Biology subjects

Zhang, X.

Publications and source records attributed to Zhang, X..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

TangentXY: Improved copy-number estimation of sex chromosomes reveals features associated with aneuploidies in chromosomes X and Y across cancers

Signal-to-noise ratios in copy-number estimates across genomic loci improve with the use of large panels of germline reference data. However, in the case of sex chromosomes, germline samples differ between males and females (males have one X chromosome (chrX) and one Y chromosome (chrY), while females have two chrXs), limiting the size of the reference sets to those that match the sex of the index sample. Frequent mosaic loss of chrY in normal male tissue also leads to biases when not accounted for. Here we report TangentXY, a copy-number normalization method that uses both male and female reference samples, correcting for sex differences in chrX and accounting for chrY mosaicism, to assess sex chromosome copy numbers for samples of either sex. We show that TangentXY maintains sex-specific differences in chrX and chrY while minimizing noise. We also used TangentXY to analyze associations between copy-number alterations in sex chromosomes and cancer characteristics, and found that losses in the chrX but not chrY are correlated with TP53 mutation, that gain of chrX can predict patient prognosis in certain tumor types, and that the vast majority of gains of chrX in males remain active, whereas gains of chrX in females are divided approximately equally between active and inactive chrXs. We also found that loss of chrY correlates with inactivating driver mutations in escape genes in chrX, suggesting a mechanism by which its frequent loss promotes cancers in males.

cancer biology↗

bioRxiv2026

SIGMA: SIgnaling via Golgi Morphology Analysis for pathway-state classification

Organelle morphology is increasingly recognized as a computationally accessible readout of molecular state, yet whether subcellular architecture can identify which signaling pathway is active, not merely whether a cell is perturbed, remains untested. Here we introduce SIGMA (SIgnaling via Golgi Morphology Analysis), a framework that reframes pathway identification as a morphological classification problem centered on the Golgi apparatus. SIGMA integrates imaging flow cytometry for population-scale Golgi profiling, teacher-student knowledge distillation to transfer fine morphological cues from limited high-resolution images to an efficient lower-resolution classifier, and mechanistic generalization testing with chemically distinct activators. Applied to MRC-5 fibroblasts across six conditions spanning four signaling cascades plus controls, SIGMA achieved 90.02% cell-level accuracy and correctly assigned five of seven structurally unrelated external compounds to their expected pathway class. These results establish Golgi morphology as a multiplexed, machine-readable fingerprint of signaling state.

molecular biology↗

bioRxiv2026

Early dynamics of a neutrophil interferon program drive immunotherapy resistance and limit durable anti-tumor immunity

Despite the remarkable success of immune checkpoint blockade (ICB), durable clinical benefit remains limited to a subset of patients, highlighting the need for predictive biomarkers and new therapeutic targets. Here, we investigated the early immune dynamics associated with anti-PD1 responsiveness using longitudinal single-cell transcriptomics, spatial transcriptomics, and neutrophil-specific genetic perturbations in preclinical models of triple-negative breast cancer. We identified a conserved neutrophil interferon-stimulated gene (ISG) program that emerges before overt tumor divergence and predicts resistance to anti-PD1 therapy. Successful treatment was associated with an early reduction of IFN-responsive neutrophils in both blood and tumor tissues, whereas persistent neutrophil IFN signaling correlated with therapeutic resistance. Mechanistically, genetic disruption of either type I or type II interferon signaling in neutrophils enhanced anti-PD1 efficacy and promoted durable anti-tumor immune memory. Spatial analyses further revealed an inverse association between IFN-responsive neutrophils and tertiary lymphoid structure-enriched regions during effective therapy. Together, these findings identify IFN-responsive neutrophils as early determinants of immunotherapy outcome and establish dynamic neutrophil interferon programs as candidate blood-based biomarkers and therapeutic targets for cancer immunotherapy.

cancer biology↗

bioRxiv2026

Evaluating Clustering and Imputation Approaches for Multivariate Longitudinal Clinical Data Using a Real-World eCDF-Copula Resampling Framework

The rapid growth of technologies such as wearable devices and high-throughput sequencing has generated unprecedented volumes of longitudinal data, often with multiple correlated features. This trend highlights the need for robust clustering methods tailored to multi-feature longitudinal datasets. Although previous reviews have benchmarked clustering methods in this context, most evaluations relied heavily on simulated and overly simplified datasets, which substantially limit their interpretability and applicability to real-world data. Missing data is also ubiquitous in longitudinal data, yet understudied in the context of clustering. To address these gaps, we systematically evaluate clustering methods across three distinct types of real-world longitudinal datasets: (1) a high-dimensional, low-timepoint metagenomic study, (2) a low-dimensional, high-timepoint multi-site repeated-measure study, and (3) a high-dimensional, high-timepoint repeated-measure study. We further investigate how missing data and different imputation strategies influence clustering performance. In addition, we introduce a novel resampling framework based on the empirical cumulative distribution function (eCDF) and a copula. This framework enables us to simulate multi-feature longitudinal data that preserves the correlation structure of real datasets, while also allowing for controlled variations such as sample size. Our simulation framework naturally accommodates missing data, thereby providing a flexible tool for benchmarking and comparing clustering methods in more realistic longitudinal settings.

bioinformatics↗

bioRxiv2026

Cellular Determinants of CA1 Pyramidal Cell Recruitment during Sharp-Wave Ripples

Hippocampal ripples, accompanied by highly synchronous neuronal firing, are critical for immediate offline memory consolidation, learning, and planning. However, the cellular mechanisms that determine which individual neurons are recruited to participate in action potential (AP) firing during ripples remain unclear. We investigated the membrane potential (Vm) dynamics of CA1 pyramidal cells during ripples using whole-cell recordings in awake, head-fixed mice. We found that ripple participation was strongly associated with concurrent membrane-potential depolarization on an event-to-event basis, whereas between-cell differences in overall participation propensity were jointly explained by ripple-associated depolarization and intrinsic excitability. Applying this framework across CA1 cell populations revealed that differences in recruitment between superficial and deep pyramidal cells were primarily driven by ripple-associated depolarization, counteracted by excitability. Differences between place and non-place cells were similarly driven by ripple-associated depolarization, though this effect acted in the same direction as excitability. Finally, we identified the post-ripple LFP rebound as a prominent marker of ripple diversity linked to post-ripple membrane-potential hyperpolarization at the single-neuron level. Together, these findings provide a mechanistic understanding of how CA1 pyramidal cells are selectively recruited during hippocampal ripples.

neuroscience↗

bioRxiv2026

Awake sharp-wave ripples coincide with rhythmic cortical synchronization

Analysis of the Allen Institute Visual Coding Neuropixels dataset revealed a ~5 Hz synchronization of visual cortical activity in awake, immobile mice. During bouts of this rhythm, synchronized cortical bursts coincided with changes in the occurrence of sharp-wave ripples (SWRs) and dentate spikes and in SWR properties. Cortical synchronization, SWRs, and dentate spikes were also coordinated with slower pupil-size fluctuation, revealing that awake SWRs are embedded within nested brainwide rhythms.

neuroscience↗

bioRxiv2026

Programmable sequence-resolved in situ profiling of glycoRNA-protein interactomes by GlycoRNA-Map

Cell-surface glycoRNAs participate in diverse biological processes, but they were investigated as pooled populations and the protein interactome of individual glycoRNA species remain unresolved. Here we develop GlycoRNA-Map, a programmable photocatalytic proximity-labeling method that maps proteins surrounding sequence-defined endogenous glycoRNAs on living cells. A sialic-acid-binding aptamer and an RNA-hybridization probe jointly recruit a fluorophore-modified oligonucleotide through dual recognition of the glycan and RNA moieties of glycoRNAs. Light-activated biotinylation followed by quantitative mass spectrometry identified 200-418 enriched proteins for each of five glycoRNAs in A549 cells. Comparison of these proteomes revealed limited overlap among different glycoRNA sequences and across cell types, demonstrating that glycoRNA-associated protein environments depend on both RNA identity and cellular context. Transforming growth factor-{beta} receptors 1 and 2 (TGFBR1 and TGFBR2) were identified in the proximal proteomes of multiple glycoRNAs. Removal of cell-surface RNAs enhanced TGF-{beta} receptor signaling and promoted cell migration, demonstrating that glycoRNAs can functionally regulate extracellular signaling by interacting with their protein neighborhoods. GlycoRNA-Map thus provides an accessible strategy for resolving sequence-specific glycoRNA-protein interactome and discovering their roles in membrane signaling.

biochemistry↗

bioRxiv2026

Genomic correlates of metastatic competence and progression in human melanoma

Genomic events and their timing that grant a primary tumour the competence to disseminate remain poorly defined. We performed sequencing of 247 stage I/II primary cutaneous melanomas (CMs) and 60 matched metastases without intervening therapy from a prospectively followed registry cohort with a median followup of 92 months, integrating copy-number, mutational, protein and spatial-transcriptomic analyses. Relapse was not distinguished by oncogenic point mutations, which were largely shared between primaries and metastases, but by somatic copy-number alterations (SCNAs) and global chromosomal instability. We defined OncoCycle, a six-gene copy-number signature (amplification of CDK4, MCL1 and CD276; biallelic loss of CDKN2A, CDKN2B and TP53BP1) that predicted relapse independently of established clinicopathological features in melanoma, and a pan-cancer analysis. In matched pairs, metastatic progression was driven by continued copy-number evolution and reduction in intra-tumoural heterogeneity, rather than by acquired point mutations, and OncoCycle alterations from primary tumours were preserved in metastasis seeding clones. Clonal reconstruction revealed both monoclonal and polyclonal metastasis seeding, and spatial transcriptomics resolved copy-number-defined metastatic subclones occupying and programming distinct immune and stromal niches. Thus, metastatic competence was primed early by focal SCNAs on a background of chromosomal instability, elaborated by continued copy-number evolution during dissemination and spatio-temporal interactions with the tumour-microenvironment.

genomics↗

bioRxiv2026

Acoustic Monitoring of Tropical Bats: Saccopteryx bilineata's response to habitat and time across a restoration gradient using a 2D-CNN

Global biodiversity loss is a consequence of habitat degradation and fragmentation caused by the expansion of the Anthropocene and the overexploitation of natural resources. Passive acoustic monitoring enables longitudinal assessment of biodiversity but generates massive datasets. Advancements in machine learning have enabled the development of convolutional neural networks capable of automating this process. We developed a lightweight 2D convolutional neural network capable of detecting echolocation calls of the greater sac-winged bat, Saccopteryx bilineata, an insectivorous aerial edge-foraging bat, across a tropical restoration gradient in Para, Brazil. The 2D-CNN model achieved a 97.84% accuracy and 100% precision. Deploying this model across 157,335 field recordings across 29 sampling points resampled over 3 years (2023-2025) yielded 2,098 positive detections of S. bilineata. Generalised linear mixed models revealed that time since reforestation significantly increased the odds of detecting S. bilineata ({beta} = 0.22, p < 0.001), and that detection probability was 2.14 times higher in restoration plots than in forest habitats ({beta} = 0.76, p = 0.0701). Acoustic hardware had a significant covariate influence in both fitted models ({beta} = 0.52, {beta} = 0.72, both p < 0.001). These findings demonstrate the practical applications of deep learning and bioacoustics to quantify habitat recovery and the value of restoration in regenerating functional ecological niches for aerial insectivores during early successional stages.

ecology↗

bioRxiv2026

YieldLearn: An interpretable machine-learning pipeline for estimating yield-related traits based on physiological indicators

Efficient yield prediction under field conditions remains a key challenge for crop breeders and agronomists. YieldLearn is an interpretable machine-learning pipeline developed to estimate yield-related traits in durum wheat (Triticum durum) using physiological indicators measured by the Hansatech Instruments Pocket PEA. The workflow integrates fluorescence parameters (Area, Fo, Fm, Fv/Fm, PI Inst.) with field-measured agronomic traits (Booting, Height, Maturity, Lodging, Yield kg/plot, and Adjusted Yield kg/ha). Five supervised learning algorithms multinomial logistic regression, random forest, support vector machine (radial kernel), k-nearest neighbors, and decision tree were evaluated using five-fold cross-validation. Models achieved average classification accuracy around 0.57 with AUC values exceeding 0.58. SHAP (SHapley Additive exPlanations) visualizations revealed that high Fv/Fm and PI Inst. values were positively associated with yield potential, whereas elevated Fo indicated stress-induced yield reduction. An interactive Shiny App enables real-time prediction and feature-importance exploration. Given the small sample size (165 plots) and absence of environmental covariates such as temperature or soil moisture, YieldLearn should be regarded as a reproducible exploratory framework rather than a production-ready predictor. Nonetheless, it offers a valuable foundation for guiding experimental design, validating physiological indicators, and supporting the next generation of scalable, interpretable crop-yield models.

bioinformatics↗

bioRxiv2026

Biologically grounded cell profiling across microscopy modalities

Microscopy-based cell profiling has broad applications in biological discovery, disease characterization, and phenotypic drug screening. Modern microscopy continues to push the limits of resolution, speed, depth and throughput, but better imaging does not automatically lead to better biomedical discovery and translation. A key bottleneck is feature representation: existing features are either handcrafted or learned as black-box embeddings and often lack explicit biological meaning. Here we propose biological grounding as a first principle for cell profiling and implement it through MorphAgent, an AI agent framework in which each quantitative feature originates from a biological hypothesis and is anchored to a cellular structure or process. By integrating biological knowledge, multimodal reasoning and automated validation, MorphAgent makes biologically grounded feature design systematic and scalable. We show biological grounding fundamentally changes the properties of cell profiling features. Across three microscopy modalities, MorphAgent produces compact, expressive and transferable features for drug screening, cell state characterization and disease morphology analysis. In wide-field Cell Painting, a common assay for phenotypic drug screening, it improves perturbation-retrieval mean average precision by 48% over CellProfiler and 20% over DeepProfiler while using substantially fewer dimensions. In confocal mitochondrial imaging, biologically grounded features transfer across independently acquired datasets and imaging resolutions, supporting accurate aging-state classification. In structured illumination microscopy (SIM) imaging of Tau-labeled samples, biologically grounded super resolution features reveal nanoscale morphologies inaccessible at conventional resolution, enhancing the discrimination of disease-associated mutations. Moreover, biologically grounded features provide a hierarchical organization of cellular morphology, enabling multilevel alignment with transcriptomics data and facilitating mechanistic understanding. Biologically grounded cell profiling thus brings AI reasoning closer to biological mechanisms, advancing biomedical discovery and translation.

bioinformatics↗

bioRxiv2026

Mutation Order and Selection Shape Intratumor Heterogeneity in Tumor Evolution

Cancer progression often requires the accumulation of multiple driver mutations, yet the same set of drivers may be acquired in different orders. How these alternative mutation-order pathways jointly shape tumor clonal structure remains unclear. We develop a multitype branching-process model in which malignant transformation requires two driver mutations and distinguish malignant cells both by their mutation order and by the independent transformation event that founded their clone. Under a successive exponential approximation, we establish point-process limits for the pathway-specific clone-size processes and derive a closed-form expression for the limiting expected Simpson's index of the combined malignant population. When the two mutation orders produce malignant cells with the same net growth rate, the limiting index separates into effective pathway weights, determined by mutation rates and birth-death dynamics at preceding stages, and within-pathway concentration terms, determined by intermediate-to-malignant growth-rate ratios. This decomposition shows that a driver's effect on heterogeneity depends critically on when it is acquired. A strong driver acquired early expands the intermediate lineage and increases the supply of independent malignant founders, whereas the same driver acquired at the final transition strengthens the growth and age advantage of early-founded malignant clones. Under additive fitness effects, these counteracting mechanisms can produce a non-monotone relationship between selective advantage and clonal concentration. We further show that threshold-like non-additive fitness effects can generate highly concentrated malignant populations, while order-dependent terminal fitness leads the faster-growing pathway to dominate asymptotically. Together, these results reveal how mutation order, mutational accessibility, selection, and epistasis jointly determine lineage-level intratumor heterogeneity.

cancer biology↗

bioRxiv2026

A single-nucleus multi-omic atlas of gene regulation across 21 adult human tissues

Diverse human cell types establish specialized functions through lineage- and context-specific regulatory programs. Interpreting non-coding genetic risk requires integrated multi-omic reference maps that directly connect regulatory DNA to cellular expression across human tissues. Here we present a single-nucleus multi-omic atlas comprising 459,856 transcriptomic and chromatin accessibility profiles from 21 adult human tissues and four donors, including paired measurements from 160,688 nuclei. The atlas resolves nine cell lineages, 61 broad cell types and 313 subclusters, and identifies 1,085,062 candidate cis-regulatory elements (cCREs), including 161,270 novel elements absent from ENCODE. Regulatory activity was dominated by cell identity but refined by tissue context. Joint profiling enabled 871,177 cCRE-gene associations and revealed lineage-specific regulatory architectures. Cross-tissue accessibility further identified lineage-restricted and constitutively inaccessible chromatin domains, the latter showing preferential hypomethylation across human cancers. Furthermore, we leverage this dataset to train sequence-to-function models to predict chromatin-accessibility effects for 548,656 fine-mapped variants, identifying 18,133 high-effect variants, including 1,120 broadly active variants. Models trained for eight endothelial subtypes further resolve predicted variant effects across vascular beds. Together, this atlas provides a comprehensive cellular and computational framework for interpreting regulatory sequence, context-dependent gene control, and complex trait genetics across the human body.

genomics↗

bioRxiv2026

The diversity, evolution, and conservation of African plants

Africa hosts remarkable biodiversity across diverse biomes, yet its evolutionary history at continental-scale remains poorly understood. We integrate a time-calibrated phylogeny covering 93% of African vascular plant genera with continental-scale distribution data to jointly evaluate phylogenetic diversity (PD), endemism, and diversification rates (DR). We reveal a pronounced decoupling between DR and accumulated evolutionary history across Africa. Mountain, arid, and semi-arid areas emerge as centers of neo-endemism and elevated diversification, whereas lowland and humid regions, particularly the Guineo-Congolian zone, are dominated by older lineages with low recent diversification. Several DR hotspots do not overlap with Global Biodiversity Hotspots, indicating a mismatch between rapid diversification and accumulated evolutionary history. High net diversification and speciation rates during the last 15 million years were likely driven by cooling, aridification, and mountain uplift that created new ecological gradients and isolation barriers. These abiotic changes, interacting with lineage-specific traits, shaped African modern flora and highlight conservation gaps, especially in arid regions

evolutionary biology↗

bioRxiv2026

π-MNovo improves de novo peptide sequencing through microbial-domain adaptation and evidence-guided candidate selection

The high taxonomic and strain-level diversity of microbial communities makes it difficult for reference databases to fully represent the protein sequences present in metaproteomic samples, limiting database-dependent peptide identification. De novo peptide sequencing can recover peptide sequences directly from tandem mass spectra without relying on reference databases, providing complementary peptide evidence for metaproteomics. However, most existing de novo sequencing models were developed largely from non-microbial proteomic data and lack specific adaptation to microbial spectra. Here, we established a dedicated microbial spectral resource comprising more than 10 million annotated high-quality tandem mass spectra from 72 cultured microbial isolates, with peptide digests from each isolate separated into five high-pH reversed-phase fractions to increase the opportunity for deeper and more diverse peptide sampling. Using this resource, we developed {pi}-MNovo, which combines adaptation to microbial spectra with evidence-guided candidate selection to improve full-length peptide sequencing precision. On a seven-species external benchmark, {pi}-MNovo achieved 66.30% complete-peptide recall under the same residue-mass-based complete-peptide matching criterion applied to all models, exceeding four published models by 6.9-21.7% in relative recall, with consistent gains across species and peptide-length groups. In two independent metaproteomic datasets, {pi}-MNovo increased reference-supported taxonomic coverage, while synthetic-community analysis recapitulated the designed abundance ranking. From pFind-unidentified spectra, {pi}-MNovo recovered 1,587 pFind-unreported, reference-matched peptides that passed spectrum-evidence filtering. These results show that {pi}-MNovo expands recoverable peptide evidence and enhances the utility of de novo sequencing for metaproteomic analysis.

bioinformatics↗

bioRxiv2026

Redox activation of ATG5 licenses autophagy upon nutrient restriction

Dietary restriction (DR) protects against metabolic disease, extends lifespan, and is associated with remodeling of tissue reactive oxygen species (ROS). ROS control biological adaptation through reversible oxidation of protein cysteines, yet the targets of DR-initiated redox signaling are unknown. Here we generate OxiDR, a tissue-resolved atlas of the cysteine redox proteome that quantifies oxidation state under DR. Rather than oxidizing the proteome broadly, DR selectively targets a high-amplitude set of cysteines in a tissue-specific manner, allowing systematic classification of biological processes subject to DR-mediated redox regulation. Among the cysteines most highly oxidized upon DR is Cys19 of the core autophagy protein ATG5. We show oxidation of Cys19 is required for ATG5-mediated autophagosome formation and for autophagy triggered by nutrient restriction in human cells and mice. Reversible oxidation of this cysteine promotes ATG5 binding to ATG10, thus forming the ATG5-ATG12 conjugate that lipidates LC3B/ATG8 and matures the autophagosome. In mice, loss of this redox switch prevents effective initiation of autophagy upon nutrient restriction, resulting in gross tissue pathology and rapid onset of mortality. The autophagic response to nutrient restriction is thus gated by oxidation of a single cysteine.

cell biology↗

bioRxiv2026

PROXINAUT: An Automation Platform for Rapid Hit-to-Degrader Discovery

Massive library-based platforms enable rapid early hit discovery, yet subsequent hit resynthesis and validation remain major bottlenecks in drug discovery. This delay is amplified when optimizing hits, or elaborating structures towards more advanced modalities, such as chemical inducers of proximity (CIPs). Here, we report PROXINAUT, an end-to-end workflow integrating self-encoded library-based hit discovery with an automated parallel solid-phase synthesis platform to streamline hit discovery, validation, optimization, and degrader development. As a demonstration, screening a 176k-member benzimidazole library against BRD4 yielded six hit candidates, which were obtained via parallel automated synthesis and validated as submicromolar binders. The automation workflow empowered a rapid structure activity relationship exploration around the whole scaffold and resulted in a variant with six fold improved binding affinity. Next, we aimed to automatize the development of bifunctional degraders. Solid phase synthesis typically leaves a C-terminal amide as a synthetic artifact that can compromise druglike properties. We developed a strategy to repurpose this resin-attachment site, converting it into functional degrons featuring either FBXO31-targeting C-terminal amides or cereblon-binding cyclic imides. Operating without human intervention, our automated platform executes up to 16 synthetic steps across multiple parallel structures, enabling true de novo synthesis of full bifunctional scaffolds, where ligand, linker and E3 recruiter variations can all be explored within the same workflow. This resulted in cell-active BRD4 degraders with subnanomolar to nanomolar potency. Overall, we show how library selections can merge with multi-step parallel automation to convert hits directly into validated leads and advanced degrader modalities.

bioengineering↗

bioRxiv2026

Impact of noise on optical redox ratio estimation

Optical redox ratio (ORR) is a commonly used metric to quantify the metabolic state of biological samples based on autofluorescence of NAD(P)H and FAD. Despite its relevance and popularity, little work has been done to quantify the estimation error of ORR, especially in low-photon count data. Here, we provide an analytical solution to determine ORR variability in the presence of shot noise and dark counts. We then validate this analytical model with simulated and experimental data. This work will enable researchers to quantify the uncertainty in their ORR measurements, aiding in more accurate and efficient experimental design and data analysis.

bioengineering↗