse strain-specific sleeping patterns and transduction efficiency was observed. We associated two quantitative trait loci (QTLs) with intra-strain variations in transduction phenotypes, which mechanistically relates to the phenomenon of metastable epialleles. An additional QTL was associated with the kinetics of hepatic transgene expression. Genes comprised in the above QTLs are potential targets to personalize gene therapy protocols. Importantly, we identified two mouse strains that open new directions in characterizing continuous viral vector silencing and HIV latency. Our findings suggest that wide-range patient-specific outcomes of viral vector-based gene therapy should be expected. Thus, novel escalating dose-based clinical protocols should be considered.

genetics↗

bioRxiv2024

Benefits of spaced learning are predicted by re-encoding of past experience in ventromedial prefrontal cortex

More than a century of research shows that spaced learning improves long-term memory. Yet, there remains debate concerning why. A major limitation to resolving theoretical debates is the lack of evidence for how neural representations change as a function of spacing. Here, leveraging a massive-scale 7T human fMRI dataset, we tracked neural representations and behavioral expressions of memory as participants viewed thousands of natural scene images that repeated at lags ranging from seconds to many months. We show that spaced learning increases the similarity of human ventromedial prefrontal cortex representations across stimulus encounters and, critically, these increases parallel and predict the behavioral benefits of spacing. Additionally, we show that these spacing benefits critically depend on remembering and, in turn, re-encoding past experience. Collectively, our findings provide fundamental insight into how spaced learning influences neural representations and why spacing is beneficial.

neuroscience↗