Biology subjects

Miao, Z.

Publications and source records attributed to Miao, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Multidimensional telomere diversity and inheritance at individual and population scales

Variation in telomere length, sequence composition and epigenetic state influences genome stability, aging and disease, yet its high-resolution characterization across species remains challenging. Here we present TeloXplorer, a computational framework for long-read data that jointly profiles telomere length, telomere variant repeats (TVRs) and DNA methylation at chromosome-end and haplotype resolution. Across simulated and empirical datasets from humans, Arabidopsis and yeast, TeloXplorer accurately resolved chromosome-end-specific telomere features and highlighted the importance of sample-matched, haplotype-resolved assemblies. Analysis of two human trios revealed concordant relative telomere-length profiles, predominantly Mendelian transmission of TVR haplotypes and family-conserved methylation patterns. Across 232 individuals from the Human Pangenome Reference Consortium, chromosome-end telomere-length rankings were conserved across five continental and 28 population groups. High-accuracy reads from 73 individuals further revealed elevated TVR haplotype diversity among individuals of African ancestry, together with extensive interchromosomal sharing and duplication of TVR architectures. Subtelomeric TAR1 elements were strongly associated with local DNA methylation and telomere motif diversity. Together, these analyses provide a multidimensional atlas of telomere diversity across species, chromosome ends, haplotypes and populations, revealing how telomere architecture varies and is inherited across biological scales.

genomics↗

bioRxiv2026

Cellular senescence is associated with age-related loss of liver zonation and hepatocyte function

The liver is organized into tightly regulated zones with distinct metabolic functions but zonation erodes with age. Cellular senescence contributes to aging and liver diseases, however, its impact on aging biology is ill-defined. As part of The Cellular Senescence Network Consortium, we used multiple spatial transcriptomics approaches (GeoMx, Visium, CosMx) with snRNA-seq to profile senescence signatures, zonation markers, and metabolic pathways in livers from wild-type (WT) mice of multiple ages. We observed a loss of canonical zone signatures in aged mouse livers characterized by "expansion" of midlobular (zone 2) marker gene expression, accompanied by diminished expression of zone 3 marker genes by middle-age (18 months), indicative of loss of cell identity. Multiple analytic approaches identified distinct age-, zone- and sex-specific senescence signatures, which were significantly associated with zonation markers changes. This was recapitulated in Ercc1 mutant models of accelerated senescence, supporting a causal role of senescent cells in liver aging. A "no-zone" hepatocyte-like cluster expanded with age and with the strongest Senescence-Associated Secretory Phenotype (SASP) profile. Gene expression profiles from senescent hepatocytes implicate decreased WNT signaling and increased BMP as contributing to age-related loss of zonation. Together, these data elucidate the role of senescent cells in driving aging biology in non-diseased liver through disruption of cell:cell signaling and the loss of metabolic and cell identity gene expression necessary for hepatocyte function.

cell biology↗

bioRxiv2026

An integrated single-cell atlas of human lung across the lifespan

The healthy adult lung is largely quiescent but can respond to injury and replace damaged or lost cells. However, the identities and origins of regenerative cell states remain poorly understood. Here we present the Human Developmental Lung Cell Atlas (HDLCA), a curated single-cell reference atlas of the respiratory system across the lifespan, integrating 253 datasets from 225 studies, and comprising over 18 million cells from 3,198 samples and 2,460 individuals across 34 anatomical locations in both health and disease. The HDLCA defines 142 consensus lung cell types, including 13 rare and 8 previously undescribed ones. Notably, we identify a rare intermediate alveolar epithelial progenitor (Int AP) population in normal lungs, with transcriptional signatures shared by alveolar type 2 (AT2) and alveolar type 1 (AT1) cells. Trajectory inference indicates that Int AP cells arise from both AT2 and SCGB1A1+ secretory cells and give rise to AT1 cells, with Hippo-YAP/TAZ signalling governing lineage specification. Int AP cells are associated with genetic susceptibility to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease, and their dysfunction may contribute to abnormal alveolar repair and regenerative failure in both diseases. The HDLCA provides a unified atlas-level reference for mapping respiratory cellular diversity, uncovering reparative cell dynamics and informing regenerative and cell-based therapeutic strategies.

bioinformatics↗

bioRxiv2026

A CSF Proteomic Clock Reveals Opposing Brain-Aging Programs and Predicts Neurological Disease Progression

Aging is the risk factor for neurological disease, yet the molecular architecture of human brain aging remains poorly defined. Here, we analyzed more than 10,000 cerebrospinal fluid (CSF) proteomes across multiple cohorts and proteomic platforms to developed a 249-protein CSF aging clock that accurately predicts age and generalizes across independent datasets. CSF brain-age acceleration was increased across diverse neurological diseases, associated with blood-brain barrier (BBB) dysfunction, and predictive of longitudinal cognitive decline, neuroimaging progression, and dementia conversion. A simplified 30-protein panel retained similar prognostic performance. Biologically, clock proteins resolved two opposing programs: pro-aging activation of immune, vascular/BBB, ECM and coagulation pathways marked by CHI3L1, CD14, VWF, LRG1 and LTBP2, and anti-aging collapse of neuronal, synaptic, axonal and structural-maintenance programs marked by NPTX2, COL1A2, NID1, CDH8 and PENK. Brain-wide single-cell and regional mapping linked these programs to disease-vulnerable compartments. These findings establish a molecular framework for biological brain aging and neurological disease vulnerability.

neuroscience↗

bioRxiv2026

Structural assemblies for an RNA world

Protein homo-oligomerization widely generates symmetric assemblies for encapsulation and scaffolding. Conversely, natural RNA quaternary structures appear limited, with known RNA-only multimers predominantly forming dimers or dihedral assemblies from long transcripts. Whether short RNAs can access broader higher-order assembly space has remained unclear. Here, using cryo-EM, we show that RNAs under 200 nucleotides form three distinct structural classes: a 60-subunit viral-capsid-like icosahedron, non-caged oligomers including a trimer and a strand-exchanged dimer, and a continuous filament assembled from a 57-nucleotide RNA. These structures demonstrate that assembly complexity does not simply scale with RNA length; compact RNAs can specify architectures traditionally associated with proteins. Our findings broaden the known RNA quaternary repertoire, strengthen the structural plausibility of higher-order organization in an RNA world, and establish foundational reference architectures to guide future RNA-based self-assembling nanoparticle design. HighlightsO_LIShort RNAs reveal protein-like assembly principles for an RNA world C_LIO_LIThe first natural RNA-only T=1 icosahedral cage is a viral-capsid-like 60-mer C_LIO_LICompact RNA-RNA interfaces encode global curvature, valency and propagation C_LIO_LIRNA motifs assemble into closed cages, finite oligomers and extended filaments C_LI

evolutionary biology↗

bioRxiv2026

Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro

Spatial transcriptomics enables the study of how cells coordinate their molecular states within tissue, providing insight into both normal function and disease processes. A key challenge is to identify gene expression programs that vary continuously across space and are coordinated between cell types. We present CoPro, a computational framework for detecting the spatially coordinated progression of cellular states. CoPro can operate in both supervised and unsupervised modes to identify gene programs that co-vary within or between cell types, and to disentangle multiple overlapping spatial patterns. CoPro can be applied to single-cell-level spatial transcriptomics datasets, including MERFISH, SeqFISH+, Xenium, and histology-imputed transcriptomic data. We demonstrate the utility of CoPro with data collected from colon, brain, liver, and kidney tissues. In the colon, CoPro separates epithelial differentiation along the crypt axis from spatially localized inflammatory signals. In the aging liver, it identifies multiple aging-associated cellular programs superimposed on anatomical zonation. In the brain, the flexible kernel design enables the decoupling of the gene expression gradient along the dorsal-ventral and medial-lateral axes. In the kidney, CoPro identifies tubule-vasculature coordination that is essential in nephron function. These results demonstrate CoPros utility for analyzing spatial coordination of gene expression in complex tissues and disentangling overlapping biological processes, such as anatomical organization and disease-associated variation.

bioinformatics↗

bioRxiv2026

The structure and catalytic mechanism of new cellular and viral HDV ribozymes

We have determined the molecular structure and investigated the catalytic mechanism of two new ribozymes of the Hepatitis delta virus family, found in the nematode Caenorhabditis briggsae and virus Ackermannviridae. Crystal structures of both conform to the double-pseudoknot architecture adopted by the viral HDV ribozyme. The C. briggsae ribozyme has been determined both pre- and post-cleavage. In the former both nucleotides flanking the scissile phosphate are observed, along with a metal ion, and cytosine 75 N3 bound to the O5 leaving group. The pH dependence of cleavage rate reveals a pKa of 6.6 and together with the inactivity of a C75U mutant provides evidence for its role as general acid. In contrast to other nucleolytic ribozymes that use catalytic metal ions, reaction rate does not depend on the pKa of the divalent metal ion. Limited adjustment of structure of the active center is consistent with direct bonding of the metal ion to the O2 and non-bridging O, suggesting that the ion acts as a Lewis acid to activate nucleophilic attack. This mechanism appears to be general for the HDV ribozyme class, and distinguishes it from the majority of nucleolytic ribozymes that use general base catalysis.

biochemistry↗

bioRxiv2026

GRAVITY: Dynamic gene regulatory network-enhanced RNA velocity modeling for trajectory inference and biological discovery

RNA velocity techniques have emerged as efficient tools for unraveling the complex trajectories of cell development and differentiation. However, most of existing RNA velocity approaches are constrained by estimating transcriptional parameters for each gene in isolation and neglects the regulatory relationships among genes, which limits the ability to jointly resolve the dynamic rewiring of gene regulation and the underlying gene transcriptional kinetics across cell state transitions. To address these limitations, we present GRAVITY, a novel deep learning framework that explicitly integrates regulatory dynamics into transcriptional kinetics inference and utilizes a refined two-stage optimization strategy. Benchmarking across various simulated and real single-cell RNA sequencing datasets demonstrates that GRAVITY accurately infers both cellular and gene trajectories, along with their associated kinetic parameters. Most importantly, GRAVITY uncovers terminal cell states L5/L6 in embryonic brain development dataset. Furthermore, GRAVITY not only provides mechanistic insights by identifying the driver regulatory factors and modules governing cell fate, but also enables the systematic in silico simulation of cellular velocity changes induced by targeted regulatory perturbations.

bioinformatics↗

bioRxiv2025

Flu Virus Infection in Juvenile Mice Leads to Lifelong Multi-Organ Damage and Parkinsonian Pathological Changes in Aging

The widespread prevalence of Long COVID underscores the potential for viral infections to exert prolonged effects on hosts long after initial recovery. Despite this recognition, the long-term and even lifelong consequences of viral infections remain poorly understood. In this study, we infected juvenile mice with influenza viruses and systematically examined the lifelong effects (from 4 to 450 days post-infection) of these infections. Pathological analysis revealed persistent lifelong damage in multiple organs, including chronic lung inflammation and fibrosis, along with significant cardiac and renal pathology. Viral infection led to substantial neuronal loss in key brain regions of mice (hippocampal CA1, CA3, and striatum) during middle and old age. Strikingly, we identified two hallmark pathological features of Parkinsons disease - dopaminergic neuron degeneration and Lewy body-like -synuclein inclusions - in middle and aged infected mice. Transcriptomic profiling demonstrated sustained upregulation of inflammation-related genes in both lung and blood tissues, correlating with observed pathological changes. Lung-derived secreted proteins, encoded by differentially expressed genes, may mediate cross-organ communication affecting cardiac, renal, and neurological function. Brain transcriptome analysis at three months post-infection revealed downregulation of neurodevelopmental genes, potentially contributing to subsequent neuronal loss and Parkinsonian pathology. These findings collectively suggest that pulmonary influenza infection can induce systemic multi-organ effects through both secretory pathways and chronic inflammatory responses. The study offers crucial insights for developing more effective strategies to prevent and manage infectious diseases and their long-term sequelae.

microbiology↗

bioRxiv2025

Bispecific T cell engagers control solid tumors through clonal replacement and IL2-driven effector differentiation of CD8 T-cells

Bispecific T cell engagers (TCEs) often exhibit limited efficacy in solid tumors, in part due to immunosuppressive cues in the tumor microenvironment and low expression of targetable tumor antigens. Therapeutic strategies to improve TCE target sensitivity and enhance T cell effector functions therefore have significant translational potential. Here, we engineered TCEs that induce T cell activation in vitro against the low-abundance target antigens, TRP2/Kb and DLL3. Despite in vitro activity in these models, TCE monotherapy showed limited control of tumor growth in immunocompetent mice. Leveraging this in vivo model of TCE treatment failure, we discovered that co-treatment with TCE and a CD25-biased Interleukin-2 (IL2) rescues anti-tumor activity. Further, multimodal single-cell transcriptomic and immune repertoire analyses revealed that TCE-IL2 combination therapy controlled tumors by recruiting and activating new CD8+ T cells into the tumor microenvironment. These findings demonstrate that TCE-mediated anti-tumor responses function through a CD8+ T cell clonal replacement mechanism that can be augmented by cytokine therapy. One Sentence SummaryCombining TCE therapy with IL-2 enhances TCE efficacy in aggressive small-cell lung cancer and melanoma models with low target antigen density through T cell clonal replacement and CD8+ effector T cell differentiation.

cancer biology↗

bioRxiv2025

scLinguist: A pre-trained hyena-based foundation model for cross-modality translation in single-cell multi-omics

Single-cell multi-omics provides complementary insights into cellular states, heterogeneity, and regulatory programmes. However, paired assays remain costly, low-throughput, and technically challenging, whereas large-scale single-modality data such as scRNA-seq are abundant but do not capture protein-level biology. Here we present scLinguist, a foundation model for cross-modality translation introduces a three-stage framework: self-supervised pretraining on large-scale unimodal datasets to learn modality-specific representations, post-pretraining on limited paired data to capture cross-modality relationships, and inference to predict missing modalities (e.g., protein from RNA) in fine-tuning or zero-shot settings. Systematic benchmarking shows that scLinguist consistently outperforms state-of-the-art methods in predicting protein abundance from RNA across diverse biological contexts. It achieves high predictive performance while preserving cellular heterogeneity and further enables mechanistic and generalizable inference under simulated gene perturbations. Furthermore, scLinguist exhibits strong transferability across health states and datasets. By leveraging abundant unimodal data and minimizing dependence on paired assays, scLinguist establishes a scalable and versatile framework for cross-modality translation in single-cell analysis.

cell biology↗

bioRxiv2025

RNAcentral in 2026: Genes and literature integration

RNAcentral was founded in 2014 to serve as a comprehensive database of non-coding RNA sequences. It began by providing a single unified interface to more specialised resources, and now contains 45 million sequences. It has grown beyond providing a single interface to many specialised resources and now provides several services and analyses. These include secondary structure prediction with R2DT, sequence search, and analysis with Rfam. Since its last publication in 2021, RNAcentral has developed two major features. First, literature integration with the development of LitScan and LitSumm. LitScan automatically identifies and links relevant publications to RNA entries, while LitSumm uses natural language processing to generate functional summaries from the literature. Together, these tools address the critical challenge of connecting sequence data with scattered functional knowledge across thousands of publications. Secondly, RNAcentral has created gene level entries. Gene level entries represent a large structural change to RNAcentral. While RNAcentral previously organized data exclusively at the sequence level, we now group related transcripts into gene-centric views. This allows researchers to explore all isoforms, splice variants, and related sequences for a gene in a unified interface, better reflecting biological organization and facilitating comparative analyses. RNAcentral is freely available at: https://rnacentral.org.

bioinformatics↗

bioRxiv2025

The genome of the early diverged amphioxus, Asymmetron lucayanum, illuminates the evolution of genome architecture and gene repertoires in cephalochordates

Cephalochordates (amphioxus or lancelet) are considered as living proxies for ancestral chordates due to their key phylogenetic position and slow evolutionary rate. The genomes of living amphioxus thus can help to reveal the genetic basis shaping the evolutionary transition from invertebrates to vertebrates. To gain a comprehensive understanding of the genome architecture in amphioxus, here we generate a chromosome-anchored genome assembly for Asymmetron lucaynum, representing the earliest diverging cephalochordate genus. Our results show that Asymmetron has an enlarged genome compared to those of the other four cephalochordate genomes decoded so far (all in the genus Branchiostoma), causing by pervasive expansions of inter-genic transposable elements (TEs). Nevertheless, both macrosynteny and microsynteny remain highly conserved between Asymmetron and Branchiostoma, enabling reconstruction of the ancestral genomic architecture of the Cephalochordate lineage for tracing genome evolutionary process during Deuterostome and Chordate diversification. By coupling developmental transcriptomic analyses, we further show that purifying selection and constraints on co-transcriptional regulation may have contributed to the maintenance of the conserved microsynteny blocks among cephalochordate species. We also examine the evolutionary history of Hox cluster in cephalochordates and vertebrates, and identify species-specific inversions and TE invasions at this important locus in both Asymmetron and Branchiostoma. Finally, we survey key gene families involved in both innate and adaptive immunity (e.g., TLR, NLR, MHC, and RAG) and uncover their plausible ancestry and evolutionary dynamics in chordates. Taken together, our findings illuminate the genome and gene evolution of cephalochordates and provide valuable resources for understanding the early evolution of chordates and the origin of vertebrates.

genomics↗

bioRxiv2025

RPcontact: Improved prediction of RNA-protein contacts using RNA and protein language models

Determining key contacts between RNA-protein interactions is essential for understanding the molecular mechanisms of numerous biological processes, including transcription, splicing, and translation. However, progress in this area has been impeded by the scarcity of RNA-protein complex structures in the Protein Data Bank (PDB) and the challenges posed by traditional structural determination techniques. Recent computational advancements, including deep learning methods like AlphaFold 3 and RoseTTAFoldNA, have improved contact prediction but are still limited by the availability of homologous sequences and templates. Here, we introduce RPcontact, a novel computational method designed to predict RNA-protein contacts using large language models tailored for RNA (ERNIE-RNA) and proteins (ESM-2). Despite being trained entirely on ribosomal RNA-protein (rRNA-protein) complexes, RPcontact demonstrates robust and generalized performance in predicting contacts for both dimeric and multimeric non-rRNA-protein complexes. The performance of RPcontact on contact predictions significantly improves over the binary contacts inferred from RNA-protein complex structures predicted by AlphaFold 3 and RoseTTAFoldNA, highlighting its potential in RNA-protein complex structure and function prediction.

bioinformatics↗

bioRxiv2025

scDenorm: a denormalisation tool for integrating single-cell transcriptomics data

Integrating single-cell omics data at an atlas scale enhances our understanding of cell types and disease mechanisms. However, the integration of data processed by different normalisation methods can lead to biases, such as unexpected batch effects and gene expression distortion, leading to misinterpretations in downstream analysis. To address these challenges, we present scDenorm, an algorithm that reverts normalised single-cell omics data to raw counts, preserving the integrity of the original measurements and ensuring consistent data processing during integration. We evaluated scDenorms performance on large-scale datasets and benchmarked its impact on data integration and downstream analysis across three datasets.

bioinformatics↗

bioRxiv2024

Common and distinct neural correlates of social interaction perception and theory of mind

Social interaction perception and theory of mind (ToM) frequently co-occur, but their commonalities and distinctions at behavioral and neural levels remain unclear. Participants (N = 231) provided moment-by-moment ratings of four text and four audio narratives on social interactions and ToM engagement, which were reliable (split-half r = .98 and .92, respectively) but only modestly correlated (r = .32). In a second sample (N = 90), we analyzed the co-variation between social interaction and ToM ratings and fMRI activity during text and audio narratives. Social interaction and ToM activity maps generalized across modalities (spatial r = .83 and .57, respectively), both with significant, overlapping clusters in canonical mentalizing regions (FDR q < .01). ToM uniquely engaged the lateral occipitotemporal cortex, left anterior intraparietal sulcus, and right premotor cortex. These results suggest that perceiving social interactions automatically involves mentalizing, and ToM additionally engages brain regions for action understanding and executive functions.

neuroscience↗

bioRxiv2024

Population-based metaproteomics reveals functional associations between gut microbiota and phenotypes

The association between gut microbiota and physiological disorders underscores the crucial need to understand its largely unexplored biological functions. In this study, we analyzed 86,876 microbial proteins and 1,277 human proteins from 1,967 Chinese stool samples. Our results indicate that metaproteomic data captures the microbial biodiversity of a population more effectively than metagenomic data, displaying greater variability in microbial functions and providing reliable assessments of microbiota stability. We identified crucial functions of core microbes and discovered 10,714 associations between metaproteomic taxon, microbial function, or human proteins with 39 phenotypes, including 1,381 features related to Type 2 Diabetes (T2D). Importantly, Megasphaera elsdeniis conversion of lactate to butyric acid appears to lower blood glucose levels, suggesting a protective mechanism against T2D. Our study underscores the power of population-based metaproteomics to offer unique functional insights into the gut microbiota. Highlights- The first large-scale population-based metaproteomics landscape. - Metaproteomics efficiently maps microbial functions, exposing individual functional variation. - 10,714 associations between metaproteomic taxa, microbial functions, or human proteins with 39 phenotypes. - Megasphaera elsdenii protects against elevated blood glucose levels in the host by degrading lactate to produce butyric acid.

microbiology↗

bioRxiv2024

Systematic identification and characterization of virus lncRNAs suggests extensive structural mimicry of host lncRNAs

Virus long non-coding RNAs (vlncRNAs) play crucial roles in viral infections, yet their identification and characterization remain limited. This study identified 5,053 novel vlncRNAs across 25 viral species using third-generation sequencing, with two from Influenza A virus and Vesicular stomatitis virus validated by RT-qPCR. Most vlncRNAs originated from dsDNA viruses. Only 1% having annotated RNA families, suggesting many novel RNA structures. Although only seven vlncRNAs shared sequence similarities with human lncRNAs (hlncRNAs), 772 vlncRNAs from 15 human viruses structurally mimicked hlncRNAs. These vlncRNA and hlncRNAs bound to similar miRNAs, potentially acting as miRNA sponges to promote essential life process. Splicing analysis showed vlncRNAs had a prevalence of alternative first exon. Finally, we developed vlncRNAbase (http://computationalbiology.cn/vlncRNAbase/#/) to store and organize the newly identified and known vlncRNAs. Overall, the study provides a valuable resource for further investigation into vlncRNAs and deepens our understanding of the diversity, structure and function of the molecule.

bioinformatics↗