Biology subjects

Mao, J.

Publications and source records attributed to Mao, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Establishment of Rabbit Formative-like Pluripotent Cells by Bidirectional Modulation of WNT Signaling Pathway

The generation of embryonic stem cells (ESCs) with full developmental capacity and robust chimera-forming potential remains a major bottleneck for most non-rodent mammalian species including rabbits, an important animal both for biomedicine and agriculture. Formative pluripotency is a unique intermediate state bridging the naive and primed pluripotent states, which is defined by distinct molecular signatures, intrinsic competence for primordial germ cell (PGC) specification, and unique developmental adaptability to early embryonic microenvironments. Here, we established a novel culture medium termed KFiC, which could bidirectionally and coordinately modulate the WNT/{beta}-catenin signaling pathway via combined treatment of WNT activator CHIR99021 and inhibitor IWR-1. The KFiC culture system enables efficient derivation and stable long-term self-renewal of rbESCs with consistent formative-like pluripotent characteristics. KFiC-cultured rbESCs (KFiC-rbESCs) form compact, undifferentiated colonies, maintain intact genomic stability, and recapitulate the transcriptional landscape of pre-implantation epiblast cells, accompanied by sustained high expression of the core formative pluripotency marker OTX2. Functionally, KFiC-rbESCs possess efficient PGC differentiation competence and extensively contribute to embryonic development after microinjection into rabbit morulae, displaying their superior developmental plasticity. Notably, the KFiC system can stabilize an intermediate formative-like pluripotent state in mouse, human, and porcine pluripotent stem cells, and KFiC-adapted mouse ESCs retain potent chimera-forming ability. Collectively, this study establishes a reliable culture platform for generating and maintaining formative-like rbESCs. This cross-species compatible system provides a promising foundation for further optimizing naive-level pluripotency in rbESCs and facilitates the generation of genetically engineered rabbit disease models.

developmental biology↗

bioRxiv2026

Method Choice, Not Biology, Determines In Silico Perturbation Results: A Systematic Evaluation of Eight Methods Across Four Datasets

Most in silico perturbation methods for single-cell transcriptomics have been validated only on individual datasets, leaving their reliability and generalizability unknown. Through systematic cross-method, cross-dataset benchmarking of eight methods spanning six mathematical frameworks across four datasets, we find that six of eight methods--including widely used VAE-based and tensor decomposition approaches--fail to produce detectable transcription factor (TF)-to-pathway signals. Only CellOracle and DDIM consistently detected TF-to-glycolysis directional regulation. Cross-pathway analysis in PBMC monocytes revealed biologically coherent TF-pathway associations beyond glycolysis (SPI1[->]glycolysis 4.4x enrichment, FOS[->]AP-1 targets 4.4x), with SOX9 serving as a biological specificity control (no pathway enrichment). Method choice alone could reverse biological conclusions: DDIM and scTenifoldKnk rankings were significantly anti-correlated ({rho}=-0.811, p=0.027). CRISPRi Perturb-seq validation in K562 cells confirmed TF knockdown suppresses glycolysis gene expression (JUN {delta}=-1.72, CEBPB {delta}=-1.59, SPI1 {delta}=-1.57, FOS {delta}=-0.70), but CellOracle-predicted perturbation directions did not match experimental directions (40.9% agreement, not different from chance), revealing a fundamental gap between steady-state correlation and causal perturbation. Diagnostic analyses using VAE latent space profiling, correlation distribution comparison, and gene-gene graph analysis identified distinct failure modes in unsuccessful methods: VAE latent space competition (STAT3 signal-to-noise 0.44 vs. SPI1 4.25), correlation noise (TF-glycolysis |r|=0.038 indistinguishable from background |r|=0.047), and graph non-specificity (0.84x enrichment). A controlled ablation experiment showed that adding a GRN prior to DDIM did not improve target recall (delta=0 for all TFs), confirming that performance differences are multi-factorial. These findings establish preliminary guidance for method selection, including cross-pathway validation, direction-aware benchmarking, and minimum data requirements ([≥]500 cells, [≥]1,000 HVGs).

bioinformatics↗

bioRxiv2026

Method for modeling oviduct function and impact on embryonic development

Direct experimental analysis of the mammalian oviduct is constrained by limited tissue access and the short lifespan of ex vivo preparations. Extracellular matrix-embedded three-dimensional epithelial organoids provide longer-term in vitro models. However, their inward-facing apical surface and the absence of supporting stromal cells limit physiological studies of the oviduct, including ciliary activity and maternal-embryonic interactions. Here, we provide a step-wise protocol detailing the generation of mouse and human oviductal assembloids in which epithelial cells form an outward-facing (apical-out) layer around a stromal core. Epithelial and stromal cells from adult mouse oviducts or human Fallopian tubes are isolated, expanded separately, and subsequently aggregated in a rotational culture system. The protocol also outlines morphological and immunostaining criteria for confirming cellular organization, whole-mount detection of external cilia, measurement of ciliary beat frequency, and co-culture of mouse assembloids with preimplantation embryos. Mouse and human assembloids retained epithelial and stromal identity and displayed cilia at the accessible outer surface. In a proof-of-concept experiment, embryos co-cultured with the assembloids developed to blastocysts at a rate similar to that of in vivo-derived blastocysts. This reductionist system provides a straightforward and tractable model to investigate oviduct physiology and embryo-maternal communication while allowing direct manipulation and observation of the epithelial interface. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/743297v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1acb462org.highwire.dtl.DTLVardef@17695f8org.highwire.dtl.DTLVardef@6fd6c3org.highwire.dtl.DTLVardef@7cc470_HPS_FORMAT_FIGEXP M_FIG C_FIG SummaryThe protocol for generating mouse and human oviductal assembloids by combining epithelial and stromal cells for studying oviductal function in an in vitro setting.

cell biology↗

bioRxiv2026

Kinase inhibitors can change protonation or tautomeric state upon binding

The binding affinity of a ligand to a protein is influenced by the protonation and tautomeric states of both partners. However, this relationship remains under-investigated due to the limited availability of computational tools capable of considering all charge and tautomer states in a scalable manner to study clinically relevant systems. Here, we use Multi-Conformation Continuum Electrostatics (MCCE) to calculate the protonation and tautomer distributions of nine kinase domains bound to 18 FDA-approved inhibitors while considering their Boltzmann-ensemble. Our simulations show that protein net charge and proton distribution remain largely stable even upon binding charged inhibitors. Our results find that individual inhibitor charges are dynamic, frequently increasing, or decreasing upon binding a specific protein target. Kinase-inhibitor binding significantly shifts the relative probabilities of low-energy states ({Delta}G < 2.5 kcal/mol), though it does not recruit higher-energy conformers into the bound population. Our consideration of all possible charge states and tautomers enable us to identify when tautomer have significant significant free binding energy differentials (3-6kcal/mol). In turn, we find that minority species can become the dominant component in the bound state, emphasizing the necessity of considering ensemble-wide protonation and tautomer states to accurately predict protein-ligand binding energetics.

biophysics↗

bioRxiv2026

CytoGem-XAI:A Hypergraph Neural Network Framework for Genome-Scale Metabolic Modeling and Interpretable Analysis

Genome-scale metabolic models are essential for understanding cellular metabolism, yet existing deep learning approaches remain black boxes, and traditional flux balance analysis (FBA) cannot provide sample-specific predictions. To our knowledge, CytoGem-XAI is the first framework to combine hypergraph neural network representation with interpretable, FBA-parallel analysis and sample-specific metabolic characterization. Built upon hypergraph representations where reactions are encoded as hyperedges connecting their participating metabolites, CytoGem-XAI introduces three analysis modules: perturbation-based carbon source importance ranking, hard intervention reaction bottleneck identification, and pathway-level topological attribution. Beyond prediction, CytoGem-XAI uniquely enables condition-dependent carbon source essentiality and reaction bottlenecks that vary with genetic background--capabilities absent from both traditional FBA and existing deep learning methods. Trained on 17,400 E. coli growth conditions using 10-fold cross-validation, our framework achieves R2 = 0.862, substantially outperforming AMN (R2 = 0.81, +6.4%), FBA (R2 = 0.62, +39%), and gradient boosting baselines (R2 = 0.71, +21%). Biological validation confirms that CytoGem-XAI identifies known essential carbon sources (e.g., alanine, malate) and rate-limiting enzymes (e.g., TCA cycle), while also revealing N-acetylmuramate--a peptidoglycan precursor--as a previously underappreciated essential nutrient.

bioinformatics↗

bioRxiv2026

Multivariate integration of histological images and gene expression data: a comparative review

Integrating histological images with gene expression data offers a promising approach for linking tissue morphologies to molecular signatures and improving disease subtyping. However, such integration remains challenging due to the high dimensionality of these datasets, cross-modal heterogeneity, and limited interpretability. Multivariate methods such as Sparse Canonical Correlation Analysis (Sparse CCA), Joint Nonnegative Matrix Factorisation (Joint NMF), and Angle-based Joint and Individual Variation Explained (AJIVE), have been used to address these challenges by reducing dimensionality while identifying features associated with latent factors, thereby enhancing biological interpretability. Despite increasing application in imaging-omics research, systematic comparisons of their methodological properties remain limited. Consequently, users often lack guidance on how to appropriately select these methods in practice, and these approaches are frequently treated as interchangeable despite differing modelling assumptions. Here, we use paired H&E images and gene expression data from breast cancer as a representative case study to examine the methodological characteristics, interpretability, and complementary properties of these integration approaches. Our results show that each method captures distinct yet complementary aspects of the underlying information. Although the biological findings are derived from the TCGA-BRCA datasets, the methodological insights identified here extend more broadly to imaging-omics integration studies. Overall, this comparative review highlights the strengths and limitations of each approach and outlines considerations for future methodological development.

bioinformatics↗

bioRxiv2026

The resource-rational dynamics of evidence accumulation

Evidence accumulation is a fundamental aspect of human decision-making. However, how the precise temporal structure of evidence shapes the accumulation process has not been systematically studied. As a result, current understanding of evidence accumulation remains largely limited to its time-averaged behavior. We tested human subjects in a visual estimation task in which they inferred the angular position of an unknown source from a noisy stimulus sequence. Introducing systematic temporal perturbations, i.e., breaks of different durations and at different positions in the otherwise regular evidence sequence, revealed that subjects actively compensated for the memory loss endured during the break by dynamically enhancing evidence integration and memory maintenance immediately after the break. We derived a new time-continuous Bayesian updating model that is dynamically constrained by optimal performance-effort trade-offs. With two free parameters determining the overall resource-efficiencies of encoding and memory maintenance, the model accurately predicts the rich dependencies of subjects accumulation behavior on the evidence schedule, including subjects individual tendencies to emphasize either early (primacy) or late (recency) samples in the evidence sequence. Our results suggest that evidence accumulation is a non-stationary, dynamically controlled process that optimally balances the information gained from incoming evidence against the cognitive effort required to acquire and maintain it. The proposed model is general and should apply broadly across many task domains.

animal behavior and cognition↗

bioRxiv2026

An oxytocin-gated circuit from the hypothalamus silences olfactory tubercle neurons to drive prosocial grooming

Spontaneous helping behaviors such as allogrooming are vital for survival in social species, yet their underlying neural mechanisms remain largely unknown. Although oxytocin (OXT) is known to modulate allogrooming, the precise neural circuits, molecular pathways, and sensory drivers remain unclear. Here, using a social emergency paradigm in mice, we show that observer mice selectively allogroom distressed, anesthetized conspecifics, a behavior that is guided by main olfactory system cues and mitigates the demonstrators anxiety. We identify an oxytocinergic circuit from the paraventricular hypothalamus to the medial olfactory tubercle (PVNOXT[->]mOT) that is necessary and sufficient for this helping behavior. In vivo fiber photometry reveals that PVNOXT[->]mOT activity and OXT release are temporally locked to allogrooming initiation. This behavioral effect requires oxytocin receptor (OXTR) signaling specifically on mOT dopamine D1 receptor-expressing neurons, where OXTRs suppress neuronal excitability via G protein-gated inwardly rectifying K+ (GIRK) channels. Disruption of this inhibitory mechanism induces neuronal hyperexcitability and impairs allogrooming, a deficit rescued by restoring GIRK function. These findings define a PVNOXT-mOT circuit for prosocial helping, revealing an oxytocinergic pathway with potential therapeutic relevance for neuropsychiatric disorders characterized by social deficits.

neuroscience↗

bioRxiv2026

DIVAS: an R package for identifying shared and individual variations of multiomics data

MotivationMultiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities. ResultsWe present an open-source R package implementing DIVAS, a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas AJIVE and MOFA+ did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss. Availability and implementationDIVAS is available as an R package on GitHub (https://github.com/ByronSyun/DIVAS), with documentation and vignettes at https://byronsyun.github.io/DIVAS/. A step-by-step vignette for the COVID-19 case study is available at https://byronsyun.github.io/DIVAS_COVID19_CaseStudy/.

bioinformatics↗

bioRxiv2025

Derivation, Characterizations, and Applications of Rabbit Haploid Embryonic Stem Cells

Haploid embryonic stem cells (haESCs), which contain a single set of chromosomes, provide powerful systems for investigating gene function and creating gene-edited animal models. haESCs have been derived from several mammalian species including human, mouse and rats, yet not from rabbits. Here, we report the derivation of parthenogenetic haESCs of rabbit (rbPhESCs) using an optimized culture medium. These cells maintain a stable haploid karyotype during long-term culture and own pluripotency features comparable to diploid embryonic stem cells derived from rabbit fertilized embryos. By integrating gene trapping cassette into ROSA26 locus, rbPhESCs offer an ideal platform for high-throughput functional genomic screening, as shown in haESCs from other mammals. Consistent with their parthenogenetic origin, rbPhESCs durably preserve maternal-specific imprinting patterns even after extended culture in serum-containing conditions, providing a valuable platform for generating disease models deficient in maternally expressed imprinted genes. Therefore, our findings expand the repertoire of mammalian haESCs and establish rbPhESCs as a valuable platform for genetic studies and biotechnological applications in rabbits.

cell biology↗

bioRxiv2025

phylobar: an R package for multiresolution compositional barplots in omics studies

SummaryStacked barplots, though widely used in microbiome studies, can obscure important patterns in microbiome data. They omit rare taxa and can mask shifts that emerge at finer taxonomic levels. To address this issue, we introduce phylobar, an R package that interactively links stacked barplots with overview phylogenetic or taxonomic hierarchies. The interface allows users to collapse or expand subtrees, paint color palettes interactively, and search for specific taxa. This allows comparison across taxonomic resolutions that are hidden in static overviews. phylobar works with any omics data with hierarchical organization, including cell type hierarchies, as we demonstrate in a case study of immune cell composition in COVID-19 patients. Availability and Implementationphylobar is available as an R package on GitHub. It uses the htmlwidgets library to link interactive D3 visualizations with R. The interactive plots can be embedded within R Markdown or Quarto notebooks, and views can be exported as vector graphics files. The package is open source and documented at https://mkdiro-O.github.io/phylobar.

bioinformatics↗

bioRxiv2025

Vaginal epithelial estrogen receptor α coordinates glycogen deposition, microbial stability, and pH regulation in mice

Estrogen plays a central role in regulating the vaginal environment, but the specific contribution of epithelial estrogen receptor (ESR1) to microbial and biochemical homeostasis has not been fully defined. In our previous work, we showed that epithelial ESR1 is indispensable for estrogen-induced epithelial proliferation, cornification, and MUC1 expression. Here, using mice with conditional deletion of Esr1 in vaginal epithelial cells, called epithelial Esr1d/d, we extend these findings to demonstrate that epithelial ESR1 also regulates glycogen deposition, luminal pH, and microbial stability. Compared to control littermates, epithelial Esr1d/d mice reduced glycogen abundance, elevated vaginal pH, and a compositional shift in the vaginal microbiome, marked by enrichment of Comamonadaceae and loss of Lactobacillus species, without significant differences in alpha diversity. These changes parallel features of postmenopausal dysbiosis in women. Together, our findings identify epithelial ESR1 as a master regulator of multiple pathways that sustain vaginal homeostasis, integrating epithelial metabolism, barrier function, and host-microbe interactions. This work provides a mechanistic framework to understand postmenopausal vaginal dysbiosis and suggests epithelial estrogen signaling as a potential therapeutic target for genitourinary syndrome of menopause. Significance StatementThe vaginal environment is essential for reproductive and gynecologic health, yet the mechanisms by which estrogen shapes this niche remain incompletely understood. We show that epithelial estrogen receptor (ESR1) regulates glycogen deposition, luminal pH, and microbial composition in the murine vagina. Loss of epithelial ESR1 reduced glycogen and increased luminal pH without altering overall microbial diversity, but shifted community structure toward enrichment of Comamonadaceae, a family associated with neutral to mildly alkaline environments. These findings identify epithelial ESR1 as a key regulator of the metabolic and physicochemical conditions that maintain vaginal microbial balance and provide a mechanistic framework for understanding postmenopausal dysbiosis.

physiology↗

bioRxiv2025

CellDiffusion: a generative model to annotate single-cell and spatial RNA-seq using bulk references

Annotating single-cell and spatial RNA-seq data can be greatly enhanced by leveraging bulk RNA-seq, which remains a cost-effective and well-established benchmark for characterising transcriptional activity in immune cell populations. However, a major technical hurdle lies in the contrasting properties of these data types: single-cell and spatial data are inherently sparse due to its cell-level sampling scheme, leading to much lower sequencing depth compared to bulk RNA-seq. We developed CellDiffusion, a generative machine learning (ML) tool that bridges this gap. CellDiffusion generates realistic virtual cells to augment the sparse single-cell and spatial data, improving signals and the representation of rare cell types. The augmented data are more comparable to bulk references, increasing the accuracy of cell type annotation using bulk references and automated ML classifiers. We benchmarked CellDiffusion on single-cell and spatial datasets from human peripheral blood samples, white adipose tissues, and breast tumours. Our method significantly outperforms state-of-the-art methods such as SingleR, Seurat, and scVI. In addition, CellDiffusion provides critical biological insights, including the identification of novel cell subtypes and their function during cell state transition; the discovery of new marker genes for tissue-resident immune cells, revealing their functional shifts in myeloid populations; and the accurate characterisation of cell subtypes in spatial transcriptomics to decipher tumour microenvironment.

bioinformatics↗

bioRxiv2025

Integrative Biophysical Illumination of the 3D GPCRome Dynamics

Achieving high-resolution insights into protein dynamics remains a major challenge, particularly for membrane proteins such as G protein-coupled receptors (GPCRs) - a highly dynamic class of cell surface receptors and key drug targets. Here, we present an integrative strategy that bridges computational and experimental biophysical methods for probing protein dynamics. To illustrate its potential, we developed GPCRmd-NMR, a platform that synergistically combines molecular dynamics (MD) simulations with high-resolution nuclear magnetic resonance (NMR) spectroscopy across the 3D GPCRome. The resulting dataset maps over 20 billion NMR signals to more than 7 million conformational snapshots of GPCRs, enabling structure-resolved analysis at an unprecedented scale. Application across multiple GPCRs provides access to previously underexplored functional GPCR dynamics, receptor states, and sites, including microswitches and hidden ligand entrance gateways. Our integrated framework offers a powerful solution to long-standing challenges faced by both experimental and computational researchers. To support broad accessibility, GPCRmd-NMR is available as an open-access, web-based framework equipped with user-friendly analysis tools (https://www.gpcrmd.org/gpcrmd-nmr-tool) and is readily applicable to other dynamic protein families beyond GPCRs and other types of biophysical data.

bioinformatics↗

bioRxiv2025

Quantifying the impact of genetic mutations on enhancer dynamics

Transcriptional regulation is mediated by enhancers, yet how genetic perturbations alter enhancer activity and gene expression remains poorly understood. We developed UDI-UMI-STARR-seq, which integrates dual indexes and unique molecular identifiers, and combined it with RNA-seq to profile the effects of perturbations on enhancer activity and target gene expression. We applied this approach to a library of 253,632 fragments representing 46,142 cell type-specific candidate enhancers and assessed the impact of CRISPR/Cas9-mediated deletion of six transcription factors (or TFs; ATF2, CTCF, FOXA1, LEF1, TCF7L2, and SCRT1) with diverse regulatory roles. Across knockout lines, we identified responsive enhancers that were either repressed or induced, often through motifs such as the p53 family of TFs. Enhancer-gene mapping revealed TF-specific programs, including repression of Wnt/p53 targets with ATF2 or LEF1 loss, downregulation of the FIRRE locus with CTCF loss, and compensatory upregulation of RNA polymerase II regulators following FOXA1 depletion. A deep learning model trained on enhancer sequences recapitulated core principles of enhancer grammar, including cooperative motif syntax and the influence of flanking sequence context. Applying this framework to the neurodevelopmental disorder-associated 16p12.1 deletion identified responsive enhancers linked to genes involved in axon guidance, synaptic plasticity, and translational control, providing a scalable readout of enhancer dynamics generalizable to genetic mutations.

genomics↗

bioRxiv2025

Dynamic S-acylation of GSDMA regulates pyroptosis

GSDMA, the primary member of the gasdermin family found in the skin, is critical for pathogen-induced pyroptosis during infection. Recent studies revealed that another gasdermin, GSDMD, undergoes palmitoylation during pyroptosis. However, whether and how the other gasdermin members undergo lipid modification remains poorly understood. Here, we demonstrate that GSDMA is S-acylated at the conserved cysteine residues in its N-terminal domain. We show that S-acylation of GSDMA promotes pyroptosis by facilitating its membrane anchoring and protein oligomerization, a mechanism distinct from the palmitoylation of GSDMD at the N-terminal C191 residue. Additionally, we present evidence that recombinant proteins of GSDMA and GSDMD can undergo S- acylation in vitro via direct interaction with palmitoyl-CoA, suggesting they potentially possess auto-acylation capacity. Furthermore, we identify ABHD17A as one of the deacylating enzymes that regulate the dynamic fatty acylation cycle of GSDMA. Overall, our studies reveal new molecular mechanisms underlying GSDMA function through S- acylation and underscore its important role in regulating pyroptosis mediated by GSDMA. HighlightsO_LIGSDMA is S-acylated at N-terminal conserved cysteine residues. C_LIO_LIGSDMA S-acylation facilitates membrane anchoring and protein oligomerization. C_LIO_LIGSDMA and GSDMD recombinant proteins can undergo S-acylation in vitro C_LIO_LIThe ABHD17A enzyme is involved in GSDMA deacylation. C_LI

biochemistry↗

bioRxiv2025

Cytoplasmic Abundant Heat-Soluble Proteins from Tardigrades Protect Synthetic Cells Under Stress

Cytoplasmic abundant heat-soluble (CAHS) proteins, a stress-responsive intrinsically disordered protein from tardigrades, have been discovered to form gel-like networks providing structural support during dehydration, thus enabling anhydrobiosis. However, the mechanism by which CAHS proteins protect the dehydrating cellular membrane remains enigmatic. Using giant unilamellar vesicles (GUVs) as a model membrane system, we show that encapsulated CAHS12 undergoes a reversible structural transformation that reinforces membrane integrity and preserves encapsulated components, mimicking natural anhydrobiosis. CAHS12-containing GUVs demonstrated stability for weeks and mechanical robustness under dehydration, elevated temperature, and osmotic stresses. Molecular simulations suggest that CAHS12 forms a filamentous network within the vesicle lumen that mitigates membrane collapse and preserves compartmental architecture. Synthetic cells with cell-free transcription-translation capabilities withstand desiccation and recover biochemical activities, akin to the tun state of the tardigrade. This discovery opens up synthetic cell applications in bioengineering, cold-chain-independent biomanufacturing, and adaptive biointerfaces.

synthetic biology↗

bioRxiv2025

Oral microbial signatures of head and neck cancer patients highlight diverse longitudinal patterns of oral mucositis severity

BackgroundOral mucositis is a painful complication commonly observed in head and neck cancer patients receiving cancer treatment. Emerging evidence suggests that changes in the oral microbiome can contribute to oral mucositis development, making microbial signatures potential targets for therapeutic inter-ventions. This study aimed to: (1) characterize longitudinal microbial patterns of oral mucositis severity among head and neck cancer patients; (2) determine clinically relevant patient clusters based on oral mucositis severity trajectories; and (3) identify microbial signatures specific to these clusters. ResultsWe derived a calibrated oral mucositis score by applying non-negative sparse principal component analysis to seven oral mucositis related symptom ratings, using longitudinal microbiome data from 140 head and neck cancer patients. Functional data analysis and hierarchical clustering identified three distinct patient clusters with differing microbial trajectories of oral mucositis progression. One cluster exhibited patients with a rapid increase in oral mucositis severity following treatment initiation, while the other clusters displayed more gradual increase. Demographic comparisons revealed significant differences in age and weight distributions between clusters, with older, lighter patients more common in clusters experiencing more gradual oral mucositis progression. Partial least squares knockoff analysis identified cluster-specific microbial signatures: notably, Prevotella spp. positively associated with calibrated oral mucositis score across all clusters, while Alloprevotella (Alloprevotella0302) was significantly enriched only in patients experiencing rapid oral mucositis progression. Conversely, genera associated with oral health, including Haemophilus, Rothia, and Actinomyces, were negatively correlated with calibrated oral mucositis score. ConclusionsDistinct trajectories of oral mucositis scores in head and neck cancer patients are linked to specific oral microbial profiles and demographic factors. The identification of cluster-specific microbial profiles highlights the potential for microbiome-targeted interventions to manage oral mucositis severity. While most taxa were cluster-specific, Prevotella consistently ranked among the top taxa positively associated with the calibirated oral mucositis score across clusters, suggesting it may not differentiate between patient groups but rather reflects overall disease severity.

bioinformatics↗